System

Abstract

To provide a system.SOLUTION: A system comprises: means for collecting data on the correctness, answering time, and the process to the answer for problems answered by students; means for analyzing the collected data and calculating indicators including accuracy rate, answering speed, and problem-solving efficiency; means for visualizing the calculated indicators; and means for providing the visualized indicators to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the field of education, it is difficult to fairly and accurately evaluate students' learning processes and to objectively and comprehensively evaluate the results. In particular, the evaluation of elements that are difficult to quantify, such as thinking ability, judgment ability, and expressive ability, is heavily dependent on the teacher's experience and skills, making it even more challenging for novice teachers. [Means for solving the problem]

[0005] This invention provides a means to convert a student's learning process into fair and accurate indicators and objectively evaluate those indicators, students' self-assessments, and teacher evaluations. It also provides a means to accurately capture changes in thinking ability and attitudes across situations and circumstances over time. These functions can be trained by AI, making it possible for even novice teachers to easily and objectively create curriculum guidelines and report cards. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15]FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention has a means for converting a student's learning process into a fair and accurate indicator. Specifically, it collects data such as the correctness or incorrectness of the student's answers, the time it took to answer, and the process leading up to the answer, and then quantifies the student's learning process based on this data. This quantification can be expressed as an indicator such as the percentage of correct answers, the speed of answering, or the efficiency of problem-solving.

[0029] "Example 2"

[0030] Furthermore, the system of the present invention has a means for objectively and comprehensively evaluating the above indicators, students' self-evaluations, and teacher evaluations. Specifically, students themselves perform self-evaluations and input the results into the system. Teachers also evaluate students' learning progress and input the results into the system. The system combines these evaluation results with the above indicators to comprehensively evaluate students' learning progress.

[0031] "Example 3"

[0032] The system of the present invention also has a means for accurately capturing changes in thinking ability and attitude across a timeline of situations and situations. Specifically, the system records the student's answering process along a timeline and analyzes changes in the flow of thought and attitude during that process. This makes it possible to visually grasp changes in the student's thinking ability, judgment, and expressiveness along a timeline.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Collect data such as whether students answered the questions correctly, the time it took them to answer, and the process they used to arrive at the answer.

[0036] Step 2: Quantify the student's learning process based on the collected data. This quantification can be expressed as indicators such as the rate of correct answers, answering speed, and problem-solving efficiency.

[0037] "Example 2"

[0038] Step 1: Students self-assess and enter the results into the system.

[0039] Step 2: Teachers also evaluate students' learning progress and enter the results into the system.

[0040] Step 3: The system combines these assessment results with the above indicators to comprehensively evaluate the student's learning status.

[0041] "Example 3"

[0042] Step 1: Record the student's response process over time.

[0043] Step 2: Analyze the flow of thoughts and changes in attitude during the recorded process.

[0044] Step 3: Based on the analysis results, visually grasp the changes in students' thinking, judgment, and expression abilities over time.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, making it particularly difficult for novice teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means for objectively and comprehensively evaluating students' self-assessments and teacher assessments, making it difficult to accurately grasp changes in thinking ability and attitudes across time and situations.

[0048] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0049] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude across a timeline of situations and circumstances, means for collecting data on whether the student answered a question correctly, the time it took to answer, and the process leading up to the answer, means for analyzing the collected data and calculating indexes such as the rate of correct answers, answer speed, and problem-solving efficiency, and means for visualizing the calculated indexes so that users can view them. This allows for a fair and accurate evaluation of a student's learning process, making it possible for even novice teachers to easily and objectively prepare curriculum guidelines and report cards.

[0050] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[0051] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating students' learning outcomes.

[0052] "Self-assessment" refers to the act of students themselves evaluating their own learning outcomes and progress.

[0053] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning outcomes and progress.

[0054] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0055] "Transformation of thinking ability and attitude" refers to changes in students' ways of thinking and behavior.

[0056] "Correctness of answer" refers to information indicating whether the question answered by the student is correct or incorrect.

[0057] "Response time" refers to the time it takes a student to answer a question.

[0058] The "solution process" refers to the sequence of steps or thought processes a student goes through to solve a problem.

[0059] "Data collection" refers to the act of gathering the necessary information.

[0060] "Data analysis" refers to the act of analyzing collected data and extracting useful information.

[0061] "Correct answer rate" refers to the percentage of questions answered correctly by a student.

[0062] "Speed ​​of answering" refers to the speed at which a student answers a question.

[0063] "Problem-solving efficiency" refers to the effective methods and procedures students use to solve problems.

[0064] "Visualization" refers to the display of data or information in a visual format, such as a graph or chart.

[0065] "Users" refer to teachers and students who use this system.

[0066] This invention is a system for fairly and accurately evaluating a student's learning process. This system operates in cooperation with a server, a terminal, and a user.

[0067] First, when students use a learning application to answer questions, the device records in real time whether the answer is correct, the time it takes to answer, and the process leading up to the answer. Specifically, this data is collected through a dedicated application on the device (e.g., a learning management system app).

[0068] The device then transmits the collected data to a server. This transmission is carried out over the Internet, and the data is encrypted. For example, the device transmits the recorded answer data to the server using the HTTPS protocol.

[0069] The server stores the received data in a database (e.g., MySQL (registered trademark)). The stored data is analyzed using a Python data analysis library (e.g., Pandas, NumPy). The server reads the data, completes missing values, and normalizes the data.

[0070] Based on the analyzed data, the server calculates metrics such as the correct answer rate, answer speed, and problem-solving efficiency. These metrics are calculated using Python data analysis libraries. For example, the server calculates each student's correct answer rate, calculates the average answer time, and calculates a score to evaluate the efficiency of problem-solving.

[0071] The calculated metrics are visualized using data visualization tools (e.g., Matplotlib, Seaborn). The server displays the generated graphs and charts in a web interface for easy access by users. For example, a teacher can open a web browser and view a dashboard showing students' learning progress.

[0072] For example, when a student answers a math problem, the device records whether the answer is correct and measures the time it takes to answer. The device sends this data to a server, which stores it in a database. The server analyzes the stored data and calculates the accuracy rate and answer speed. The calculation results are visualized as graphs and charts, and users can view these results through a web interface.

[0073] Examples of prompts include:

[0074] "Please collect the correct answers, answer time, and process leading to the answer of the questions answered by the students, and use this information to generate a program that quantifies the students' learning process. Calculate and visualize the specific indicators such as the percentage of correct answers, answer speed, and problem-solving efficiency."

[0075] In this way, the system of the present invention fairly and accurately evaluates students' learning processes, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[0076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0077] Step 1: Data collection

[0078] When a student answers questions using a learning application, the device records in real time whether the answer is correct, the time it took to answer, and the process leading up to the answer. The input is the student's answer data (correct or incorrect, the time it took to answer, and the process), and the output is the collected data. Specifically, the device monitors the student's answers and records the data each time an answer is completed.

[0079] Step 2: Send data

[0080] The terminal sends the collected data to the server. The collected data is the input, and the data sent to the server is generated as the output. Specifically, the terminal encrypts the data using the HTTPS protocol and sends it to the server via the Internet.

[0081] Step 3: Save data

[0082] The server stores the received data in a database. The input is the data sent to the server, and the output is the data stored in the database. Specifically, the server uses a relational database such as MySQL to store each student's answer data in a table.

[0083] Step 4: Data analysis

[0084] The server analyzes the stored data. The input is the data stored in the database, and the output is the analyzed data. Specifically, the server reads the data using a Python data analysis library (e.g., Pandas, NumPy), completes missing values, and normalizes the data.

[0085] Step 5: Calculate the metrics

[0086] The server calculates indicators such as the correct answer rate, answer speed, and problem-solving efficiency based on the analyzed data. The analyzed data is input, and the calculated indicators are generated as output. Specifically, the server calculates each student's correct answer rate, calculates the average answer time, and calculates a score to evaluate the efficiency of problem-solving.

[0087] Step 6: Visualize the results

[0088] The server generates graphs and charts using data visualization tools (e.g., Matplotlib, Seaborn) to visualize the calculated metrics. The calculated metrics are the input, and the visualized graphs and charts are generated as the output. Specifically, the server displays each student's correct answer rate as a bar graph and their answer speed as a line graph.

[0089] Step 7: Provide feedback

[0090] Users (teachers and students) can view the visualized results through their devices. The input is the visualized graphs and charts, and the output is the feedback generated for the users to view. Specifically, the server displays the generated graphs and charts in a web interface for easy user access.

[0091] (Application example 1)

[0092] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Conventional learning process evaluation systems lack the means to fairly and accurately evaluate students' learning processes, and because they rely heavily on the teacher's subjectivity, objective evaluation is difficult. Furthermore, when evaluating the work efficiency of factory robots, there is a lack of means to quantify the accuracy of work, work time, and work process, making it difficult to accurately evaluate work efficiency. To solve these issues, a system is needed that can fairly and accurately evaluate students' learning processes and the work processes of factory robots.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0095] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude by linking situations and circumstances over time, means for converting a work process into a fair and accurate index, means for collecting the index, work accuracy, work time, and work process as data and quantifying work efficiency based on these, and means for calculating the work success rate, work speed, and work process efficiency. This makes it possible to fairly and accurately evaluate a student's learning process and the work process of a factory robot.

[0096] The "student learning process" is the process by which students acquire knowledge and skills through learning activities.

[0097] "Fair and accurate indicators" are standards that can be evaluated objectively and accurately, without relying on subjectivity.

[0098] "Children's self-assessment" refers to children themselves evaluating their own learning situation and achievements.

[0099] "Evaluation from teachers" refers to teachers' evaluation of students' learning status and achievements.

[0100] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0101] "Transformation in thinking ability and attitude" refers to the change in students' thinking ability and attitude through learning.

[0102] A "work process" refers to the series of steps or actions that a factory robot takes to complete a specific task.

[0103] "Work accuracy" refers to the precision and accuracy with which factory robots perform their work.

[0104] "Work time" refers to the time it takes a factory robot to complete a specific task.

[0105] "Work efficiency" refers to the efficiency with which factory robots perform their work.

[0106] "Task success rate" refers to the percentage of tasks performed by a factory robot that were successful.

[0107] "Work speed" refers to the speed at which factory robots perform work.

[0108] "Collecting as data" refers to recording specific information so that it can be later used for analysis and evaluation.

[0109] "Quantification" refers to expressing the subject of evaluation in numerical or concrete indicators.

[0110] "Calculating means" refers to a method or device that performs a numerical evaluation or analysis based on specific data.

[0111] The system for implementing this invention is composed of three main elements: a server, a terminal, and a user. The specific roles and processes of each element are explained below.

[0112] server

[0113] The server plays a central role in converting the learning process of students and the work process of factory robots into fair and accurate indicators. The server has the following functions:

[0114] Data collection: Collect student answer data and factory robot work data, including correct answers, answer times, task accuracy, and task time.

[0115] Data processing: Based on the collected data, indicators such as accuracy rate, answer speed, task success rate, and task speed are calculated.

[0116] Training the AI ​​model: The generative AI model is trained using the collected data, enabling more accurate evaluation.

[0117] The hardware and software used includes Python, time libraries, and random libraries.

[0118] Terminal

[0119] A terminal is a device through which a user interacts with the system. It can be a smartphone, smart glasses, a head-mounted display, or a robot. A terminal has the following capabilities:

[0120] Data entry: Entering work data for students and factory robots, including entering answers and recording work progress.

[0121] Data display: Displays the evaluation results and indicators sent from the server, allowing users to check their own learning status and work efficiency.

[0122] User

[0123] Users are people who use the system, such as students, teachers, factory operators, etc. Users have the following capabilities:

[0124] Data provision: Input learning and work data into the terminal.

[0125] Check the evaluation: Check the evaluation results sent from the server and use them for your next study or work.

[0126] Specific examples

[0127] For example, if a factory robot performs 10 tasks and calculates the success rate, total time, and number of successful tasks, the server would do the following:

[0128] 1. Every time the robot starts a task, it records the start and end times of the task.

[0129] 2. Use sensors and cameras to determine whether the task was successful.

[0130] 3. Calculate the work hours and add them to the total work hours.

[0131] 4. Count the number of successful tasks and calculate the success rate.

[0132] Prompt Sentence Examples

[0133] An example of a prompt for a generative AI model is:

[0134] Design a system to evaluate the work efficiency of factory robots. This system will collect data on the accuracy, work time, and work process of the robot's work, and quantify the robot's work efficiency based on this data. Using Python, create a program to calculate the work success rate, work speed, and work process efficiency.

[0135] The above is an embodiment of the present invention.

[0136] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0137] Step 1:

[0138] Data collection

[0139] The server collects work data from factory robots. Specifically, it records the start and end times of each task each time the robot starts work. The inputs are the start and end signals of the task, and the output is the start and end times of each task. This data is accurately recorded using a time sensor.

[0140] Step 2:

[0141] Determining the success of the task

[0142] The server determines whether the task was successful based on the collected task data. The input is sensor data showing the progress and results of the task, and the output is a judgment result of whether the task was successful or not. This judgment is made using sensors and cameras.

[0143] Step 3:

[0144] Calculating work hours

[0145] The server calculates the working time of each task based on the start and end times. The inputs are the start and end times, and the output is the working time of each task. This calculation is performed using the time library.

[0146] Step 4:

[0147] Total work time

[0148] The server aggregates the work time for each task and calculates the total work time. The input is the work time for each task, and the output is the total work time. This aggregation is performed based on the work time data stored in the database.

[0149] Step 5:

[0150] Calculating the success rate of work

[0151] The server counts the number of successful tasks and calculates the task success rate. The input is the success determination result for each task, and the output is the task success rate. This calculation is performed by dividing the number of successful tasks by the total number of tasks.

[0152] Step 6:

[0153] Calculating work speed

[0154] The server calculates the work speed based on the work time of each task. The input is the work time of each task, and the output is the work speed. This calculation is performed using an algorithm that calculates the speed based on the work time.

[0155] Step 7:

[0156] Displaying the evaluation results

[0157] The terminal displays the evaluation results and indicators sent from the server. The input is the evaluation data sent from the server, and the output is the evaluation results that the user can check. This display is performed using a terminal such as a smartphone or head-mounted display.

[0158] The above are the specific processing steps of this program.

[0159] Example 2

[0160] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, and lack a means for objectively and comprehensively evaluating self-assessments and teacher assessments. This makes it difficult to accurately grasp students' learning status, and novice teachers, in particular, face the challenge of creating curriculum guidelines and report cards, which are cumbersome.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting the student's learning process into a fair and accurate index, means for objectively comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for saving the evaluation data in a database, means for performing a comprehensive evaluation by combining the saved evaluation data and the learning index, means for visualizing the comprehensive evaluation result, and means for displaying the evaluation result on a terminal. This makes it possible to accurately and objectively evaluate the student's learning situation, and even a novice teacher can easily create curriculum guidelines and report cards.

[0163] "Means of converting students' learning processes into fair and accurate indicators" are means of providing standards and measures that can be used to objectively evaluate students' learning status.

[0164] "Student self-assessment" is the act of students themselves evaluating their own learning situation and level of understanding.

[0165] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning situation and level of understanding.

[0166] "Means of objective comprehensive evaluation" are methods that combine students' self-evaluations with teacher evaluations to provide a comprehensive evaluation without subjective bias.

[0167] The "means for storing evaluation data in a database" refers to a means for storing collected evaluation data in a database so that it can be accessed later.

[0168] "Means for conducting comprehensive evaluation by combining stored evaluation data and learning indicators" refers to a means for comprehensively evaluating a student's learning situation using evaluation data stored in a database and pre-set learning indicators.

[0169] The "means for visualizing the overall evaluation results" is a means for displaying the results of the overall evaluation in a visual format such as a graph or chart.

[0170] The "means for displaying the evaluation result on the terminal" refers to a means for displaying the generated evaluation result on the screen of the terminal used by the user.

[0171] This invention is a system for fairly and accurately evaluating a student's learning process. This system operates in cooperation with a server, a terminal, and a user.

[0172] The server was developed using Python and MySQL for database management. The server runs a program that comprehensively evaluates students' self-assessments, teacher assessments, and learning indicators. This program performs the following processes:

[0173] 1. Data Entry:

[0174] The user (student) evaluates themselves and inputs the results into the system via a terminal. For example, a student may evaluate their math comprehension as "80 points."

[0175] The user (teacher) evaluates the student's learning status and inputs the results into the system via a terminal. For example, a teacher may evaluate "Student A's level of understanding in mathematics is 75 points."

[0176] 2. Data Retention:

[0177] The server receives the rating data sent from the devices and stores it in a MySQL database by executing SQL queries to insert the rating data into the appropriate tables.

[0178] 3. Data Processing:

[0179] The server combines the saved assessment data with predefined learning indicators to comprehensively evaluate the student's learning status. For this process, it uses the Python Pandas library. For example, it creates a data frame and calculates the average value of each assessment.

[0180] 4. Generating evaluation results:

[0181] The server generates the overall evaluation results. Specifically, it calculates the overall evaluation score based on the evaluation data and learning indicators, and uses the Matplotlib library to visualize the results, for example, by generating bar graphs or pie charts.

[0182] 5. Displaying the results:

[0183] The server sends the generated evaluation results to the terminal. Users (students and teachers) can check the results on the terminal screen. For example, the evaluation results are displayed in dashboard format.

[0184] Examples:

[0185] The user (student) enters "My level of understanding of mathematics is 80 points" into the evaluation input form on the device and presses the send button.

[0186] The user (teacher) enters "Student A's level of understanding of mathematics is 75 points" into the evaluation input form on the device and presses the send button.

[0187] The server receives this rating data and stores it in a "ratings" table in a MySQL database.

[0188] The server creates a data frame using the Pandas library and calculates the average value of the evaluation data.

[0189] The server generates the overall evaluation results as a bar graph using the Matplotlib library.

[0190] The server sends the generated bar graph to the terminal, and the users (students and teachers) can view the graph on the terminal screen.

[0191] Example prompt for a generative AI model:

[0192] "Student A's self-assessment score is 80, and Teacher B's score is 75. Based on these assessments and learning indicators, please calculate Student A's overall score."

[0193] In this way, the system can comprehensively evaluate the student's learning status.

[0194] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0195] Step 1: Data entry

[0196] The user (student) evaluates themselves and inputs the results into the system via a terminal. For example, a student may evaluate their understanding of mathematics as "80 points."

[0197] The user (teacher) evaluates the student's learning status and inputs the results into the system via a terminal. For example, a teacher may evaluate "Student A's level of understanding in mathematics is 75 points."

[0198] Input: Student self-assessment data, teacher assessment data

[0199] Output: Rating data is sent from the device to the server.

[0200] Step 2: Save data

[0201] The server receives the rating data sent from the devices and stores it in a MySQL database by executing SQL queries to insert the rating data into the appropriate tables.

[0202] Input: Evaluation data sent from the device

[0203] Output: The evaluation data is saved in the database.

[0204] Step 3: Data processing

[0205] The server combines the saved assessment data with predefined learning indicators to comprehensively evaluate the student's learning status. For this process, it uses the Python Pandas library. For example, it creates a data frame and calculates the average value of each assessment.

[0206] Input: Evaluation data stored in a database, learning metrics

[0207] Output: Overall evaluation score

[0208] Step 4: Generate evaluation results

[0209] The server generates the overall evaluation results. Specifically, it calculates the overall evaluation score based on the evaluation data and learning indicators, and uses the Matplotlib library to visualize the results, for example, by generating bar graphs or pie charts.

[0210] Input: Overall rating score

[0211] Output: Visualized evaluation results (graphs and charts)

[0212] Step 5: View the results

[0213] The server sends the generated evaluation results to the terminal. Users (students and teachers) can check the results on the terminal screen. For example, the evaluation results are displayed in dashboard format.

[0214] Input: Visualized evaluation results

[0215] Output: Evaluation results displayed on the terminal

[0216] (Application example 2)

[0217] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0218] In conventional educational systems, it is difficult to fairly and accurately evaluate students' learning progress, making it difficult for inexperienced teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means to comprehensively assess students' self-assessments and teacher assessments, and appropriate learning content is often not recommended. This poses a challenge in providing optimal learning support to each student.

[0219] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting the student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately capturing changes in thinking ability and attitude by linking situations and circumstances over time, means for recommending appropriate learning content based on the evaluation results, means for performing a comprehensive evaluation using a generative AI model, and means for generating prompt sentences and inputting the evaluation results. This makes it possible to comprehensively evaluate the student's learning situation and recommend appropriate learning content.

[0220] "Means of replacing students' learning processes with fair and accurate indicators" are methods that provide standards and measures for objectively evaluating students' learning status.

[0221] "Means for objectively and comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" is a method for making a comprehensive judgment by combining the student's own assessment and the teacher's assessment.

[0222] "A means of accurately capturing changes in thinking ability and attitudes by linking scenes and situations over time" is a method for accurately grasping changes in students' thinking ability and attitudes over time.

[0223] "Means for recommending appropriate learning content based on evaluation results" refers to a method for suggesting optimal learning materials and teaching materials to students based on the results of a comprehensive evaluation.

[0224] "Means for comprehensive evaluation using a generative AI model" refers to a method for comprehensively evaluating students' learning status using an artificial intelligence model.

[0225] The "means for generating a prompt sentence and inputting the evaluation result" is a method for generating an instruction sentence for inputting the evaluation result.

[0226] The system for implementing this invention is configured as follows: First, the server has a means for converting a student's learning process into a fair and accurate index. This means provides standards and measures for objectively evaluating the student's learning situation. Next, the server has a means for objectively comprehensively evaluating the index, the student's self-evaluation, and the teacher's evaluation. This means makes it possible to make a comprehensive judgment by combining the student's own evaluation and the teacher's evaluation.

[0227] Furthermore, the server has a means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time. This allows for accurate understanding of changes in students' thinking ability and attitudes over time. The server also includes a means of recommending appropriate learning content based on the evaluation results, making it possible to propose optimal learning materials and teaching materials to students based on the results of the overall evaluation.

[0228] The server has a means for performing a comprehensive evaluation using a generative AI model. This means allows a comprehensive evaluation of a student's learning status using an artificial intelligence model. The server also includes a means for generating a prompt sentence and inputting the evaluation result. This means allows an instruction sentence for inputting the evaluation result to be generated.

[0229] This system is implemented using devices such as smartphones and tablets. The devices provide an interface for inputting students' self-assessments and teachers' assessments. The server processes and calculates the data using software such as Python, NumPy, and scikit-learn. Specifically, the server inputs students' self-assessment data and teachers' assessment data as NumPy arrays and sets indicators for comprehensive assessment. A generative AI model is used to perform a comprehensive assessment, and appropriate learning content is recommended based on the results.

[0230] As a specific example, consider the case where Student A's self-assessment scores are 80, 85, and 90, and the teacher's scores are 75, 80, and 85. The indices are set to 0.5, 0.3, and 0.2. An example of a prompt that performs an overall assessment based on this assessment data and recommends appropriate learning content is as follows:

[0231] "Student A's self-assessments are 80, 85, and 90. Teacher ratings are 75, 80, and 85. Please use these ratings to give an overall rating and recommend appropriate learning content. The index values ​​are 0.5, 0.3, and 0.2."

[0232] In this way, the present invention makes it possible to comprehensively evaluate a student's learning situation and recommend appropriate learning content.

[0233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0234] Step 1:

[0235] Users (students) use a device to input self-assessment data. Specifically, they input their self-assessment scores for each subject and skill through the interface of a smartphone or tablet. The input data is sent to the server. Examples of input data include 80 points for mathematics, 85 points for English, and 90 points for science.

[0236] Step 2:

[0237] The user (teacher) uses a terminal to input student evaluation data. The teacher observes the student's learning status and inputs evaluation scores for each subject and skill. The input data is sent to the server. Examples of input data include 75 points for mathematics, 80 points for English, and 85 points for science.

[0238] Step 3:

[0239] The server stores the received student self-assessment data and teacher assessment data as NumPy arrays. This makes it easy to process and calculate the data. Specifically, the data is stored as follows:

[0240] Student self-assessment data: [80, 85, 90]

[0241] Teacher evaluation data: [75, 80, 85]

[0242] Step 4:

[0243] The server sets an index for the overall evaluation. The index is used to weight each piece of evaluation data. Examples of indices are 0.5, 0.3, and 0.2. This determines how much each piece of evaluation data affects the overall evaluation.

[0244] Step 5:

[0245] The server performs an overall evaluation using the generative AI model. Specifically, it uses the set indicators to combine the student's self-evaluation data and the teacher's evaluation data to calculate the overall evaluation. The calculation result is output as an overall evaluation score. For example, the overall evaluation score is 82.5.

[0246] Step 6:

[0247] The server recommends appropriate learning content based on the overall evaluation score. Specifically, if the overall evaluation score is high, advanced learning materials are recommended, and if the overall evaluation score is low, basic learning materials are recommended. The recommended learning content is sent to the device. For example, if the overall evaluation score is 82.5, intermediate-level learning content is recommended.

[0248] Step 7:

[0249] The server generates prompts and provides a means for inputting the evaluation results. Specifically, based on the evaluation results, it generates prompts to be input into the generative AI model. The generated prompts are displayed on the terminal. An example of a prompt is, "Student A's self-evaluation scores are 80, 85, and 90. The teacher's ratings are 75, 80, and 85. Please conduct an overall evaluation based on these ratings and recommend appropriate learning content. The indices are 0.5, 0.3, and 0.2."

[0250] In this way, the system can comprehensively evaluate students' learning status and recommend appropriate learning content.

[0251] Example 3

[0252] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] Conventional learning assessment systems have difficulty accurately capturing changes in students' learning processes and thinking, making assessments subjective. Furthermore, it is difficult for novice educators to base their assessments on objective data when creating curriculum guidelines and assessment forms. Furthermore, there is a lack of a way to visually grasp changes in students' thinking, judgment, and expression abilities over time.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the educator's assessment, means for accurately capturing changes in thinking ability and attitude across situations and circumstances on a time axis, means for recording the student's answering process in real time and saving the answer input timing and change history, means for analyzing the saved data and extracting changes in thought flow and attitude, and means for visualizing the analysis results and displaying changes in thinking ability, judgment, and expression along a time axis. This allows for objective and accurate evaluation of a student's learning process, enabling educators to easily create curriculum guidelines and evaluation sheets.

[0255] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[0256] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating students' learning outcomes.

[0257] "Self-assessment" refers to students evaluating their own learning outcomes and progress.

[0258] "Evaluation by educators" refers to educators' evaluation of students' learning outcomes and attitudes.

[0259] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0260] "Transformation of thinking ability and attitude" refers to the change in students' thought process and learning attitude over time.

[0261] "Real-time recording" refers to instantly saving students' answers and actions as data.

[0262] "Timing of answer input" refers to the specific time when the student inputs the answer.

[0263] "Change history" refers to past records of when a student has revised their answers.

[0264] "Saved data" refers to records of students' answers and actions stored on the server.

[0265] "Analyzing data" refers to using collected data to extract specific patterns or trends.

[0266] "Changes in thought processes and attitudes" refers to showing how students' thought processes and learning attitudes have changed.

[0267] "Visualization" refers to the display of data in a visual format such as a graph or chart.

[0268] "Displaying along a time axis" refers to displaying data in order according to the progression of time.

[0269] "Thinking, judgment and expression skills" refers to students' ability to solve problems, make sound decisions and express their ideas.

[0270] This invention is a system that allows educators to fairly and accurately evaluate students' learning processes and easily create curriculum guidelines and evaluation sheets. A specific embodiment of this system is described below.

[0271] First, the device provides an interface for students to enter their answers. For example, they can use online forms such as Google® Forms or Microsoft® Forms. When students enter their answers, the data is sent to the server in real time. Specifically, the device displays a form like the one below:

[0272] Math problem: "Please solve x + 3 = 10."

[0273] Answer column: "x = [ ]"

[0274] When a student enters "x = 7" and presses the submit button, the data is sent to the server.

[0275] Next, the server saves the received answer data in a database. MySQL or PostgreSQL is used as the database. The server also saves the answer input timing and change history. For example, the server saves the answer "x = 7" at "2023-10-01 10:00:00".

[0276] The server then analyzes the saved data using Python's Pandas library. Specifically, it analyzes the change history of answers and the timing of input, and extracts changes in thought process and attitude. For example, the server sorts the data chronologically and analyzes what changes occurred and when.

[0277] Finally, the server displays the analysis results as graphs and charts using visualization libraries such as Matplotlib and Seaborn. This makes it possible to visually grasp changes in students' thinking, judgment, and expression skills over time. For example, the server can display the change history of answers as a graph, visualizing changes in thinking over time.

[0278] As a concrete example, consider the case of recording a student's process of solving a math problem. For example, if the student first enters "x = 5" and then later corrects it to "x = 7," the timing of these inputs and the history of changes are recorded and saved in a database. The data is then analyzed using Pandas and visualized as a graph using Matplotlib.

[0279] An example of a prompt sentence might be:

[0280] "Record the process by which students solve math problems and analyze changes in their thought process and attitude. Specifically, record the timing of inputting answers and the history of changes, extract the timing and type of changes in thinking, and visualize them as a graph."

[0281] In this way, the server can accurately grasp changes in the students' thinking ability, judgment ability, and expressive ability over time. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0282] Step 1: Data collection

[0283] The device provides an interface for students to input their answers, for example, using an online form. When students input their answers, the data is sent to the server in real time. The input includes the student's answer and the timing of input. The output is the answer data sent to the server.

[0284] Specifically, the terminal will display a form like this:

[0285] Math problem: "Please solve x + 3 = 10."

[0286] Answer column: "x = [ ]"

[0287] Step 2: Save data

[0288] The server stores the received answer data in a database. MySQL or PostgreSQL is used as the database. The input includes the answer data sent from the device and the input timing. The output is the answer data stored in the database.

[0289] Specifically, the server executes the following SQL query:

[0290] Insert the answer data into the database.

[0291] Step 3: Data analysis

[0292] The server analyzes the stored data using Python's Pandas library. The input includes answer data retrieved from the database and the timing of input. The output is an analysis showing changes in thought patterns and attitudes.

[0293] Specifically, the server performs the following steps:

[0294] Retrieve data from the database and convert it into a Pandas dataframe.

[0295] Analyze answer change history and input timing.

[0296] Step 4: Data visualization

[0297] The server displays the analysis results as graphs and charts using visualization libraries such as Matplotlib and Seaborn. The input includes the results of the data analysis. The output is the visualized graphs and charts.

[0298] Specifically, the server performs the following steps:

[0299] Create a graph based on the analysis results.

[0300] Generates HTML and image files to display the graph.

[0301] Step 5: Delivering results

[0302] The server sends the visualized results to the terminal for user viewing. The input includes the visualized graphs and charts. The output is displayed in a user-viewable format.

[0303] Specifically, the server performs the following steps:

[0304] Generate the visualized results as a web page.

[0305] Send a web page to the device.

[0306] In this way, the server can accurately capture changes in students' thinking, judgment, and expressive abilities over time.

[0307] (Application example 3)

[0308] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] In conventional education systems, it is difficult to fairly and accurately evaluate students' learning processes, especially to grasp changes in thinking ability and attitudes over time. Furthermore, there is a lack of support for novice teachers to easily and objectively create curriculum guidelines and report cards. Furthermore, there is no means to record students' problem-solving processes in real time, analyze the data, and provide specific feedback.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0311] In this invention, the server includes a means for converting a student's learning process into a fair and accurate indicator, a means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and the teacher's assessment, a means for accurately capturing changes in thinking ability and attitude by linking situations and circumstances over time, a means for recording the student's problem-solving process in real time, a means for analyzing the recorded data and visualizing changes in the student's thought process and attitude, and a means for providing specific feedback to the student based on the analysis results. This makes it possible to record and analyze the student's learning process in detail and visualize changes in thinking ability and attitude. Furthermore, the use of a generative AI model can improve the accuracy of the analysis results, and by implementing it as an application installed on mobile information devices such as smartphones and tablets, convenience can be improved.

[0312] The "learning process" is the series of steps by which students acquire knowledge and skills through learning activities.

[0313] "Fair and accurate indicators" are standards for objectively and accurately evaluating students' learning status.

[0314] "Self-assessment" is the act of students themselves evaluating their own learning situation and results.

[0315] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0316] "Thinking ability" is the ability to think logically to solve problems and make decisions.

[0317] "Attitudinal change" refers to the change in students' attitudes and behavior during the learning process.

[0318] "Real-time recording methods" are methods that record the process of students' learning activities immediately while they are in the process of doing so.

[0319] "Means for analyzing data" refers to methods for processing the recorded data and extracting meaningful information.

[0320] "Line of thought" is the process by which students think through a problem.

[0321] "Visualization means" refers to a method of converting abstract data or information into a form that is visually easy to understand.

[0322] "Means of providing feedback" refers to methods of communicating areas for improvement and evaluations to students based on the analysis results.

[0323] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate results.

[0324] A "mobile information terminal" is a portable information processing device such as a smartphone or tablet.

[0325] A system for implementing this invention is configured as follows: First, a server executes a program for converting a student's learning process into a fair and accurate indicator. This program is implemented as an application installed on a mobile information terminal such as a smartphone or tablet.

[0326] Hardware and software used

[0327] Hardware: Smartphones, tablets, cameras

[0328] Software: OpenCV (video recording and analysis), NumPy (data processing), generative AI model

[0329] Data processing and calculation

[0330] 1. Real-time recording: Using the device camera, we record the process of students solving problems in real time. We use OpenCV to capture and save videos and screen captures.

[0331] 2. Data analysis: The recorded video data is sent to a server and analyzed using a generative AI model. The analysis extracts and visualizes changes in students' thought processes and attitudes.

[0332] 3. Feedback generation: Based on the analysis results, the server generates specific feedback for the student, including points for improvement and evaluation, and sends this feedback to the device.

[0333] Specific examples

[0334] For example, when recording the process of solving a math problem, the device's camera can be used to capture images of the student's hands and screen. The recorded video is then sent to a server where a generative AI model analyzes it. The analysis results provide information such as how the student attempted to solve the problem and at what step they got stuck. Based on this, the server can provide the student with specific feedback, such as, "You made a calculation error in this part. Try this method next time."

[0335] Prompt Sentence Examples

[0336] "Record the process students go through when solving math problems and analyze their thought processes and changes in attitude during that process. Specifically, visualize the thinking behind each step of the solution and provide feedback."

[0337] In this way, this invention makes it possible to record and analyze students' learning processes in detail and visualize changes in their thinking ability and attitudes. Furthermore, by using a generative AI model, the accuracy of the analysis results can be improved, and by implementing it as an application installed on mobile information devices such as smartphones and tablets, convenience can be improved.

[0338] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0339] Step 1:

[0340] The device records the student's problem-solving process in real time. Specifically, it takes a video using the device's camera and saves the video using OpenCV. The input is the camera image, and the output is the recorded video file.

[0341] Step 2:

[0342] The device sends the recorded video file to the server. Specifically, the video data is uploaded to the server via the network. The input is the video file, and the output is the video data stored on the server.

[0343] Step 3:

[0344] The server analyzes the received video data. Specifically, it processes the video data using a generative AI model to extract changes in the student's thought process and attitude. The input is the video data, and the output is the analysis results.

[0345] Step 4:

[0346] The server generates feedback based on the analysis results. Specifically, it generates written feedback for students, including specific improvements and evaluations based on the analysis results. The input is the analysis results, and the output is a feedback message.

[0347] Step 5:

[0348] The server sends the generated feedback message to the terminal. Specifically, the server sends the feedback message to the terminal via a network. The input is the feedback message, and the output is the feedback displayed on the terminal.

[0349] Step 6:

[0350] The terminal displays the received feedback message to the user. Specifically, the feedback message is displayed on the application screen so that the student can check it. The input is the feedback message, and the output is the feedback displayed on the screen.

[0351] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0352] "Example 1"

[0353] One embodiment of the present invention is a learning assessment system incorporating an emotion engine. This system converts a student's learning process into fair and accurate indicators, and objectively evaluates these indicators, the student's self-assessment, and teacher evaluations. Furthermore, it accurately captures changes in thinking ability and attitudes by linking situations and circumstances over time. Furthermore, this system is capable of learning using AI, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[0354] "Example 2"

[0355] The emotion engine recognizes emotions from students' facial expressions, tone of voice, choice of words, etc. For example, it recognizes how a student feels about a problem from their facial expressions and tone of voice when solving a problem. This emotion engine captures the emotions a student feels when solving a problem as data and reflects this in the evaluation of the learning process.

[0356] "Example 3"

[0357] Furthermore, the emotion engine adjusts the evaluation of the learning process based on the emotions it recognizes. For example, if a student shows high concentration while solving a problem, it will adjust the evaluation of that student's learning process upward. Conversely, if a student shows anxiety or hesitation while solving a problem, it will adjust the evaluation of that student's learning process downward. In this way, the emotion engine objectively evaluates students' emotions and reflects them in the evaluation of their learning process, thereby achieving fairer and more accurate learning evaluation.

[0358] The processing flow of each embodiment will be described below.

[0359] "Example 1"

[0360] Step 1: The learning assessment system translates student learning processes into fair and accurate indicators.

[0361] Step 2: Objectively evaluate the indicators, students' self-assessments, and teacher assessments.

[0362] Step 3: Accurately grasp the changes in thinking ability and attitudes across situations and circumstances over time.

[0363] Step 4: AI can learn, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[0364] "Example 2"

[0365] Step 1: The emotion engine recognizes emotions from students' facial expressions, tone of voice, choice of words, etc.

[0366] Step 2: Recognize how the student feels about the problem from their facial expressions and tone of voice as they solve it.

[0367] Step 3: The emotion engine captures data about the emotions students feel when solving problems and reflects this in the evaluation of the learning process.

[0368] "Example 3"

[0369] Step 1: The emotion engine adjusts the evaluation of the learning process based on the recognized emotions.

[0370] Step 2: If the student demonstrates high levels of concentration while solving the problem, revise the student's learning process rating upward.

[0371] Step 3: If a student shows signs of anxiety or uncertainty while solving the problem, lower the student's assessment of their learning process.

[0372] Step 4: The emotion engine objectively evaluates students' emotions and reflects them in the evaluation of the learning process, thereby achieving fairer and more accurate learning assessment.

[0373] Example 1

[0374] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0375] Conventional learning assessment systems have made it difficult to fairly and accurately evaluate students' learning processes. Furthermore, they lacked a means for objectively and comprehensively evaluating self-assessments and teacher assessments, making it impossible to accurately grasp changes in thinking ability and attitudes across time. Furthermore, there was a lack of support for novice teachers to easily and objectively create curriculum guidelines and report cards.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0377] In this invention, the server includes: means for converting a student's learning process into a fair and accurate indicator; means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and teacher assessments; means for accurately capturing changes in thinking ability and attitude over time across situations and circumstances; means for collecting the correctness or incorrectness of a student's answers, the time it took to answer, and the process leading up to the answer; means for storing the collected data in a database; means for analyzing the stored data and calculating indicators such as accuracy rate, answer speed, and problem-solving efficiency; means for analyzing the student's self-assessment and teacher assessment using an emotion engine; means for integrating the data analysis results and the emotion assessment results to perform a comprehensive evaluation; means for training an AI model using the collected data; and means for generating curriculum guidelines and report cards based on the comprehensive evaluation results and the predictions of the AI ​​model. This enables fair and accurate evaluation of a student's learning process and objectively evaluating the student's self-assessment and teacher assessments. Furthermore, even novice teachers can easily and objectively create curriculum guidelines and report cards.

[0378] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[0379] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating the learning process.

[0380] "Self-assessment" refers to students' own evaluation of their learning situation and achievements.

[0381] "Teacher evaluation" refers to a teacher's evaluation of a student's learning situation and achievements.

[0382] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0383] "Transformation in thinking ability and attitude" refers to the deepening of students' thinking and changes in their learning attitudes.

[0384] "Response time" refers to the time it takes a student to answer a question.

[0385] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[0386] "Correct answer rate" refers to the percentage of questions answered correctly by a student.

[0387] "Speed ​​of answering" refers to the speed at which a student answers a question.

[0388] "Problem-solving efficiency" refers to the efficiency with which students solve problems.

[0389] An "emotion engine" refers to software or algorithms that analyze and evaluate emotions.

[0390] An "AI model" refers to a mathematical model that uses artificial intelligence to analyze data and make predictions and classifications.

[0391] A "curriculum guideline" refers to a document that defines the learning content and goals that an educational institution provides to students.

[0392] A "report card" is a document used to evaluate a student's learning achievements and attitude and notify parents and students.

[0393] The present invention provides a system for fairly and accurately evaluating a student's learning process and for comprehensively evaluating the student's learning process. Specific embodiments of this system will be described below.

[0394] 1. Program Generation

[0395] The system's program is generated using Python and includes modules for data collection, data storage, data analysis, sentiment evaluation, overall evaluation, AI model training, and report generation.

[0396] 2. Program processing explanation

[0397] The server collects the correct answers, the time it took to answer, and the process leading to the answer. This data is stored in a database (e.g., MySQL).

[0398] The server uses the collected data to quantify the student's learning process by processing the data using Python libraries (e.g., Pandas, NumPy) and calculating metrics such as accuracy rate, answer speed, and problem-solving efficiency.

[0399] The server uses an emotion engine (e.g., Microsoft Azure (registered trademark) emotion analysis API) to collect students' self-evaluations and evaluations from teachers, and then performs a comprehensive evaluation.

[0400] The server uses AI models (e.g., TENSORFLOW (registered trademark), Keras) to learn students' learning processes, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[0401] 3. Examples of concrete examples and prompts

[0402] As a concrete example, consider a student answering a math problem. The student takes 30 seconds to answer and gets it right. The server collects this data and stores it in a database. The server then analyzes this data and calculates the accuracy rate and answer speed. Furthermore, it uses an emotion engine to collect the student's self-evaluation (e.g., "I was in good form today") and the teacher's evaluation (e.g., "I answered quickly and accurately") to generate an overall evaluation.

[0403] Example prompt sentence:

[0404] Collect correct answers, answer times, and the steps taken to reach a solution, and use these to quantify the student's learning process. Additionally, use the emotion engine to collect student self-assessments and teacher evaluations to provide a comprehensive assessment.

[0405] In this way, the system of the present invention can convert the student's learning process into a fair and accurate indicator and perform comprehensive evaluation.

[0406] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0407] Step 1: Data collection

[0408] The device records in real time whether the student answers a question correctly, how long it took to answer, and the process leading up to the answer. For example, when a student answers a math question, the device measures the time it takes to answer and records whether it answers correctly or incorrectly. The input is the student's answering behavior, and the output is the recorded data. The device then sends the recorded data to a server.

[0409] Step 2: Save data

[0410] The server stores the received data in a database (e.g. MySQL). The input is the data sent from the terminal, and the output is the data stored in the database. The server checks the integrity of the data and whether there is any invalid data.

[0411] Step 3: Data analysis

[0412] The server uses Python libraries (e.g., Pandas, NumPy) to analyze the stored data. The input is the data stored in the database, and the output is the analysis results. The server calculates indicators such as the accuracy rate, solution speed, and problem-solving efficiency. For example, it calculates that the accuracy rate is 80% and the average solution time is 25 seconds.

[0413] Step 4: Emotional assessment

[0414] Users (students) input their self-evaluation after learning (e.g., "I felt good today"). Users (teachers) input their evaluation of the student's learning process (e.g., "Your answers were quick and accurate"). The inputs are student and teacher evaluations, and the output is the emotional evaluation results. The server analyzes these evaluations using an emotional engine (e.g., Microsoft Azure's Emotion Analysis API).

[0415] Step 5: Overall evaluation

[0416] The server integrates the data analysis results and the emotion evaluation results to perform an overall evaluation. The inputs are the data analysis results and the emotion evaluation results, and the output is the overall evaluation result. The server stores the overall evaluation result in a database.

[0417] Step 6: Training the AI ​​model

[0418] The server uses the collected data to train an AI model (e.g., TensorFlow, Keras). The input is the collected data, and the output is a trained AI model. For example, it learns a student's learning patterns based on past data.

[0419] Step 7: Generate reports

[0420] The server generates curriculum guidelines and report cards based on the overall assessment results and the prediction results of the AI ​​model. The input is the overall assessment results and the prediction results of the AI ​​model, and the output is the generated report. For example, a report may be created stating that "Student A has a high rate of correct answers to math problems and is also fast at answering them." The server sends the generated report to the terminal so that the user (teacher) can check it.

[0421] (Application example 1)

[0422] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0423] Conventional learning assessment and work evaluation systems have difficulty in fairly and accurately evaluating the processes of students and workers. Furthermore, because evaluations do not take into account changes in emotions and motivation, comprehensive performance evaluations are insufficient. Furthermore, there was a need for a system that allows novice teachers and administrators to easily conduct evaluations.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0425] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for accurately grasping changes in thinking ability and attitude linking scenes and situations over time, means for converting a worker's work process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the worker's self-assessment, and the manager's evaluation, and means for analyzing emotions during work and evaluating stress levels and motivation. This makes it possible to fairly and accurately evaluate the student's or worker's process and to comprehensively evaluate performance taking changes in emotions and motivation into account.

[0426] The "learning process" is the series of steps by which students acquire knowledge and skills through learning activities.

[0427] An "indicator" is a number or data that serves as a basis for evaluation or judgment.

[0428] "Self-evaluation" refers to an individual's evaluation of their own behavior and achievements.

[0429] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0430] "Thinking ability" is the intellectual ability to solve problems and make decisions.

[0431] "Attitudinal change" refers to changes in an individual's behavior and thinking over time.

[0432] A "work process" is a series of procedures or methods that a worker follows to carry out a task.

[0433] "Emotion analysis" is a technology that collects and analyzes an individual's emotional state as data.

[0434] "Stress level" is an index that indicates the degree of stress felt by an individual.

[0435] "Motivation" is the internal drive or motivation that drives an individual to take action.

[0436] A system for implementing the present invention has the following configuration.

[0437] System configuration

[0438] 1. Hardware:

[0439] Camera: Records the worker's work process and collects image data.

[0440] Biosensors: Monitor workers' heart rate and other biometric data.

[0441] Server: Collects, analyzes, and stores data.

[0442] 2. Software:

[0443] OpenCV: A library for image processing.

[0444] EmotionRecognizer: Software for analyzing emotions from workers' facial expressions.

[0445] scikit-learn: A machine learning library for performing data analysis.

[0446] Data collection and analysis

[0447] The server converts the worker's work process into a fair and accurate indicator based on the data collected from the camera and biosensor. Specifically, it processes the data in the following steps:

[0448] 1. Data Collection:

[0449] The camera records the worker's work and captures image data for each frame.

[0450] Biosensors collect workers' heart rates and other biometric data in real time.

[0451] 2. Data Analysis:

[0452] Using OpenCV, the accuracy and speed of work are analyzed from the collected image data.

[0453] Using EmotionRecognizer, the facial expressions of workers are analyzed from the collected image data to obtain emotional data.

[0454] Using scikit-learn, we assess workers' stress levels and motivation based on collected biometric and emotional data.

[0455] comprehensive evaluation

[0456] The server quantifies the worker's work process based on the analyzed data and objectively evaluates the indicators, the worker's self-evaluation, and the manager's evaluation, making it possible to comprehensively evaluate the worker's performance.

[0457] Specific examples

[0458] For example, consider a worker performing assembly work in a factory. The accuracy, speed, and procedures of the various tasks performed by this worker are recorded with a camera, and biosensors are used to monitor the worker's heart rate and facial expressions while they work. Based on the collected data, the worker's skills and emotions are quantified and comprehensively evaluated.

[0459] Prompt Sentence Examples

[0460] Develop a system to evaluate the skills and emotions of assembly workers in a factory. Record their work with a camera and monitor their heart rate with a biosensor. Based on the collected data, quantify and comprehensively evaluate the worker's skills (accuracy, speed, efficiency) and emotions (stress level, motivation).

[0461] In this way, the mode for carrying out the invention can be concretely shown.

[0462] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0463] Step 1:

[0464] The server uses a camera to record the worker's work process. The input is video data from the camera, and the output is image data for each frame. Specifically, the camera captures the worker's movements in real time and saves them frame by frame.

[0465] Step 2:

[0466] The server collects the worker's heart rate and other biometric data using a biosensor. The input is the biometric data from the biosensor, and the output is real-time heart rate data. Specifically, the biosensor monitors the worker's heart rate and transmits the data to the server.

[0467] Step 3:

[0468] The server analyzes the accuracy and speed of the work from the image data collected using OpenCV. The input is image data for each frame, and the output is analytical data on the accuracy and speed of the work. Specifically, OpenCV processes the image data and evaluates the work procedure and speed.

[0469] Step 4:

[0470] The server uses EmotionRecognizer to analyze the facial expressions of the workers from the collected image data and obtain emotional data. The input is image data for each frame, and the output is emotional data. Specifically, EmotionRecognizer analyzes the image data and identifies the emotions from the workers' facial expressions.

[0471] Step 5:

[0472] The server evaluates the worker's stress level and motivation based on biometric and emotional data collected using scikit-learn. The input is heart rate data and emotional data, and the output is evaluation data related to stress level and motivation. Specifically, scikit-learn analyzes the biometric and emotional data to quantify the worker's stress level and motivation.

[0473] Step 6:

[0474] The server quantifies the worker's work process based on the analyzed data and objectively evaluates the indicators, the worker's self-assessment, and the manager's assessment. The input is analytical data on the worker's accuracy and speed, emotional data, and assessment data on stress level and motivation, and the output is comprehensive assessment data. Specifically, the server integrates this data and comprehensively evaluates the worker's performance.

[0475] Example 2

[0476] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0477] Conventional learning assessment systems have the problem that student self-assessments and teacher assessments are subjective, making it difficult to achieve an objective overall assessment. Furthermore, because assessments do not take into account students' emotions, it is difficult to accurately grasp the true level of understanding and changes in attitudes during the learning process. Furthermore, there are issues with the difficulty for novice teachers to easily and objectively create curriculum guidelines and report cards.

[0478] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0479] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately capturing changes in thinking ability and attitude across a timeline of situations and circumstances, means for recognizing emotions by analyzing the student's facial expressions and tone of voice, means for reflecting the emotion data in the assessment of the learning process, and means for integrating the self-assessment data, teacher assessment data, and emotion data to generate an overall assessment. This makes it possible to objectively and comprehensively evaluate a student's learning situation, allowing even a novice teacher to easily create curriculum guidelines and report cards.

[0480] "Means of replacing students' learning processes with fair and accurate indicators" refers to means of providing standards and metrics for objectively evaluating students' learning status.

[0481] "Student self-assessment" refers to the act of students themselves evaluating their own learning situation and level of understanding and entering the results into the system.

[0482] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning status and entering the results into the system.

[0483] "Objective comprehensive evaluation means" refers to a means of integrating students' self-evaluations, teacher evaluations, and other data to make a comprehensive evaluation without relying on subjectivity.

[0484] "A means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time" is a means of tracking and evaluating changes in students' thinking ability and attitudes over time during the learning process.

[0485] The "means of recognizing emotions by analyzing a student's facial expression and tone of voice" refers to a means of capturing a student's facial expression and tone of voice using a camera or microphone, and analyzing them to recognize the student's emotions.

[0486] The "means for reflecting the emotion data in the evaluation of the learning process" refers to a means for incorporating the recognized emotion data as part of the learning evaluation and reflecting it in the overall evaluation.

[0487] The "means for generating a comprehensive evaluation by integrating self-evaluation data, teacher-evaluation data, and emotion data" is a means for integrating various evaluation data to generate a final comprehensive evaluation.

[0488] The present invention provides a system for fairly and accurately evaluating a student's learning process. The system is implemented using the following hardware and software.

[0489] Hardware and software used

[0490] Hardware: cameras, microphones, servers

[0491] Software: Emotion engine, database management system, rating algorithm

[0492] Program processing

[0493] 1. Self-evaluation input by the user (student)

[0494] The user self-evaluates their learning status. For example, the user may enter "My understanding of mathematics is 80%" in the evaluation form displayed on the device screen.

[0495] The terminal transmits the self-evaluation data input by the user to the server.

[0496] The server stores the received self-assessment data in a database.

[0497] 2. Teachers enter their assessments

[0498] Teachers evaluate students' learning status and input the results into the system via their terminals. For example, a teacher might evaluate "Student A's level of understanding in mathematics is 85%" and input the results into the terminal.

[0499] The terminal transmits the evaluation data entered by the teacher to the server.

[0500] The server stores the received teacher evaluation data in a database.

[0501] 3. Emotion Recognition by Emotion Engine

[0502] The server uses a camera and microphone to capture students' facial expressions and tone of voice in real time.

[0503] The server sends the captured data to the emotion engine.

[0504] The emotion engine analyzes the received data and recognizes the student's emotions. For example, if a student has a "confused" expression while solving a problem, the emotion engine will recognize that emotion as "confused."

[0505] The emotion engine transmits the recognized emotion data to the server.

[0506] The server stores the received emotion data in a database.

[0507] 4. Data integration and generation of overall assessment

[0508] The server obtains self-evaluation data, teacher-evaluation data, and emotion data from the database.

[0509] The server uses an evaluation algorithm to integrate these data and generate an overall evaluation. For example, if the self-evaluation is 80%, the teacher's evaluation is 85%, and the emotion data is "confused," the overall evaluation will be "The student has a high level of understanding, but is confused when solving problems."

[0510] The server stores the generated overall evaluation in a database and notifies the user or teacher as necessary.

[0511] Specific examples

[0512] When a user inputs their self-evaluation, the terminal displays the evaluation form and transmits the input contents to the server in real time.

[0513] When a teacher inputs an evaluation, the terminal displays an evaluation form for the teacher and sends the input to the server.

[0514] The server periodically sends data from the camera and microphone to the emotion engine to recognize emotions in real time.

[0515] The emotion engine uses facial expression analysis algorithms and voice analysis algorithms to recognize emotions and sends the results to the server.

[0516] The server uses a rating algorithm to combine the data and adjust the weighting of each piece of data when generating an overall rating.

[0517] Prompt Sentence Examples

[0518] "Students have entered their self-assessment. Now enter your teacher assessment."

[0519] "The emotion engine has analyzed your students' expressions. Please review the results."

[0520] "An overall assessment has been generated. View the results."

[0521] In this way, the system performs specific actions at each step to comprehensively evaluate the student's learning situation.

[0522] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0523] Step 1:

[0524] User self-assessment

[0525] Input: The user self-evaluates their learning status. For example, the user enters "My understanding of mathematics is 80%" in the evaluation form displayed on the device screen.

[0526] Specific operation: The device transmits the self-evaluation data entered by the user to the server in real time.

[0527] Output: The server stores the received self-assessment data in a database.

[0528] Step 2:

[0529] Teachers enter their assessments

[0530] Input: The teacher evaluates the student's learning status and inputs the results into the system via the terminal. For example, the teacher evaluates "Student A's level of understanding in mathematics is 85%" and inputs this into the terminal.

[0531] Specific operation: The device transmits the evaluation data entered by the teacher to the server in real time.

[0532] Output: The server stores the received teacher evaluation data in a database.

[0533] Step 3:

[0534] Emotion recognition by emotion engine

[0535] Input: The server uses a camera and microphone to capture students' facial expressions and tone of voice in real time.

[0536] Specific operation: The server sends the captured data to the emotion engine. The emotion engine analyzes the received data and recognizes the student's emotion. For example, if a student has a "confused" expression while solving a problem, the emotion engine recognizes that emotion as "confused."

[0537] Output: The emotion engine sends the recognized emotion data to the server, which stores the data in a database.

[0538] Step 4:

[0539] Integrating data and generating an overall assessment

[0540] Input: The server retrieves self-assessment data, teacher-assessment data, and emotion data from the database.

[0541] Specific operation: The server uses an evaluation algorithm to integrate these data and generate an overall evaluation. For example, if the self-evaluation is 80%, the teacher's evaluation is 85%, and the emotion data is "confused," the overall evaluation will be "The student has a good understanding, but is confused when solving problems."

[0542] Output: The server stores the generated overall evaluation in a database and notifies the user or teacher as needed.

[0543] In this way, the system performs specific actions at each step to comprehensively evaluate the student's learning situation.

[0544] (Application example 2)

[0545] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0546] Conventional learning assessment systems have had the problem of making it difficult to fairly and accurately evaluate students' learning progress. In particular, they were unable to grasp changes in students' emotions and attitudes in real time and reflect them in learning assessments. It was also difficult for novice teachers to easily and objectively create curriculum guidelines and report cards.

[0547] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0548] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for accurately capturing changes in thinking ability and attitude over time, means for analyzing the student's facial expressions and tone of voice in real time to recognize their emotions, and means for integrating the emotion recognition results, self-assessment results, and teacher evaluation results to calculate an overall evaluation score. This allows for a comprehensive evaluation of a student's learning situation, making it possible for even a novice teacher to easily and objectively create curriculum guidelines and report cards.

[0549] "Means of replacing students' learning processes with fair and accurate indicators" refers to methods that provide standards and metrics for objectively evaluating students' learning activities and achievements.

[0550] "Means for objectively comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" refers to a method of integrating the student's own assessment and the teacher's assessment to objectively perform a comprehensive assessment.

[0551] "A means of accurately capturing changes in thinking ability and attitudes by linking scenes and situations over time" is a method of accurately grasping changes in students' thinking ability and attitudes over time during the learning process.

[0552] "Method of analyzing students' facial expressions and tone of voice in real time to recognize their emotions" refers to a method of using a camera or microphone to analyze students' facial expressions and tone of voice in real time to recognize their emotions.

[0553] The "means for integrating emotion recognition results, self-assessment results, and teacher evaluation results to calculate a comprehensive evaluation score" is a method for integrating emotion recognition results, the student's own evaluation, and the teacher's evaluation to calculate a comprehensive evaluation score.

[0554] A system for implementing the present invention has the following configuration.

[0555] System Configuration

[0556] 1. Hardware:

[0557] Camera: Used to capture students' facial expressions in real time.

[0558] Microphone: Used to capture the student's tone of voice in real time.

[0559] Smartphone or head-mounted display (HMD): A device with a built-in camera and microphone that collects data in real time.

[0560] 2. Software:

[0561] Emotion recognition engine: This engine analyzes students' facial expressions and tone of voice to recognize their emotions. Specifically, it uses Microsoft Azure's Emotion API and Google Cloud's Speech-to-Text API.

[0562] Evaluation integration system: A system for integrating emotion recognition results, self-assessment results, and teacher evaluation results to calculate an overall evaluation score.

[0563] Data processing and calculation

[0564] 1. Emotion recognition:

[0565] The server sends the student's facial expression and voice data obtained from the camera and microphone to the emotion recognition engine.

[0566] The emotion recognition engine analyzes facial expression data to recognize emotions such as joy, sadness, and surprise, and also analyzes voice data to recognize emotions from the tone of voice.

[0567] 2. Evaluation Integration:

[0568] The server collects the students' self-assessment results and the teacher's assessment results.

[0569] The server integrates the emotion recognition results, self-assessment results, and teacher assessment results to calculate an overall assessment score, which allows for a comprehensive evaluation of the student's learning status.

[0570] Specific examples

[0571] For example, when a student is taking an online class, their facial expressions and tone of voice are captured in real time using their smartphone's camera and microphone. The server then sends this data to an emotion recognition engine to recognize their emotions. The recognized emotion data is then combined with self-assessment results and teacher evaluation results to calculate an overall assessment score. This score is then used to objectively evaluate the student's learning progress.

[0572] Prompt Sentence Examples

[0573] Below are some example prompts to input to a generative AI model:

[0574] I am developing a system to comprehensively evaluate students' learning progress. I would like to create an application that can analyze students' facial expressions and tone of voice in real time to recognize their emotions. How can I use the Azure Face API and Google Cloud Speech-to-Text API to perform emotion and voice recognition and calculate an overall evaluation score based on the results?

[0575] In this way, the present invention makes it possible to comprehensively evaluate the learning status of students, and even a novice teacher can easily and objectively prepare curriculum guidelines and report cards.

[0576] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0577] Step 1:

[0578] The device uses a camera and microphone to capture students' facial expression and voice data in real time. The input is the student's facial expression and voice, and the output is the captured image data and voice data.

[0579] Step 2:

[0580] The device transmits the captured facial expression data and voice data to the server. The input is the captured image data and voice data, and the output is the data transmitted to the server.

[0581] Step 3:

[0582] The server sends the received facial expression data to the emotion recognition engine. The input is the facial expression data, and the output is the data sent to the emotion recognition engine.

[0583] Step 4:

[0584] The emotion recognition engine analyzes facial expression data and recognizes emotions such as joy, sadness, surprise, etc. The input is facial expression data, and the output is recognized emotion data.

[0585] Step 5:

[0586] The server sends the received voice data to a voice recognition engine. The input is the voice data, and the output is the data sent to the voice recognition engine.

[0587] Step 6:

[0588] A speech recognition engine analyzes speech data and recognizes emotions from the tone of voice. The input is speech data, and the output is recognized emotional data.

[0589] Step 7:

[0590] The server integrates the emotion data received from the emotion recognition engine and the voice recognition engine. The input is emotion data from facial expressions and emotion data from tone of voice, and the output is the integrated emotion data.

[0591] Step 8:

[0592] The user (student) inputs their self-evaluation data into the terminal. The input is the student's self-evaluation data, and the output is the self-evaluation data entered into the terminal.

[0593] Step 9:

[0594] The user (teacher) inputs the student's evaluation into the terminal. The input is the teacher's evaluation data, and the output is the teacher's evaluation data input into the terminal.

[0595] Step 10:

[0596] The server integrates the integrated emotion data, self-assessment data, and teacher evaluation data to calculate a comprehensive evaluation score. The inputs are the integrated emotion data, self-assessment data, and teacher evaluation data, and the output is the comprehensive evaluation score.

[0597] Step 11:

[0598] The server sends the calculated overall evaluation score to the terminal and displays it to the user. The input is the overall evaluation score, and the output is the overall evaluation score displayed on the terminal.

[0599] Example 3

[0600] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0601] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, making it difficult for inexperienced teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means to accurately capture changes in students' thinking ability and attitudes over time, making it difficult to evaluate students' learning processes in detail. Furthermore, learning assessments do not take students' emotions into account, resulting in a lack of fairness and accuracy in learning assessments.

[0602] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0603] In this invention, the server includes: a means for converting a student's learning process into a fair and accurate indicator; a means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and the teacher's assessment; a means for accurately capturing changes in thinking ability and attitude across a timeline; a means for recording the student's answer process in real time; a means for analyzing the recorded data with an emotion recognition engine; a means for saving and visualizing the analysis results in a database; and a means for adjusting the assessment of the learning process based on the emotion data. This allows for a detailed and fair assessment of the student's learning process, making it easy for even novice teachers to create curriculum guidelines and report cards. Furthermore, because learning assessment takes students' emotions into account, more accurate learning assessment can be achieved.

[0604] The "student learning process" is the series of steps by which students acquire knowledge and skills through learning activities.

[0605] "Fair and accurate indicators" are standards for objectively and accurately evaluating students' learning outcomes.

[0606] "Self-assessment" is the act of students themselves evaluating their own learning outcomes and progress.

[0607] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning outcomes and attitude.

[0608] "Changes in thinking ability and attitude" refers to the way in which a student's train of thought and attitude change over time.

[0609] "Real-time recording means" refers to a method or device for instantly recording a student's answer process on the spot.

[0610] An "emotion recognition engine" is software or hardware that identifies emotions from students' facial expressions and tone of voice.

[0611] A "database" is a system for systematically storing analysis results and other data.

[0612] A "visualization tool" is a method or tool for displaying data in a visual format, such as a graph or chart.

[0613] The "means for adjusting the assessment of the learning process" is a method or system for correcting the learning assessment based on the emotional data of the students.

[0614] The present invention provides a system for fairly and accurately evaluating a student's learning process. A specific embodiment of this system will be described below.

[0615] 1. Generating the system program

[0616] The server generates a program for recording, analyzing, and evaluating the student's learning process. This program uses the following hardware and software:

[0617] Hardware: High-performance server (e.g. general-purpose server), camera, microphone

[0618] Software: Data analysis tools (e.g., Python, Pandas, Matplotlib), emotion recognition engines (e.g., general emotion recognition software)

[0619] 2. Explanation of program processing

[0620] The server records the student's answer process in real time as it is sent from the device. The device uses a camera and microphone to capture the student's facial expressions and voice, and sends this data to the server. The server inputs the received data into an emotion recognition engine and analyzes the emotional data. The analysis results are stored in a database, processed using Python's Pandas library, and visualized with Matplotlib. Finally, the server adjusts the evaluation of the learning process based on the emotional data.

[0621] 3. Specific Examples

[0622] A user uses the system to record Student A's process of solving a math problem. Student A's facial expressions and voice are captured using the device's camera and microphone and sent to the server. The server analyzes the data using an emotion recognition engine and stores the results in a database. The server then processes the data using Pandas and visualizes it with Matplotlib. Finally, the server evaluates Student A's learning process based on the emotion data and upwardly revises the evaluation if the student's concentration is high.

[0623] Prompt Sentence Examples

[0624] "Please record the process of Student A solving a math problem and analyze it using an emotion recognition engine. Based on the analysis results, please evaluate and visualize the learning process."

[0625] In this way, the server, terminals, and users cooperate to operate the system, and the learning process of the students can be accurately evaluated. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] Recording the student's answer process

[0628] Input: Questions answered by students, video and audio data from camera and microphone

[0629] Specific operation: The user answers questions using the device, which uses a camera and microphone to capture the student's facial expressions and voice in real time.

[0630] Output: Video and audio data is sent to the server.

[0631] Step 2:

[0632] Emotional Data Analysis

[0633] Input: Video and audio data sent to the server

[0634] Specific operation: The server inputs the received video and audio data into the emotion recognition engine, which identifies emotions from the student's facial expressions and tone of voice.

[0635] Output: Emotional data (e.g., concentration, joy, anxiety, uncertainty) is generated.

[0636] Step 3:

[0637] Data storage and visualization

[0638] Input: Parsed emotion data

[0639] How it works: The server stores the analyzed sentiment data in a database along a time axis, then processes the data using Python's Pandas library and visualizes it with Matplotlib.

[0640] Output: Visualized data (e.g., graphs and charts) are generated.

[0641] Step 4:

[0642] Assessment and adjustment of the learning process

[0643] Input: Visualized emotion data

[0644] How it works: The server evaluates the learning process based on the emotional data. If the user shows high concentration, the evaluation is revised upward, and if the user shows anxiety or hesitation, the evaluation is revised downward.

[0645] Output: A tailored learning assessment is generated.

[0646] In this way, the server, terminals, and users can work together to operate the system, enabling accurate evaluation of students' learning processes.

[0647] (Application example 3)

[0648] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0649] Conventional learning assessment systems and work evaluation systems have difficulty accurately grasping changes in the train of thought and attitude of learners and workers over time. They also cannot reflect changes in emotions in their evaluations, making it difficult to provide fair and accurate evaluations. Furthermore, there was a need for a system that allows even novice teachers and inexperienced work instructors to easily conduct evaluations.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0651] In this invention, the server includes means for converting a student's learning process into a fair and accurate indicator, means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude across a timeline, means for recording the worker's movements and facial expressions in real time and analyzing them with an emotion recognition engine, means for visualizing the worker's thought process and changes in attitude along a timeline based on the analysis results, means for evaluating the work process using a generative AI model, and means for evaluating the worker's emotions and adjusting the evaluation of the work process. This makes it possible to accurately grasp changes in the student's or worker's thought process and attitude along a timeline and perform fair and accurate evaluations.

[0652] "Means of converting students' learning processes into fair and accurate indicators" refers to methods of converting students' learning activities into criteria for objective evaluation.

[0653] "Means for objectively and comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" refers to a method for objectively evaluating the generated indicators by integrating the student's own assessment and the teacher's assessment.

[0654] "A means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time" is a method of accurately recording changes in thinking and attitudes in specific situations and circumstances over time.

[0655] "Means of recording the worker's actions and facial expressions in real time and analyzing them with an emotion recognition engine" refers to a method of instantly recording the worker's actions and facial expressions and analyzing them using emotion recognition technology.

[0656] "Means for visualizing the flow of thoughts and changes in attitude of workers over time based on the analysis results" refers to a method for visually displaying changes in thoughts and attitude of workers over time based on analyzed data.

[0657] "Means for evaluating work processes using generative AI models" refers to a method for evaluating the progress and efficiency of work using a generated artificial intelligence model.

[0658] The "means for assessing the worker's emotions and adjusting the evaluation of the work process" is a method for assessing the worker's emotional state and appropriately correcting the evaluation of the work based on the results.

[0659] A system for implementing this invention has the following configuration: First, the server provides means for converting a student's learning process into a fair and accurate indicator. This means converts the learning activity into a standard for objectively evaluating the learning activity. Next, it provides means for objectively comprehensively evaluating the indicator, the student's self-evaluation, and the teacher's evaluation. This means objectively evaluates the generated indicator by integrating the student's own evaluation and the teacher's evaluation.

[0660] Furthermore, the server provides a means to accurately capture changes in thinking ability and attitudes across situations and circumstances over time. This means accurately records changes in thinking and attitudes in specific situations and circumstances over time. It also provides a means to record the worker's actions and facial expressions in real time and analyze them using an emotion recognition engine. This means instantly records the worker's actions and facial expressions and analyzes them using emotion recognition technology.

[0661] The server also includes a means for visualizing the worker's thought process and changes in attitude over time based on the analysis results. This means visually displays the changes in the worker's thought process and attitude over time based on the analyzed data. Furthermore, a means for evaluating the work process using a generative AI model is also provided. This means evaluates the progress and efficiency of the work using the generated artificial intelligence model.

[0662] Finally, the server provides a means for evaluating the worker's emotions and adjusting the evaluation of the work process, which evaluates the worker's emotional state and appropriately modifies the evaluation of the work based on the results.

[0663] Hardware and software used

[0664] Hardware: Factory robots, cameras, sensors

[0665] Software: Image recognition software, emotion recognition engines, data analysis tools, generative AI models

[0666] Data processing and calculation

[0667] The server uses cameras and sensors installed on factory robots to record the movements and facial expressions of workers in real time. The collected data is analyzed using image recognition software, and changes in the worker's movements and facial expressions are recorded over time. Next, an emotion recognition engine evaluates the worker's emotions based on the data analyzed by the image recognition software. The emotion recognition engine identifies emotions such as concentration, anxiety, and hesitation from the worker's facial expressions and movements.

[0668] Based on the collected data and the results of emotion recognition, the data analysis tool visualizes the worker's thought process and changes in attitude over time. A generative AI model is then used to evaluate the work process. For example, if the worker shows high concentration, the evaluation of that work process is revised upward. Conversely, if the worker shows anxiety or hesitation, the evaluation is revised downward.

[0669] Examples and prompts

[0670] For example, consider a worker performing assembly work in a factory. The worker's movements and facial expressions are recorded using cameras and sensors and analyzed using an emotion recognition engine. Based on the analysis results, it is possible to visualize which processes the worker is concentrating on and which processes he or she is feeling confused or anxious about. This makes it possible to identify specific areas for improvement to increase the worker's efficiency.

[0671] Prompt Sentence Examples

[0672] "Record the movements and facial expressions of workers performing assembly work in a factory and analyze them using an emotion recognition engine. Based on the analysis results, visualize the worker's train of thought and changes in attitude over time and evaluate the work process. If the worker shows high concentration, revise the evaluation of that work process upward; conversely, if the worker shows anxiety or hesitation, revise it downward."

[0673] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0674] Step 1:

[0675] The server uses cameras and sensors installed on factory robots to record the movements and facial expressions of workers in real time.

[0676] Input: Real-time data of worker's movements and facial expressions

[0677] Output: Recorded movement and facial expression data

[0678] Specific actions: Cameras and sensors capture the worker's movements and facial expressions and send the data to a server.

[0679] Step 2:

[0680] The server analyzes the recorded data through image recognition software.

[0681] Input: Recorded movement and facial expression data

[0682] Output: Analyzed behavior and facial expression feature data

[0683] Specific actions: Image recognition software analyzes movement and facial expression data to extract features.

[0684] Step 3:

[0685] The server evaluates the worker's emotions using an emotion recognition engine based on the analyzed data.

[0686] Input: Analyzed behavior and facial expression feature data

[0687] Output: Emotion evaluation data of workers

[0688] Specific operation: The emotion recognition engine analyzes the feature data and identifies the worker's emotional state (concentration, anxiety, hesitation, etc.).

[0689] Step 4:

[0690] The server visualizes the worker's flow of thought and changes in attitude over time based on the emotional evaluation data.

[0691] Input: Emotional evaluation data of workers

[0692] Output: Visualization of thought patterns and attitude changes over time

[0693] What it does: The data analysis tool organizes the emotion assessment data over time and displays it visually.

[0694] Step 5:

[0695] The server uses a generative AI model to evaluate the work process.

[0696] Input: Visualization of thought patterns and attitude changes over time

[0697] Output: Evaluation data of work processes

[0698] How it works: A generative AI model analyzes the visualization data and evaluates the efficiency and quality of the work process.

[0699] Step 6:

[0700] The server evaluates the worker's emotions and adjusts the evaluation of the work process.

[0701] Input: Work process evaluation data, worker emotion evaluation data

[0702] Output: Evaluation data of the adjusted work process

[0703] Specific operation: The server adjusts the evaluation of the work process upward or downward based on the emotional evaluation data.

[0704] Step 7:

[0705] The server provides feedback of the final evaluation data to the worker or manager.

[0706] Input: Adjusted work process evaluation data

[0707] Output: Feedback data for workers and managers

[0708] Specific operation: The server compiles the evaluation data into a report format and provides it to workers and managers.

[0709] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0710] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0711] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0712] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0713] [Second embodiment]

[0714] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0715] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0716] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0717] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0718] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0719] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0720] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0721] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0722] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0723] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0724] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0725] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0726] "Example 1"

[0727] The system of the present invention has a means for converting a student's learning process into a fair and accurate indicator. Specifically, it collects data such as the correctness or incorrectness of the student's answers, the time it took to answer, and the process leading up to the answer, and then quantifies the student's learning process based on this data. This quantification can be expressed as an indicator such as the percentage of correct answers, the speed of answering, or the efficiency of problem-solving.

[0728] "Example 2"

[0729] Furthermore, the system of the present invention has a means for objectively and comprehensively evaluating the above indicators, students' self-evaluations, and teacher evaluations. Specifically, students themselves perform self-evaluations and input the results into the system. Teachers also evaluate students' learning progress and input the results into the system. The system combines these evaluation results with the above indicators to comprehensively evaluate students' learning progress.

[0730] "Example 3"

[0731] The system of the present invention also has a means for accurately capturing changes in thinking ability and attitude across a timeline of situations and situations. Specifically, the system records the student's answering process along a timeline and analyzes changes in the flow of thought and attitude during that process. This makes it possible to visually grasp changes in the student's thinking ability, judgment, and expressiveness along a timeline.

[0732] The processing flow of each embodiment will be described below.

[0733] "Example 1"

[0734] Step 1: Collect data such as whether students answered the questions correctly, the time it took them to answer, and the process they used to arrive at the answer.

[0735] Step 2: Quantify the student's learning process based on the collected data. This quantification can be expressed as indicators such as the rate of correct answers, answering speed, and problem-solving efficiency.

[0736] "Example 2"

[0737] Step 1: Students self-assess and enter the results into the system.

[0738] Step 2: Teachers also evaluate students' learning progress and enter the results into the system.

[0739] Step 3: The system combines these assessment results with the above indicators to comprehensively evaluate the student's learning status.

[0740] "Example 3"

[0741] Step 1: Record the student's response process over time.

[0742] Step 2: Analyze the flow of thoughts and changes in attitude during the recorded process.

[0743] Step 3: Based on the analysis results, visually grasp the changes in students' thinking, judgment, and expression abilities over time.

[0744] Example 1

[0745] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0746] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, making it particularly difficult for novice teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means for objectively and comprehensively evaluating students' self-assessments and teacher assessments, making it difficult to accurately grasp changes in thinking ability and attitudes across time and situations.

[0747] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0748] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude across a timeline of situations and circumstances, means for collecting data on whether the student answered a question correctly, the time it took to answer, and the process leading up to the answer, means for analyzing the collected data and calculating indexes such as the rate of correct answers, answer speed, and problem-solving efficiency, and means for visualizing the calculated indexes so that users can view them. This allows for a fair and accurate evaluation of a student's learning process, making it possible for even novice teachers to easily and objectively prepare curriculum guidelines and report cards.

[0749] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[0750] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating students' learning outcomes.

[0751] "Self-assessment" refers to the act of students themselves evaluating their own learning outcomes and progress.

[0752] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning outcomes and progress.

[0753] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0754] "Transformation of thinking ability and attitude" refers to changes in students' ways of thinking and behavior.

[0755] "Correctness of answer" refers to information indicating whether the question answered by the student is correct or incorrect.

[0756] "Response time" refers to the time it takes a student to answer a question.

[0757] The "solution process" refers to the sequence of steps or thought processes a student goes through to solve a problem.

[0758] "Data collection" refers to the act of gathering the necessary information.

[0759] "Data analysis" refers to the act of analyzing collected data and extracting useful information.

[0760] "Correct answer rate" refers to the percentage of questions answered correctly by a student.

[0761] "Speed ​​of answering" refers to the speed at which a student answers a question.

[0762] "Problem-solving efficiency" refers to the effective methods and procedures students use to solve problems.

[0763] "Visualization" refers to the display of data or information in a visual format, such as a graph or chart.

[0764] "Users" refer to teachers and students who use this system.

[0765] This invention is a system for fairly and accurately evaluating a student's learning process. This system operates in cooperation with a server, a terminal, and a user.

[0766] First, when students use a learning application to answer questions, the device records in real time whether the answer is correct, the time it takes to answer, and the process leading up to the answer. Specifically, this data is collected through a dedicated application on the device (e.g., a learning management system app).

[0767] The device then transmits the collected data to a server. This transmission is carried out over the Internet, and the data is encrypted. For example, the device transmits the recorded answer data to the server using the HTTPS protocol.

[0768] The server stores the received data in a database (e.g., MySQL). The stored data is analyzed using a Python data analysis library (e.g., Pandas, NumPy). The server reads the data, imputes missing values, and normalizes the data.

[0769] Based on the analyzed data, the server calculates metrics such as the correct answer rate, answer speed, and problem-solving efficiency. These metrics are calculated using Python data analysis libraries. For example, the server calculates each student's correct answer rate, calculates the average answer time, and calculates a score to evaluate the efficiency of problem-solving.

[0770] The calculated metrics are visualized using data visualization tools (e.g., Matplotlib, Seaborn). The server displays the generated graphs and charts in a web interface for easy access by users. For example, a teacher can open a web browser and view a dashboard showing students' learning progress.

[0771] For example, when a student answers a math problem, the device records whether the answer is correct and measures the time it takes to answer. The device sends this data to a server, which stores it in a database. The server analyzes the stored data and calculates the accuracy rate and answer speed. The calculation results are visualized as graphs and charts, and users can view these results through a web interface.

[0772] Examples of prompts include:

[0773] "Please collect the correct answers, answer time, and process leading to the answer of the questions answered by the students, and use this information to generate a program that quantifies the students' learning process. Calculate and visualize the specific indicators such as the percentage of correct answers, answer speed, and problem-solving efficiency."

[0774] In this way, the system of the present invention fairly and accurately evaluates students' learning processes, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[0775] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0776] Step 1: Data collection

[0777] When a student answers questions using a learning application, the device records in real time whether the answer is correct, the time it took to answer, and the process leading up to the answer. The input is the student's answer data (correct or incorrect, the time it took to answer, and the process), and the output is the collected data. Specifically, the device monitors the student's answers and records the data each time an answer is completed.

[0778] Step 2: Send data

[0779] The terminal sends the collected data to the server. The collected data is the input, and the data sent to the server is generated as the output. Specifically, the terminal encrypts the data using the HTTPS protocol and sends it to the server via the Internet.

[0780] Step 3: Save data

[0781] The server stores the received data in a database. The input is the data sent to the server, and the output is the data stored in the database. Specifically, the server uses a relational database such as MySQL to store each student's answer data in a table.

[0782] Step 4: Data analysis

[0783] The server analyzes the stored data. The input is the data stored in the database, and the output is the analyzed data. Specifically, the server reads the data using a Python data analysis library (e.g., Pandas, NumPy), completes missing values, and normalizes the data.

[0784] Step 5: Calculate the metrics

[0785] The server calculates indicators such as the correct answer rate, answer speed, and problem-solving efficiency based on the analyzed data. The analyzed data is input, and the calculated indicators are generated as output. Specifically, the server calculates each student's correct answer rate, calculates the average answer time, and calculates a score to evaluate the efficiency of problem-solving.

[0786] Step 6: Visualize the results

[0787] The server generates graphs and charts using data visualization tools (e.g., Matplotlib, Seaborn) to visualize the calculated metrics. The calculated metrics are the input, and the visualized graphs and charts are generated as the output. Specifically, the server displays each student's correct answer rate as a bar graph and their answer speed as a line graph.

[0788] Step 7: Provide feedback

[0789] Users (teachers and students) can view the visualized results through their devices. The input is the visualized graphs and charts, and the output is the feedback generated for the users to view. Specifically, the server displays the generated graphs and charts in a web interface for easy user access.

[0790] (Application example 1)

[0791] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0792] Conventional learning process evaluation systems lack the means to fairly and accurately evaluate students' learning processes, and because they rely heavily on the teacher's subjectivity, objective evaluation is difficult. Furthermore, when evaluating the work efficiency of factory robots, there is a lack of means to quantify the accuracy of work, work time, and work process, making it difficult to accurately evaluate work efficiency. To solve these issues, a system is needed that can fairly and accurately evaluate students' learning processes and the work processes of factory robots.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0794] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude by linking situations and circumstances over time, means for converting a work process into a fair and accurate index, means for collecting the index, work accuracy, work time, and work process as data and quantifying work efficiency based on these, and means for calculating the work success rate, work speed, and work process efficiency. This makes it possible to fairly and accurately evaluate a student's learning process and the work process of a factory robot.

[0795] The "student learning process" is the process by which students acquire knowledge and skills through learning activities.

[0796] "Fair and accurate indicators" are standards that can be evaluated objectively and accurately, without relying on subjectivity.

[0797] "Children's self-assessment" refers to children themselves evaluating their own learning situation and achievements.

[0798] "Evaluation from teachers" refers to teachers' evaluation of students' learning status and achievements.

[0799] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0800] "Transformation in thinking ability and attitude" refers to the change in students' thinking ability and attitude through learning.

[0801] A "work process" refers to the series of steps or actions that a factory robot takes to complete a specific task.

[0802] "Work accuracy" refers to the precision and accuracy with which factory robots perform their work.

[0803] "Work time" refers to the time it takes a factory robot to complete a specific task.

[0804] "Work efficiency" refers to the efficiency with which factory robots perform their work.

[0805] "Task success rate" refers to the percentage of tasks performed by a factory robot that were successful.

[0806] "Work speed" refers to the speed at which factory robots perform work.

[0807] "Collecting as data" refers to recording specific information so that it can be later used for analysis and evaluation.

[0808] "Quantification" refers to expressing the subject of evaluation in numerical or concrete indicators.

[0809] "Calculating means" refers to a method or device that performs a numerical evaluation or analysis based on specific data.

[0810] The system for implementing this invention is composed of three main elements: a server, a terminal, and a user. The specific roles and processes of each element are explained below.

[0811] server

[0812] The server plays a central role in converting the learning process of students and the work process of factory robots into fair and accurate indicators. The server has the following functions:

[0813] Data collection: Collect student answer data and factory robot work data, including correct answers, answer times, task accuracy, and task time.

[0814] Data processing: Based on the collected data, indicators such as accuracy rate, answer speed, task success rate, and task speed are calculated.

[0815] Training the AI ​​model: The generative AI model is trained using the collected data, enabling more accurate evaluation.

[0816] The hardware and software used includes Python, time libraries, and random libraries.

[0817] Terminal

[0818] A terminal is a device through which a user interacts with the system. It can be a smartphone, smart glasses, a head-mounted display, or a robot. A terminal has the following capabilities:

[0819] Data entry: Entering work data for students and factory robots, including entering answers and recording work progress.

[0820] Data display: Displays the evaluation results and indicators sent from the server, allowing users to check their own learning status and work efficiency.

[0821] User

[0822] Users are people who use the system, such as students, teachers, factory operators, etc. Users have the following capabilities:

[0823] Data provision: Input learning and work data into the terminal.

[0824] Check the evaluation: Check the evaluation results sent from the server and use them for your next study or work.

[0825] Specific examples

[0826] For example, if a factory robot performs 10 tasks and calculates the success rate, total time, and number of successful tasks, the server would do the following:

[0827] 1. Every time the robot starts a task, it records the start and end times of the task.

[0828] 2. Use sensors and cameras to determine whether the task was successful.

[0829] 3. Calculate the work hours and add them to the total work hours.

[0830] 4. Count the number of successful tasks and calculate the success rate.

[0831] Prompt Sentence Examples

[0832] An example of a prompt for a generative AI model is:

[0833] Design a system to evaluate the work efficiency of factory robots. This system will collect data on the accuracy, work time, and work process of the robot's work, and quantify the robot's work efficiency based on this data. Using Python, create a program to calculate the work success rate, work speed, and work process efficiency.

[0834] The above is an embodiment of the present invention.

[0835] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0836] Step 1:

[0837] Data collection

[0838] The server collects work data from factory robots. Specifically, it records the start and end times of each task each time the robot starts work. The inputs are the start and end signals of the task, and the output is the start and end times of each task. This data is accurately recorded using a time sensor.

[0839] Step 2:

[0840] Determining the success of the task

[0841] The server determines whether the task was successful based on the collected task data. The input is sensor data showing the progress and results of the task, and the output is a judgment result of whether the task was successful or not. This judgment is made using sensors and cameras.

[0842] Step 3:

[0843] Calculating work hours

[0844] The server calculates the working time of each task based on the start and end times. The inputs are the start and end times, and the output is the working time of each task. This calculation is performed using the time library.

[0845] Step 4:

[0846] Total work time

[0847] The server aggregates the work time for each task and calculates the total work time. The input is the work time for each task, and the output is the total work time. This aggregation is performed based on the work time data stored in the database.

[0848] Step 5:

[0849] Calculating the success rate of work

[0850] The server counts the number of successful tasks and calculates the task success rate. The input is the success determination result for each task, and the output is the task success rate. This calculation is performed by dividing the number of successful tasks by the total number of tasks.

[0851] Step 6:

[0852] Calculating work speed

[0853] The server calculates the work speed based on the work time of each task. The input is the work time of each task, and the output is the work speed. This calculation is performed using an algorithm that calculates the speed based on the work time.

[0854] Step 7:

[0855] Displaying the evaluation results

[0856] The terminal displays the evaluation results and indicators sent from the server. The input is the evaluation data sent from the server, and the output is the evaluation results that the user can check. This display is performed using a terminal such as a smartphone or head-mounted display.

[0857] The above are the specific processing steps of this program.

[0858] Example 2

[0859] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0860] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, and lack a means for objectively and comprehensively evaluating self-assessments and teacher assessments. This makes it difficult to accurately grasp students' learning status, and novice teachers, in particular, face the challenge of creating curriculum guidelines and report cards, which are cumbersome.

[0861] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting the student's learning process into a fair and accurate index, means for objectively comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for saving the evaluation data in a database, means for performing a comprehensive evaluation by combining the saved evaluation data and the learning index, means for visualizing the comprehensive evaluation result, and means for displaying the evaluation result on a terminal. This makes it possible to accurately and objectively evaluate the student's learning situation, and even a novice teacher can easily create curriculum guidelines and report cards.

[0862] "Means of converting students' learning processes into fair and accurate indicators" are means of providing standards and measures that can be used to objectively evaluate students' learning status.

[0863] "Student self-assessment" is the act of students themselves evaluating their own learning situation and level of understanding.

[0864] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning situation and level of understanding.

[0865] "Means of objective comprehensive evaluation" are methods that combine students' self-evaluations with teacher evaluations to provide a comprehensive evaluation without subjective bias.

[0866] The "means for storing evaluation data in a database" refers to a means for storing collected evaluation data in a database so that it can be accessed later.

[0867] "Means for conducting comprehensive evaluation by combining stored evaluation data and learning indicators" refers to a means for comprehensively evaluating a student's learning situation using evaluation data stored in a database and pre-set learning indicators.

[0868] The "means for visualizing the overall evaluation results" is a means for displaying the results of the overall evaluation in a visual format such as a graph or chart.

[0869] The "means for displaying the evaluation result on the terminal" refers to a means for displaying the generated evaluation result on the screen of the terminal used by the user.

[0870] This invention is a system for fairly and accurately evaluating a student's learning process. This system operates in cooperation with a server, a terminal, and a user.

[0871] The server was developed using Python and MySQL for database management. The server runs a program that comprehensively evaluates students' self-assessments, teacher assessments, and learning indicators. This program performs the following processes:

[0872] 1. Data Entry:

[0873] The user (student) evaluates themselves and inputs the results into the system via a terminal. For example, a student may evaluate their math comprehension as "80 points."

[0874] The user (teacher) evaluates the student's learning status and inputs the results into the system via a terminal. For example, a teacher may evaluate "Student A's level of understanding in mathematics is 75 points."

[0875] 2. Data Retention:

[0876] The server receives the rating data sent from the devices and stores it in a MySQL database by executing SQL queries to insert the rating data into the appropriate tables.

[0877] 3. Data Processing:

[0878] The server combines the saved assessment data with predefined learning indicators to comprehensively evaluate the student's learning status. For this process, it uses the Python Pandas library. For example, it creates a data frame and calculates the average value of each assessment.

[0879] 4. Generating evaluation results:

[0880] The server generates the overall evaluation results. Specifically, it calculates the overall evaluation score based on the evaluation data and learning indicators, and uses the Matplotlib library to visualize the results, for example, by generating bar graphs or pie charts.

[0881] 5. Displaying the results:

[0882] The server sends the generated evaluation results to the terminal. Users (students and teachers) can check the results on the terminal screen. For example, the evaluation results are displayed in dashboard format.

[0883] Examples:

[0884] The user (student) enters "My level of understanding of mathematics is 80 points" into the evaluation input form on the device and presses the send button.

[0885] The user (teacher) enters "Student A's level of understanding of mathematics is 75 points" into the evaluation input form on the device and presses the send button.

[0886] The server receives this rating data and stores it in a "ratings" table in a MySQL database.

[0887] The server creates a data frame using the Pandas library and calculates the average value of the evaluation data.

[0888] The server generates the overall evaluation results as a bar graph using the Matplotlib library.

[0889] The server sends the generated bar graph to the terminal, and the users (students and teachers) can view the graph on the terminal screen.

[0890] Example prompt for a generative AI model:

[0891] "Student A's self-assessment score is 80, and Teacher B's score is 75. Based on these assessments and learning indicators, please calculate Student A's overall score."

[0892] In this way, the system can comprehensively evaluate the student's learning status.

[0893] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0894] Step 1: Data entry

[0895] The user (student) evaluates themselves and inputs the results into the system via a terminal. For example, a student may evaluate their math comprehension as "80 points."

[0896] The user (teacher) evaluates the student's learning status and inputs the results into the system via a terminal. For example, a teacher may evaluate "Student A's level of understanding in mathematics is 75 points."

[0897] Input: Student self-assessment data, teacher assessment data

[0898] Output: Rating data is sent from the device to the server.

[0899] Step 2: Save data

[0900] The server receives the rating data sent from the devices and stores it in a MySQL database by executing SQL queries to insert the rating data into the appropriate tables.

[0901] Input: Evaluation data sent from the device

[0902] Output: The evaluation data is saved in the database.

[0903] Step 3: Data processing

[0904] The server combines the saved assessment data with predefined learning indicators to comprehensively evaluate the student's learning status. For this process, it uses the Python Pandas library. For example, it creates a data frame and calculates the average value of each assessment.

[0905] Input: Evaluation data stored in a database, learning metrics

[0906] Output: Overall evaluation score

[0907] Step 4: Generate evaluation results

[0908] The server generates the overall evaluation results. Specifically, it calculates the overall evaluation score based on the evaluation data and learning indicators, and uses the Matplotlib library to visualize the results, for example, by generating bar graphs or pie charts.

[0909] Input: Overall rating score

[0910] Output: Visualized evaluation results (graphs and charts)

[0911] Step 5: View the results

[0912] The server sends the generated evaluation results to the terminal. Users (students and teachers) can check the results on the terminal screen. For example, the evaluation results are displayed in dashboard format.

[0913] Input: Visualized evaluation results

[0914] Output: Evaluation results displayed on the terminal

[0915] (Application example 2)

[0916] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0917] In conventional educational systems, it is difficult to fairly and accurately evaluate students' learning progress, making it difficult for inexperienced teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means to comprehensively assess students' self-assessments and teacher assessments, and appropriate learning content is often not recommended. This poses a challenge in providing optimal learning support to each student.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting the student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately capturing changes in thinking ability and attitude by linking situations and circumstances over time, means for recommending appropriate learning content based on the evaluation results, means for performing a comprehensive evaluation using a generative AI model, and means for generating prompt sentences and inputting the evaluation results. This makes it possible to comprehensively evaluate the student's learning situation and recommend appropriate learning content.

[0919] "Means of replacing students' learning processes with fair and accurate indicators" are methods that provide standards and measures for objectively evaluating students' learning status.

[0920] "Means for objectively and comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" is a method for making a comprehensive judgment by combining the student's own assessment and the teacher's assessment.

[0921] "A means of accurately capturing changes in thinking ability and attitudes by linking scenes and situations over time" is a method for accurately grasping changes in students' thinking ability and attitudes over time.

[0922] "Means for recommending appropriate learning content based on evaluation results" refers to a method for suggesting optimal learning materials and teaching materials to students based on the results of a comprehensive evaluation.

[0923] "Means for comprehensive evaluation using a generative AI model" refers to a method for comprehensively evaluating students' learning status using an artificial intelligence model.

[0924] The "means for generating a prompt sentence and inputting the evaluation result" is a method for generating an instruction sentence for inputting the evaluation result.

[0925] The system for implementing this invention is configured as follows: First, the server has a means for converting a student's learning process into a fair and accurate index. This means provides standards and measures for objectively evaluating the student's learning situation. Next, the server has a means for objectively comprehensively evaluating the index, the student's self-evaluation, and the teacher's evaluation. This means makes it possible to make a comprehensive judgment by combining the student's own evaluation and the teacher's evaluation.

[0926] Furthermore, the server has a means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time. This allows for accurate understanding of changes in students' thinking ability and attitudes over time. The server also includes a means of recommending appropriate learning content based on the evaluation results, making it possible to propose optimal learning materials and teaching materials to students based on the results of the overall evaluation.

[0927] The server has a means for performing a comprehensive evaluation using a generative AI model. This means allows a comprehensive evaluation of a student's learning status using an artificial intelligence model. The server also includes a means for generating a prompt sentence and inputting the evaluation result. This means allows an instruction sentence for inputting the evaluation result to be generated.

[0928] This system is implemented using devices such as smartphones and tablets. The devices provide an interface for inputting students' self-assessments and teachers' assessments. The server processes and calculates the data using software such as Python, NumPy, and scikit-learn. Specifically, the server inputs students' self-assessment data and teachers' assessment data as NumPy arrays and sets indicators for comprehensive assessment. A generative AI model is used to perform a comprehensive assessment, and appropriate learning content is recommended based on the results.

[0929] As a specific example, consider the case where Student A's self-assessment scores are 80, 85, and 90, and the teacher's scores are 75, 80, and 85. The indices are set to 0.5, 0.3, and 0.2. An example of a prompt that performs an overall assessment based on this assessment data and recommends appropriate learning content is as follows:

[0930] "Student A's self-assessments are 80, 85, and 90. Teacher ratings are 75, 80, and 85. Please use these ratings to give an overall rating and recommend appropriate learning content. The index values ​​are 0.5, 0.3, and 0.2."

[0931] In this way, the present invention makes it possible to comprehensively evaluate a student's learning situation and recommend appropriate learning content.

[0932] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0933] Step 1:

[0934] Users (students) use a device to input self-assessment data. Specifically, they input their self-assessment scores for each subject and skill through the interface of a smartphone or tablet. The input data is sent to the server. Examples of input data include 80 points for mathematics, 85 points for English, and 90 points for science.

[0935] Step 2:

[0936] The user (teacher) uses a terminal to input student evaluation data. The teacher observes the student's learning status and inputs evaluation scores for each subject and skill. The input data is sent to the server. Examples of input data include 75 points for mathematics, 80 points for English, and 85 points for science.

[0937] Step 3:

[0938] The server stores the received student self-assessment data and teacher assessment data as NumPy arrays. This makes it easy to process and calculate the data. Specifically, the data is stored as follows:

[0939] Student self-assessment data: [80, 85, 90]

[0940] Teacher evaluation data: [75, 80, 85]

[0941] Step 4:

[0942] The server sets an index for the overall evaluation. The index is used to weight each piece of evaluation data. Examples of indices are 0.5, 0.3, and 0.2. This determines how much each piece of evaluation data affects the overall evaluation.

[0943] Step 5:

[0944] The server performs an overall evaluation using the generative AI model. Specifically, it uses the set indicators to combine the student's self-evaluation data and the teacher's evaluation data to calculate the overall evaluation. The calculation result is output as an overall evaluation score. For example, the overall evaluation score is 82.5.

[0945] Step 6:

[0946] The server recommends appropriate learning content based on the overall evaluation score. Specifically, if the overall evaluation score is high, advanced learning materials are recommended, and if the overall evaluation score is low, basic learning materials are recommended. The recommended learning content is sent to the device. For example, if the overall evaluation score is 82.5, intermediate-level learning content is recommended.

[0947] Step 7:

[0948] The server generates prompts and provides a means for inputting the evaluation results. Specifically, based on the evaluation results, it generates prompts to be input into the generative AI model. The generated prompts are displayed on the terminal. An example of a prompt is, "Student A's self-evaluation scores are 80, 85, and 90. The teacher's ratings are 75, 80, and 85. Please conduct an overall evaluation based on these ratings and recommend appropriate learning content. The indices are 0.5, 0.3, and 0.2."

[0949] In this way, the system can comprehensively evaluate students' learning status and recommend appropriate learning content.

[0950] Example 3

[0951] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0952] Conventional learning assessment systems have difficulty accurately capturing changes in students' learning processes and thinking, making assessments subjective. Furthermore, it is difficult for novice educators to base their assessments on objective data when creating curriculum guidelines and assessment forms. Furthermore, there is a lack of a way to visually grasp changes in students' thinking, judgment, and expression abilities over time.

[0953] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the educator's assessment, means for accurately capturing changes in thinking ability and attitude across situations and circumstances on a time axis, means for recording the student's answering process in real time and saving the answer input timing and change history, means for analyzing the saved data and extracting changes in thought flow and attitude, and means for visualizing the analysis results and displaying changes in thinking ability, judgment, and expression along a time axis. This allows for objective and accurate evaluation of a student's learning process, enabling educators to easily create curriculum guidelines and evaluation sheets.

[0954] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[0955] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating students' learning outcomes.

[0956] "Self-assessment" refers to students evaluating their own learning outcomes and progress.

[0957] "Evaluation by educators" refers to educators' evaluation of students' learning outcomes and attitudes.

[0958] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[0959] "Transformation of thinking ability and attitude" refers to the change in students' thought process and learning attitude over time.

[0960] "Real-time recording" refers to instantly saving students' answers and actions as data.

[0961] "Timing of answer input" refers to the specific time when the student inputs the answer.

[0962] "Change history" refers to past records of when a student has revised their answers.

[0963] "Saved data" refers to records of students' answers and actions stored on the server.

[0964] "Analyzing data" refers to using collected data to extract specific patterns or trends.

[0965] "Changes in thought processes and attitudes" refers to showing how students' thought processes and learning attitudes have changed.

[0966] "Visualization" refers to the display of data in a visual format such as a graph or chart.

[0967] "Displaying along a time axis" refers to displaying data in order according to the progression of time.

[0968] "Thinking, judgment and expression skills" refers to students' ability to solve problems, make sound decisions and express their ideas.

[0969] This invention is a system that allows educators to fairly and accurately evaluate students' learning processes and easily create curriculum guidelines and evaluation sheets. A specific embodiment of this system is described below.

[0970] First, the device provides an interface for students to enter their answers. For example, they can use online forms such as Google Forms or Microsoft Forms. When students enter their answers, the data is sent to the server in real time. Specifically, the device displays a form like this:

[0971] Math problem: "Please solve x + 3 = 10."

[0972] Answer column: "x = [ ]"

[0973] When a student enters "x = 7" and presses the submit button, the data is sent to the server.

[0974] Next, the server saves the received answer data in a database. MySQL or PostgreSQL is used as the database. The server also saves the answer input timing and change history. For example, the server saves the answer "x = 7" at "2023-10-01 10:00:00".

[0975] The server then analyzes the saved data using Python's Pandas library. Specifically, it analyzes the change history of answers and the timing of input, and extracts changes in thought process and attitude. For example, the server sorts the data chronologically and analyzes what changes occurred and when.

[0976] Finally, the server displays the analysis results as graphs and charts using visualization libraries such as Matplotlib and Seaborn. This makes it possible to visually grasp changes in students' thinking, judgment, and expression skills over time. For example, the server can display the change history of answers as a graph, visualizing changes in thinking over time.

[0977] As a concrete example, consider the case of recording a student's process of solving a math problem. For example, if the student first enters "x = 5" and then later corrects it to "x = 7," the timing of these inputs and the history of changes are recorded and saved in a database. The data is then analyzed using Pandas and visualized as a graph using Matplotlib.

[0978] An example of a prompt sentence might be:

[0979] "Record the process by which students solve math problems and analyze changes in their thought process and attitude. Specifically, record the timing of inputting answers and the history of changes, extract the timing and type of changes in thinking, and visualize them as a graph."

[0980] In this way, the server can accurately grasp changes in the students' thinking ability, judgment ability, and expressive ability over time. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0981] Step 1: Data collection

[0982] The device provides an interface for students to input their answers, for example, using an online form. When students input their answers, the data is sent to the server in real time. The input includes the student's answer and the timing of input. The output is the answer data sent to the server.

[0983] Specifically, the terminal will display a form like this:

[0984] Math problem: "Please solve x + 3 = 10."

[0985] Answer column: "x = [ ]"

[0986] Step 2: Save data

[0987] The server stores the received answer data in a database. MySQL or PostgreSQL is used as the database. The input includes the answer data sent from the device and the input timing. The output is the answer data stored in the database.

[0988] Specifically, the server executes the following SQL query:

[0989] Insert the answer data into the database.

[0990] Step 3: Data analysis

[0991] The server analyzes the stored data using Python's Pandas library. The input includes answer data retrieved from the database and the timing of input. The output is an analysis showing changes in thought patterns and attitudes.

[0992] Specifically, the server performs the following steps:

[0993] Retrieve data from the database and convert it into a Pandas dataframe.

[0994] Analyze answer change history and input timing.

[0995] Step 4: Data visualization

[0996] The server displays the analysis results as graphs and charts using visualization libraries such as Matplotlib and Seaborn. The input includes the results of the data analysis. The output is the visualized graphs and charts.

[0997] Specifically, the server performs the following steps:

[0998] Create a graph based on the analysis results.

[0999] Generates HTML and image files to display the graph.

[1000] Step 5: Delivering results

[1001] The server sends the visualized results to the terminal for user viewing. The input includes the visualized graphs and charts. The output is displayed in a user-viewable format.

[1002] Specifically, the server performs the following steps:

[1003] Generate the visualized results as a web page.

[1004] Send a web page to the device.

[1005] In this way, the server can accurately capture changes in students' thinking, judgment, and expressive abilities over time.

[1006] (Application example 3)

[1007] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1008] In conventional education systems, it is difficult to fairly and accurately evaluate students' learning processes, especially to grasp changes in thinking ability and attitudes over time. Furthermore, there is a lack of support for novice teachers to easily and objectively create curriculum guidelines and report cards. Furthermore, there is no means to record students' problem-solving processes in real time, analyze the data, and provide specific feedback.

[1009] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1010] In this invention, the server includes a means for converting a student's learning process into a fair and accurate indicator, a means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and the teacher's assessment, a means for accurately capturing changes in thinking ability and attitude by linking situations and circumstances over time, a means for recording the student's problem-solving process in real time, a means for analyzing the recorded data and visualizing changes in the student's thought process and attitude, and a means for providing specific feedback to the student based on the analysis results. This makes it possible to record and analyze the student's learning process in detail and visualize changes in thinking ability and attitude. Furthermore, the use of a generative AI model can improve the accuracy of the analysis results, and by implementing it as an application installed on mobile information devices such as smartphones and tablets, convenience can be improved.

[1011] The "learning process" is the series of steps by which students acquire knowledge and skills through learning activities.

[1012] "Fair and accurate indicators" are standards for objectively and accurately evaluating students' learning status.

[1013] "Self-assessment" is the act of students themselves evaluating their own learning situation and results.

[1014] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[1015] "Thinking ability" is the ability to think logically to solve problems and make decisions.

[1016] "Attitudinal change" refers to the change in students' attitudes and behavior during the learning process.

[1017] "Real-time recording methods" are methods that record the process of students' learning activities immediately while they are in the process of doing so.

[1018] "Means for analyzing data" refers to methods for processing the recorded data and extracting meaningful information.

[1019] "Line of thought" is the process by which students think through a problem.

[1020] "Visualization means" refers to a method of converting abstract data or information into a form that is visually easy to understand.

[1021] "Means of providing feedback" refers to methods of communicating areas for improvement and evaluations to students based on the analysis results.

[1022] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate results.

[1023] A "mobile information terminal" is a portable information processing device such as a smartphone or tablet.

[1024] A system for implementing this invention is configured as follows: First, a server executes a program for converting a student's learning process into a fair and accurate indicator. This program is implemented as an application installed on a mobile information terminal such as a smartphone or tablet.

[1025] Hardware and software used

[1026] Hardware: Smartphones, tablets, cameras

[1027] Software: OpenCV (video recording and analysis), NumPy (data processing), generative AI model

[1028] Data processing and calculation

[1029] 1. Real-time recording: Using the device camera, we record the process of students solving problems in real time. We use OpenCV to capture and save videos and screen captures.

[1030] 2. Data analysis: The recorded video data is sent to a server and analyzed using a generative AI model. The analysis extracts and visualizes changes in students' thought processes and attitudes.

[1031] 3. Feedback generation: Based on the analysis results, the server generates specific feedback for the student, including points for improvement and evaluation, and sends this feedback to the device.

[1032] Specific examples

[1033] For example, when recording the process of solving a math problem, the device's camera can be used to capture images of the student's hands and screen. The recorded video is then sent to a server where a generative AI model analyzes it. The analysis results provide information such as how the student attempted to solve the problem and at what step they got stuck. Based on this, the server can provide the student with specific feedback, such as, "You made a calculation error in this part. Try this method next time."

[1034] Prompt Sentence Examples

[1035] "Record the process students go through when solving math problems and analyze their thought processes and changes in attitude during that process. Specifically, visualize the thinking behind each step of the solution and provide feedback."

[1036] In this way, this invention makes it possible to record and analyze students' learning processes in detail and visualize changes in their thinking ability and attitudes. Furthermore, by using a generative AI model, the accuracy of the analysis results can be improved, and by implementing it as an application installed on mobile information devices such as smartphones and tablets, convenience can be improved.

[1037] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1038] Step 1:

[1039] The device records the student's problem-solving process in real time. Specifically, it takes a video using the device's camera and saves the video using OpenCV. The input is the camera image, and the output is the recorded video file.

[1040] Step 2:

[1041] The device sends the recorded video file to the server. Specifically, the video data is uploaded to the server via the network. The input is the video file, and the output is the video data stored on the server.

[1042] Step 3:

[1043] The server analyzes the received video data. Specifically, it processes the video data using a generative AI model to extract changes in the student's thought process and attitude. The input is the video data, and the output is the analysis results.

[1044] Step 4:

[1045] The server generates feedback based on the analysis results. Specifically, it generates written feedback for students, including specific improvements and evaluations based on the analysis results. The input is the analysis results, and the output is a feedback message.

[1046] Step 5:

[1047] The server sends the generated feedback message to the terminal. Specifically, the server sends the feedback message to the terminal via a network. The input is the feedback message, and the output is the feedback displayed on the terminal.

[1048] Step 6:

[1049] The terminal displays the received feedback message to the user. Specifically, the feedback message is displayed on the application screen so that the student can check it. The input is the feedback message, and the output is the feedback displayed on the screen.

[1050] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1051] "Example 1"

[1052] One embodiment of the present invention is a learning assessment system incorporating an emotion engine. This system converts a student's learning process into fair and accurate indicators, and objectively evaluates these indicators, the student's self-assessment, and teacher evaluations. Furthermore, it accurately captures changes in thinking ability and attitudes by linking situations and circumstances over time. Furthermore, this system is capable of learning using AI, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[1053] "Example 2"

[1054] The emotion engine recognizes emotions from students' facial expressions, tone of voice, choice of words, etc. For example, it recognizes how a student feels about a problem from their facial expressions and tone of voice when solving a problem. This emotion engine captures the emotions a student feels when solving a problem as data and reflects this in the evaluation of the learning process.

[1055] "Example 3"

[1056] Furthermore, the emotion engine adjusts the evaluation of the learning process based on the emotions it recognizes. For example, if a student shows high concentration while solving a problem, it will adjust the evaluation of that student's learning process upward. Conversely, if a student shows anxiety or hesitation while solving a problem, it will adjust the evaluation of that student's learning process downward. In this way, the emotion engine objectively evaluates students' emotions and reflects them in the evaluation of their learning process, thereby achieving fairer and more accurate learning evaluation.

[1057] The processing flow of each embodiment will be described below.

[1058] "Example 1"

[1059] Step 1: The learning assessment system translates student learning processes into fair and accurate indicators.

[1060] Step 2: Objectively evaluate the indicators, students' self-assessments, and teacher assessments.

[1061] Step 3: Accurately grasp the changes in thinking ability and attitudes across situations and circumstances over time.

[1062] Step 4: AI can learn, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[1063] "Example 2"

[1064] Step 1: The emotion engine recognizes emotions from students' facial expressions, tone of voice, choice of words, etc.

[1065] Step 2: Recognize how the student feels about the problem from their facial expressions and tone of voice as they solve it.

[1066] Step 3: The emotion engine captures data about the emotions students feel when solving problems and reflects this in the evaluation of the learning process.

[1067] "Example 3"

[1068] Step 1: The emotion engine adjusts the evaluation of the learning process based on the recognized emotions.

[1069] Step 2: If the student demonstrates high levels of concentration while solving the problem, revise the student's learning process rating upward.

[1070] Step 3: If a student shows signs of anxiety or uncertainty while solving the problem, lower the student's assessment of their learning process.

[1071] Step 4: The emotion engine objectively evaluates students' emotions and reflects them in the evaluation of the learning process, thereby achieving fairer and more accurate learning assessment.

[1072] Example 1

[1073] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1074] Conventional learning assessment systems have made it difficult to fairly and accurately evaluate students' learning processes. Furthermore, they lacked a means for objectively and comprehensively evaluating self-assessments and teacher assessments, making it impossible to accurately grasp changes in thinking ability and attitudes across situations and circumstances over time. Furthermore, there was a lack of support for novice teachers to easily and objectively create curriculum guidelines and report cards.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1076] In this invention, the server includes: means for converting a student's learning process into a fair and accurate indicator; means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and teacher assessments; means for accurately capturing changes in thinking ability and attitude over time across situations and circumstances; means for collecting the correctness or incorrectness of a student's answers, the time it took to answer, and the process leading up to the answer; means for storing the collected data in a database; means for analyzing the stored data and calculating indicators such as accuracy rate, answer speed, and problem-solving efficiency; means for analyzing the student's self-assessment and teacher assessment using an emotion engine; means for integrating the data analysis results and the emotion assessment results to perform a comprehensive evaluation; means for training an AI model using the collected data; and means for generating curriculum guidelines and report cards based on the comprehensive evaluation results and the predictions of the AI ​​model. This enables fair and accurate evaluation of a student's learning process and objectively evaluating the student's self-assessment and teacher assessments. Furthermore, even novice teachers can easily and objectively create curriculum guidelines and report cards.

[1077] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[1078] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating the learning process.

[1079] "Self-assessment" refers to students' own evaluation of their learning situation and achievements.

[1080] "Teacher evaluation" refers to a teacher's evaluation of a student's learning situation and achievements.

[1081] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[1082] "Transformation in thinking ability and attitude" refers to the deepening of students' thinking and changes in their learning attitudes.

[1083] "Response time" refers to the time it takes a student to answer a question.

[1084] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[1085] "Correct answer rate" refers to the percentage of questions answered correctly by a student.

[1086] "Speed ​​of answering" refers to the speed at which a student answers a question.

[1087] "Problem-solving efficiency" refers to the efficiency with which students solve problems.

[1088] An "emotion engine" refers to software or algorithms that analyze and evaluate emotions.

[1089] An "AI model" refers to a mathematical model that uses artificial intelligence to analyze data and make predictions and classifications.

[1090] A "curriculum guideline" refers to a document that defines the learning content and goals that an educational institution provides to students.

[1091] A "report card" is a document used to evaluate a student's learning achievements and attitude and notify parents and students.

[1092] The present invention provides a system for fairly and accurately evaluating a student's learning process and for comprehensively evaluating the student's learning process. Specific embodiments of this system will be described below.

[1093] 1. Program Generation

[1094] The system's program is generated using Python and includes modules for data collection, data storage, data analysis, sentiment evaluation, overall evaluation, AI model training, and report generation.

[1095] 2. Program processing explanation

[1096] The server collects the correct answers, the time it took to answer, and the process leading to the answer. This data is stored in a database (e.g., MySQL).

[1097] The server uses the collected data to quantify the student's learning process by processing the data using Python libraries (e.g., Pandas, NumPy) and calculating metrics such as accuracy rate, answer speed, and problem-solving efficiency.

[1098] The server uses an emotion engine (e.g., Microsoft Azure's emotion analysis API) to collect students' self-evaluations and evaluations from teachers, and then makes a comprehensive evaluation.

[1099] The server uses AI models (e.g., TensorFlow, Keras) to learn students' learning processes, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[1100] 3. Examples of concrete examples and prompts

[1101] As a concrete example, consider a student answering a math problem. The student takes 30 seconds to answer and gets it right. The server collects this data and stores it in a database. The server then analyzes this data and calculates the accuracy rate and answer speed. Furthermore, it uses an emotion engine to collect the student's self-evaluation (e.g., "I was in good form today") and the teacher's evaluation (e.g., "I answered quickly and accurately") to generate an overall evaluation.

[1102] Example prompt sentence:

[1103] Collect correct answers, answer times, and the steps taken to reach a solution, and use these to quantify the student's learning process. Additionally, use the emotion engine to collect student self-assessments and teacher evaluations to provide a comprehensive assessment.

[1104] In this way, the system of the present invention can convert the student's learning process into a fair and accurate indicator and perform comprehensive evaluation.

[1105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1106] Step 1: Data collection

[1107] The device records in real time whether the student answers a question correctly, how long it took to answer, and the process leading up to the answer. For example, when a student answers a math question, the device measures the time it takes to answer and records whether it answers correctly or incorrectly. The input is the student's answering behavior, and the output is the recorded data. The device then sends the recorded data to a server.

[1108] Step 2: Save data

[1109] The server stores the received data in a database (e.g. MySQL). The input is the data sent from the terminal, and the output is the data stored in the database. The server checks the integrity of the data and whether there is any invalid data.

[1110] Step 3: Data analysis

[1111] The server uses Python libraries (e.g., Pandas, NumPy) to analyze the stored data. The input is the data stored in the database, and the output is the analysis results. The server calculates indicators such as the accuracy rate, solution speed, and problem-solving efficiency. For example, it calculates that the accuracy rate is 80% and the average solution time is 25 seconds.

[1112] Step 4: Emotional assessment

[1113] Users (students) input their self-evaluation after learning (e.g., "I felt good today"). Users (teachers) input their evaluation of the student's learning process (e.g., "Your answers were quick and accurate"). The inputs are student and teacher evaluations, and the output is the emotional evaluation results. The server analyzes these evaluations using an emotional engine (e.g., Microsoft Azure's Emotion Analysis API).

[1114] Step 5: Overall evaluation

[1115] The server integrates the data analysis results and the emotion evaluation results to perform an overall evaluation. The inputs are the data analysis results and the emotion evaluation results, and the output is the overall evaluation result. The server stores the overall evaluation result in a database.

[1116] Step 6: Training the AI ​​model

[1117] The server uses the collected data to train an AI model (e.g., TensorFlow, Keras). The input is the collected data, and the output is a trained AI model. For example, it learns a student's learning patterns based on past data.

[1118] Step 7: Generate reports

[1119] The server generates curriculum guidelines and report cards based on the overall assessment results and the prediction results of the AI ​​model. The input is the overall assessment results and the prediction results of the AI ​​model, and the output is the generated report. For example, a report may be created stating that "Student A has a high rate of correct answers to math problems and is also fast at answering them." The server sends the generated report to the terminal so that the user (teacher) can check it.

[1120] (Application example 1)

[1121] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1122] Conventional learning assessment and work evaluation systems have difficulty in fairly and accurately evaluating the processes of students and workers. Furthermore, because evaluations do not take into account changes in emotions and motivation, comprehensive performance evaluations are insufficient. Furthermore, there was a need for a system that allows novice teachers and administrators to easily conduct evaluations.

[1123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1124] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for accurately grasping changes in thinking ability and attitude linking scenes and situations over time, means for converting a worker's work process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the worker's self-assessment, and the manager's evaluation, and means for analyzing emotions during work and evaluating stress levels and motivation. This makes it possible to fairly and accurately evaluate the student's or worker's process and to comprehensively evaluate performance taking changes in emotions and motivation into account.

[1125] The "learning process" is the series of steps by which students acquire knowledge and skills through learning activities.

[1126] An "indicator" is a number or data that serves as a basis for evaluation or judgment.

[1127] "Self-evaluation" refers to an individual's evaluation of their own behavior and achievements.

[1128] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[1129] "Thinking ability" is the intellectual ability to solve problems and make decisions.

[1130] "Attitudinal change" refers to changes in an individual's behavior and thinking over time.

[1131] A "work process" is a series of procedures or methods that a worker follows to carry out a task.

[1132] "Emotion analysis" is a technology that collects and analyzes an individual's emotional state as data.

[1133] "Stress level" is an index that indicates the degree of stress felt by an individual.

[1134] "Motivation" is the internal drive or motivation that drives an individual to take action.

[1135] A system for implementing the present invention has the following configuration.

[1136] System configuration

[1137] 1. Hardware:

[1138] Camera: Records the worker's work process and collects image data.

[1139] Biosensors: Monitor workers' heart rate and other biometric data.

[1140] Server: Collects, analyzes, and stores data.

[1141] 2. Software:

[1142] OpenCV: A library for image processing.

[1143] EmotionRecognizer: Software for analyzing emotions from workers' facial expressions.

[1144] scikit-learn: A machine learning library for performing data analysis.

[1145] Data collection and analysis

[1146] The server converts the worker's work process into a fair and accurate indicator based on the data collected from the camera and biosensor. Specifically, it processes the data in the following steps:

[1147] 1. Data Collection:

[1148] The camera records the worker's work and captures image data for each frame.

[1149] Biosensors collect workers' heart rates and other biometric data in real time.

[1150] 2. Data Analysis:

[1151] Using OpenCV, the accuracy and speed of work are analyzed from the collected image data.

[1152] Using EmotionRecognizer, the facial expressions of workers are analyzed from the collected image data to obtain emotional data.

[1153] Using scikit-learn, we assess workers' stress levels and motivation based on collected biometric and emotional data.

[1154] comprehensive evaluation

[1155] The server quantifies the worker's work process based on the analyzed data and objectively evaluates the indicators, the worker's self-evaluation, and the manager's evaluation, making it possible to comprehensively evaluate the worker's performance.

[1156] Specific examples

[1157] For example, consider a worker performing assembly work in a factory. The accuracy, speed, and procedures of the various tasks performed by this worker are recorded with a camera, and biosensors are used to monitor the worker's heart rate and facial expressions while they work. Based on the collected data, the worker's skills and emotions are quantified and comprehensively evaluated.

[1158] Prompt Sentence Examples

[1159] Develop a system to evaluate the skills and emotions of assembly workers in a factory. Record their work with a camera and monitor their heart rate with a biosensor. Based on the collected data, quantify and comprehensively evaluate the worker's skills (accuracy, speed, efficiency) and emotions (stress level, motivation).

[1160] In this way, the mode for carrying out the invention can be concretely shown.

[1161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1162] Step 1:

[1163] The server uses a camera to record the worker's work process. The input is video data from the camera, and the output is image data for each frame. Specifically, the camera captures the worker's movements in real time and saves them frame by frame.

[1164] Step 2:

[1165] The server collects the worker's heart rate and other biometric data using a biosensor. The input is the biometric data from the biosensor, and the output is real-time heart rate data. Specifically, the biosensor monitors the worker's heart rate and transmits the data to the server.

[1166] Step 3:

[1167] The server analyzes the accuracy and speed of the work from the image data collected using OpenCV. The input is image data for each frame, and the output is analytical data on the accuracy and speed of the work. Specifically, OpenCV processes the image data and evaluates the work procedure and speed.

[1168] Step 4:

[1169] The server uses EmotionRecognizer to analyze the facial expressions of the workers from the collected image data and obtain emotional data. The input is image data for each frame, and the output is emotional data. Specifically, EmotionRecognizer analyzes the image data and identifies the emotions from the workers' facial expressions.

[1170] Step 5:

[1171] The server evaluates the worker's stress level and motivation based on biometric and emotional data collected using scikit-learn. The input is heart rate data and emotional data, and the output is evaluation data related to stress level and motivation. Specifically, scikit-learn analyzes the biometric and emotional data to quantify the worker's stress level and motivation.

[1172] Step 6:

[1173] The server quantifies the worker's work process based on the analyzed data and objectively evaluates the indicators, the worker's self-assessment, and the manager's assessment. The input is analytical data on the worker's accuracy and speed, emotional data, and assessment data on stress level and motivation, and the output is comprehensive assessment data. Specifically, the server integrates this data and comprehensively evaluates the worker's performance.

[1174] Example 2

[1175] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1176] Conventional learning assessment systems have the problem that student self-assessments and teacher assessments are subjective, making it difficult to achieve an objective overall assessment. Furthermore, because assessments do not take into account students' emotions, it is difficult to accurately grasp the true level of understanding and changes in attitudes during the learning process. Furthermore, there are issues with the difficulty for novice teachers to easily and objectively create curriculum guidelines and report cards.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1178] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately capturing changes in thinking ability and attitude across a timeline of situations and circumstances, means for recognizing emotions by analyzing the student's facial expressions and tone of voice, means for reflecting the emotion data in the assessment of the learning process, and means for integrating the self-assessment data, teacher assessment data, and emotion data to generate an overall assessment. This makes it possible to objectively and comprehensively evaluate a student's learning situation, allowing even a novice teacher to easily create curriculum guidelines and report cards.

[1179] "Means of replacing students' learning processes with fair and accurate indicators" refers to means of providing standards and metrics for objectively evaluating students' learning status.

[1180] "Student self-assessment" refers to the act of students themselves evaluating their own learning situation and level of understanding and entering the results into the system.

[1181] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning status and entering the results into the system.

[1182] "Objective comprehensive evaluation means" refers to a means of integrating students' self-evaluations, teacher evaluations, and other data to make a comprehensive evaluation without relying on subjectivity.

[1183] "A means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time" is a means of tracking and evaluating changes in students' thinking ability and attitudes over time during the learning process.

[1184] The "means of recognizing emotions by analyzing a student's facial expression and tone of voice" refers to a means of capturing a student's facial expression and tone of voice using a camera or microphone, and analyzing them to recognize the student's emotions.

[1185] The "means for reflecting the emotion data in the evaluation of the learning process" refers to a means for incorporating the recognized emotion data as part of the learning evaluation and reflecting it in the overall evaluation.

[1186] The "means for generating a comprehensive evaluation by integrating self-evaluation data, teacher-evaluation data, and emotion data" is a means for integrating various evaluation data to generate a final comprehensive evaluation.

[1187] The present invention provides a system for fairly and accurately evaluating a student's learning process. The system is implemented using the following hardware and software.

[1188] Hardware and software used

[1189] Hardware: cameras, microphones, servers

[1190] Software: Emotion engine, database management system, rating algorithm

[1191] Program processing

[1192] 1. Self-evaluation input by the user (student)

[1193] The user self-evaluates their learning status. For example, the user may enter "My understanding of mathematics is 80%" in the evaluation form displayed on the device screen.

[1194] The terminal transmits the self-evaluation data entered by the user to the server.

[1195] The server stores the received self-assessment data in a database.

[1196] 2. Teachers enter their assessments

[1197] Teachers evaluate students' learning status and input the results into the system via a terminal. For example, a teacher might evaluate "Student A's level of understanding in mathematics is 85%" and input the results into the terminal.

[1198] The terminal transmits the evaluation data entered by the teacher to the server.

[1199] The server stores the received teacher evaluation data in a database.

[1200] 3. Emotion Recognition by Emotion Engine

[1201] The server uses a camera and microphone to capture students' facial expressions and tone of voice in real time.

[1202] The server sends the captured data to the emotion engine.

[1203] The emotion engine analyzes the received data and recognizes the student's emotions. For example, if a student has a "confused" expression while solving a problem, the emotion engine will recognize that emotion as "confused."

[1204] The emotion engine transmits the recognized emotion data to the server.

[1205] The server stores the received emotion data in a database.

[1206] 4. Data integration and generation of overall assessment

[1207] The server obtains self-evaluation data, teacher-evaluation data, and emotion data from the database.

[1208] The server uses an evaluation algorithm to integrate these data and generate an overall evaluation. For example, if the self-evaluation is 80%, the teacher's evaluation is 85%, and the emotion data is "confused," the overall evaluation will be "The student has a high level of understanding, but is confused when solving problems."

[1209] The server stores the generated overall evaluation in a database and notifies the user or teacher as necessary.

[1210] Specific examples

[1211] When a user inputs their self-evaluation, the terminal displays the evaluation form and transmits the input contents to the server in real time.

[1212] When a teacher inputs an evaluation, the terminal displays an evaluation form for the teacher and sends the input to the server.

[1213] The server periodically sends data from the camera and microphone to the emotion engine to recognize emotions in real time.

[1214] The emotion engine uses facial expression analysis algorithms and voice analysis algorithms to recognize emotions and sends the results to the server.

[1215] The server uses a rating algorithm to combine the data and adjust the weighting of each piece of data when generating an overall rating.

[1216] Prompt Sentence Examples

[1217] "Students have entered their self-assessment. Now enter your teacher assessment."

[1218] "The emotion engine has analyzed your students' expressions. Please review the results."

[1219] "An overall assessment has been generated. View the results."

[1220] In this way, the system performs specific actions at each step to comprehensively evaluate the student's learning situation.

[1221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1222] Step 1:

[1223] User self-assessment

[1224] Input: The user self-evaluates their learning status. For example, the user enters "My understanding of mathematics is 80%" in the evaluation form displayed on the device screen.

[1225] Specific operation: The device transmits the self-evaluation data entered by the user to the server in real time.

[1226] Output: The server stores the received self-assessment data in a database.

[1227] Step 2:

[1228] Teachers enter their assessments

[1229] Input: The teacher evaluates the student's learning status and inputs the results into the system via the terminal. For example, the teacher evaluates "Student A's level of understanding in mathematics is 85%" and inputs this into the terminal.

[1230] Specific operation: The device transmits the evaluation data entered by the teacher to the server in real time.

[1231] Output: The server stores the received teacher evaluation data in a database.

[1232] Step 3:

[1233] Emotion recognition by emotion engine

[1234] Input: The server uses a camera and microphone to capture students' facial expressions and tone of voice in real time.

[1235] Specific operation: The server sends the captured data to the emotion engine. The emotion engine analyzes the received data and recognizes the student's emotion. For example, if a student has a "confused" expression while solving a problem, the emotion engine recognizes that emotion as "confused."

[1236] Output: The emotion engine sends the recognized emotion data to the server, which stores the data in a database.

[1237] Step 4:

[1238] Integrating data and generating an overall assessment

[1239] Input: The server retrieves self-assessment data, teacher-assessment data, and emotion data from the database.

[1240] Specific operation: The server uses an evaluation algorithm to integrate these data and generate an overall evaluation. For example, if the self-evaluation is 80%, the teacher's evaluation is 85%, and the emotion data is "confused," the overall evaluation will be "The student has a good understanding, but is confused when solving problems."

[1241] Output: The server stores the generated overall evaluation in a database and notifies the user or teacher as needed.

[1242] In this way, the system performs specific actions at each step to comprehensively evaluate the student's learning situation.

[1243] (Application example 2)

[1244] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1245] Conventional learning assessment systems have had the problem of making it difficult to fairly and accurately evaluate students' learning progress. In particular, they were unable to grasp changes in students' emotions and attitudes in real time and reflect them in learning assessments. It was also difficult for novice teachers to easily and objectively create curriculum guidelines and report cards.

[1246] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1247] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for accurately capturing changes in thinking ability and attitude over time, means for analyzing the student's facial expressions and tone of voice in real time to recognize their emotions, and means for integrating the emotion recognition results, self-assessment results, and teacher evaluation results to calculate an overall evaluation score. This allows for a comprehensive evaluation of a student's learning situation, making it possible for even a novice teacher to easily and objectively create curriculum guidelines and report cards.

[1248] "Means of replacing students' learning processes with fair and accurate indicators" refers to methods that provide standards and metrics for objectively evaluating students' learning activities and achievements.

[1249] "Means for objectively comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" refers to a method of integrating the student's own assessment and the teacher's assessment to objectively perform a comprehensive assessment.

[1250] "A means of accurately capturing changes in thinking ability and attitudes by linking scenes and situations over time" is a method of accurately grasping changes in students' thinking ability and attitudes over time during the learning process.

[1251] "Method of analyzing students' facial expressions and tone of voice in real time to recognize their emotions" refers to a method of using a camera or microphone to analyze students' facial expressions and tone of voice in real time to recognize their emotions.

[1252] The "means for integrating emotion recognition results, self-assessment results, and teacher evaluation results to calculate a comprehensive evaluation score" is a method for integrating emotion recognition results, the student's own evaluation, and the teacher's evaluation to calculate a comprehensive evaluation score.

[1253] A system for implementing the present invention has the following configuration.

[1254] System Configuration

[1255] 1. Hardware:

[1256] Camera: Used to capture students' facial expressions in real time.

[1257] Microphone: Used to capture the student's tone of voice in real time.

[1258] Smartphone or head-mounted display (HMD): A device with a built-in camera and microphone that collects data in real time.

[1259] 2. Software:

[1260] Emotion recognition engine: This engine analyzes students' facial expressions and tone of voice to recognize their emotions. Specifically, it uses Microsoft Azure's Emotion API and Google Cloud's Speech-to-Text API.

[1261] Evaluation integration system: A system for integrating emotion recognition results, self-assessment results, and teacher evaluation results to calculate an overall evaluation score.

[1262] Data processing and calculation

[1263] 1. Emotion recognition:

[1264] The server sends the student's facial expression and voice data obtained from the camera and microphone to the emotion recognition engine.

[1265] The emotion recognition engine analyzes facial expression data to recognize emotions such as joy, sadness, and surprise, and also analyzes voice data to recognize emotions from the tone of voice.

[1266] 2. Evaluation Integration:

[1267] The server collects the students' self-assessment results and the teacher's assessment results.

[1268] The server integrates the emotion recognition results, self-assessment results, and teacher assessment results to calculate an overall assessment score, which allows for a comprehensive evaluation of the student's learning status.

[1269] Specific examples

[1270] For example, when a student is taking an online class, their facial expressions and tone of voice are captured in real time using their smartphone's camera and microphone. The server then sends this data to an emotion recognition engine to recognize their emotions. The recognized emotion data is then combined with self-assessment results and teacher evaluation results to calculate an overall assessment score. This score is then used to objectively evaluate the student's learning progress.

[1271] Prompt Sentence Examples

[1272] Below are some example prompts to input to a generative AI model:

[1273] I am developing a system to comprehensively evaluate students' learning progress. I would like to create an application that can analyze students' facial expressions and tone of voice in real time to recognize their emotions. How can I use the Azure Face API and Google Cloud Speech-to-Text API to perform emotion and voice recognition and calculate an overall evaluation score based on the results?

[1274] In this way, the present invention makes it possible to comprehensively evaluate the learning status of students, and even a novice teacher can easily and objectively prepare curriculum guidelines and report cards.

[1275] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1276] Step 1:

[1277] The device uses a camera and microphone to capture students' facial expression and voice data in real time. The input is the student's facial expression and voice, and the output is the captured image data and voice data.

[1278] Step 2:

[1279] The device transmits the captured facial expression data and voice data to the server. The input is the captured image data and voice data, and the output is the data transmitted to the server.

[1280] Step 3:

[1281] The server sends the received facial expression data to the emotion recognition engine. The input is the facial expression data, and the output is the data sent to the emotion recognition engine.

[1282] Step 4:

[1283] The emotion recognition engine analyzes facial expression data and recognizes emotions such as joy, sadness, surprise, etc. The input is facial expression data, and the output is recognized emotion data.

[1284] Step 5:

[1285] The server sends the received voice data to a voice recognition engine. The input is the voice data, and the output is the data sent to the voice recognition engine.

[1286] Step 6:

[1287] A speech recognition engine analyzes speech data and recognizes emotions from the tone of voice. The input is speech data, and the output is recognized emotional data.

[1288] Step 7:

[1289] The server integrates the emotion data received from the emotion recognition engine and the voice recognition engine. The input is emotion data from facial expressions and emotion data from tone of voice, and the output is the integrated emotion data.

[1290] Step 8:

[1291] The user (student) inputs their self-evaluation data into the terminal. The input is the student's self-evaluation data, and the output is the self-evaluation data entered into the terminal.

[1292] Step 9:

[1293] The user (teacher) inputs the student's evaluation into the terminal. The input is the teacher's evaluation data, and the output is the teacher's evaluation data input into the terminal.

[1294] Step 10:

[1295] The server integrates the integrated emotion data, self-assessment data, and teacher evaluation data to calculate a comprehensive evaluation score. The inputs are the integrated emotion data, self-assessment data, and teacher evaluation data, and the output is the comprehensive evaluation score.

[1296] Step 11:

[1297] The server sends the calculated overall evaluation score to the terminal and displays it to the user. The input is the overall evaluation score, and the output is the overall evaluation score displayed on the terminal.

[1298] Example 3

[1299] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1300] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, making it difficult for inexperienced teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means to accurately capture changes in students' thinking ability and attitudes over time, making it difficult to evaluate students' learning processes in detail. Furthermore, learning assessments do not take students' emotions into account, resulting in a lack of fairness and accuracy in learning assessments.

[1301] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1302] In this invention, the server includes: a means for converting a student's learning process into a fair and accurate indicator; a means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and the teacher's assessment; a means for accurately capturing changes in thinking ability and attitude across a timeline; a means for recording the student's answer process in real time; a means for analyzing the recorded data with an emotion recognition engine; a means for saving and visualizing the analysis results in a database; and a means for adjusting the assessment of the learning process based on the emotion data. This allows for a detailed and fair assessment of the student's learning process, making it easy for even novice teachers to create curriculum guidelines and report cards. Furthermore, because learning assessment takes students' emotions into account, more accurate learning assessment can be achieved.

[1303] The "student learning process" is the series of steps by which students acquire knowledge and skills through learning activities.

[1304] "Fair and accurate indicators" are standards for objectively and accurately evaluating students' learning outcomes.

[1305] "Self-assessment" is the act of students themselves evaluating their own learning outcomes and progress.

[1306] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning outcomes and attitude.

[1307] "Changes in thinking ability and attitude" refers to the way in which a student's train of thought and attitude change over time.

[1308] "Real-time recording means" refers to a method or device for instantly recording a student's answer process on the spot.

[1309] An "emotion recognition engine" is software or hardware that identifies emotions from students' facial expressions and tone of voice.

[1310] A "database" is a system for systematically storing analysis results and other data.

[1311] A "visualization tool" is a method or tool for displaying data in a visual format, such as a graph or chart.

[1312] The "means for adjusting the assessment of the learning process" is a method or system for correcting the learning assessment based on the emotional data of the students.

[1313] The present invention provides a system for fairly and accurately evaluating a student's learning process. A specific embodiment of this system will be described below.

[1314] 1. Generating the system program

[1315] The server generates a program for recording, analyzing, and evaluating the student's learning process. This program uses the following hardware and software:

[1316] Hardware: High-performance server (e.g. general-purpose server), camera, microphone

[1317] Software: Data analysis tools (e.g., Python, Pandas, Matplotlib), emotion recognition engines (e.g., general emotion recognition software)

[1318] 2. Explanation of program processing

[1319] The server records the student's answer process in real time as it is sent from the device. The device uses a camera and microphone to capture the student's facial expressions and voice, and sends this data to the server. The server inputs the received data into an emotion recognition engine and analyzes the emotional data. The analysis results are stored in a database, processed using Python's Pandas library, and visualized with Matplotlib. Finally, the server adjusts the evaluation of the learning process based on the emotional data.

[1320] 3. Specific Examples

[1321] A user uses the system to record Student A's process of solving a math problem. Student A's facial expressions and voice are captured using the device's camera and microphone and sent to the server. The server analyzes the data using an emotion recognition engine and stores the results in a database. The server then processes the data using Pandas and visualizes it with Matplotlib. Finally, the server evaluates Student A's learning process based on the emotion data and upwardly revises the evaluation if the student's concentration is high.

[1322] Prompt Sentence Examples

[1323] "Please record the process of Student A solving a math problem and analyze it using an emotion recognition engine. Based on the analysis results, please evaluate and visualize the learning process."

[1324] In this way, the server, terminals, and users cooperate to operate the system, and the learning process of the students can be accurately evaluated. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1325] Step 1:

[1326] Recording the student's answer process

[1327] Input: Questions answered by students, video and audio data from camera and microphone

[1328] Specific operation: The user answers questions using the device, which uses a camera and microphone to capture the student's facial expressions and voice in real time.

[1329] Output: Video and audio data is sent to the server.

[1330] Step 2:

[1331] Emotional Data Analysis

[1332] Input: Video and audio data sent to the server

[1333] Specific operation: The server inputs the received video and audio data into the emotion recognition engine, which identifies emotions from the student's facial expressions and tone of voice.

[1334] Output: Emotional data (e.g., concentration, joy, anxiety, uncertainty) is generated.

[1335] Step 3:

[1336] Data storage and visualization

[1337] Input: Parsed emotion data

[1338] How it works: The server stores the analyzed sentiment data in a database along a time axis, then processes the data using Python's Pandas library and visualizes it with Matplotlib.

[1339] Output: Visualized data (e.g., graphs and charts) are generated.

[1340] Step 4:

[1341] Assessment and adjustment of the learning process

[1342] Input: Visualized emotion data

[1343] How it works: The server evaluates the learning process based on the emotional data. If the user shows high concentration, the evaluation is revised upward, and if the user shows anxiety or hesitation, the evaluation is revised downward.

[1344] Output: A tailored learning assessment is generated.

[1345] In this way, the server, terminals, and users can work together to operate the system, enabling accurate evaluation of students' learning processes.

[1346] (Application example 3)

[1347] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1348] Conventional learning assessment systems and work evaluation systems have difficulty accurately grasping changes in the train of thought and attitude of learners and workers over time. They also cannot reflect changes in emotions in their evaluations, making it difficult to provide fair and accurate evaluations. Furthermore, there was a need for a system that allows even novice teachers and inexperienced work instructors to easily conduct evaluations.

[1349] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1350] In this invention, the server includes means for converting a student's learning process into a fair and accurate indicator, means for objectively and comprehensively evaluating the indicator, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude across a timeline, means for recording the worker's movements and facial expressions in real time and analyzing them with an emotion recognition engine, means for visualizing the worker's thought process and changes in attitude along a timeline based on the analysis results, means for evaluating the work process using a generative AI model, and means for evaluating the worker's emotions and adjusting the evaluation of the work process. This makes it possible to accurately grasp changes in the student's or worker's thought process and attitude along a timeline and perform fair and accurate evaluations.

[1351] "Means of converting students' learning processes into fair and accurate indicators" refers to methods of converting students' learning activities into criteria for objective evaluation.

[1352] "Means for objectively and comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" refers to a method for objectively evaluating the generated indicators by integrating the student's own assessment and the teacher's assessment.

[1353] "A means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time" is a method of accurately recording changes in thinking and attitudes in specific situations and circumstances over time.

[1354] "Means of recording the worker's actions and facial expressions in real time and analyzing them with an emotion recognition engine" refers to a method of instantly recording the worker's actions and facial expressions and analyzing them using emotion recognition technology.

[1355] "Means for visualizing the flow of thoughts and changes in attitude of workers over time based on the analysis results" refers to a method for visually displaying changes in thoughts and attitude of workers over time based on analyzed data.

[1356] "Means for evaluating work processes using generative AI models" refers to a method for evaluating the progress and efficiency of work using a generated artificial intelligence model.

[1357] The "means for assessing the worker's emotions and adjusting the evaluation of the work process" is a method for assessing the worker's emotional state and appropriately correcting the evaluation of the work based on the results.

[1358] A system for implementing this invention has the following configuration: First, the server provides means for converting a student's learning process into a fair and accurate indicator. This means converts the learning activity into a standard for objectively evaluating the learning activity. Next, it provides means for objectively comprehensively evaluating the indicator, the student's self-evaluation, and the teacher's evaluation. This means objectively evaluates the generated indicator by integrating the student's own evaluation and the teacher's evaluation.

[1359] Furthermore, the server provides a means to accurately capture changes in thinking ability and attitudes across situations and circumstances over time. This means accurately records changes in thinking and attitudes in specific situations and circumstances over time. It also provides a means to record the worker's actions and facial expressions in real time and analyze them using an emotion recognition engine. This means instantly records the worker's actions and facial expressions and analyzes them using emotion recognition technology.

[1360] The server also includes a means for visualizing the worker's thought process and changes in attitude over time based on the analysis results. This means visually displays the changes in the worker's thought process and attitude over time based on the analyzed data. Furthermore, a means for evaluating the work process using a generative AI model is also provided. This means evaluates the progress and efficiency of the work using the generated artificial intelligence model.

[1361] Finally, the server provides a means for evaluating the worker's emotions and adjusting the evaluation of the work process, which evaluates the worker's emotional state and appropriately modifies the evaluation of the work based on the results.

[1362] Hardware and software used

[1363] Hardware: Factory robots, cameras, sensors

[1364] Software: Image recognition software, emotion recognition engines, data analysis tools, generative AI models

[1365] Data processing and calculation

[1366] The server uses cameras and sensors installed on factory robots to record the movements and facial expressions of workers in real time. The collected data is analyzed using image recognition software, and changes in the worker's movements and facial expressions are recorded over time. Next, an emotion recognition engine evaluates the worker's emotions based on the data analyzed by the image recognition software. The emotion recognition engine identifies emotions such as concentration, anxiety, and hesitation from the worker's facial expressions and movements.

[1367] Based on the collected data and the results of emotion recognition, the data analysis tool visualizes the worker's thought process and changes in attitude over time. A generative AI model is then used to evaluate the work process. For example, if the worker shows high concentration, the evaluation of that work process is revised upward. Conversely, if the worker shows anxiety or hesitation, the evaluation is revised downward.

[1368] Examples and prompts

[1369] For example, consider a worker performing assembly work in a factory. The worker's movements and facial expressions are recorded using cameras and sensors and analyzed using an emotion recognition engine. Based on the analysis results, it is possible to visualize which processes the worker is concentrating on and which processes he or she is feeling confused or anxious about. This makes it possible to identify specific areas for improvement to increase the worker's efficiency.

[1370] Prompt Sentence Examples

[1371] "Record the movements and facial expressions of workers performing assembly work in a factory and analyze them using an emotion recognition engine. Based on the analysis results, visualize the worker's train of thought and changes in attitude over time and evaluate the work process. If the worker shows high concentration, revise the evaluation of that work process upward; conversely, if the worker shows anxiety or hesitation, revise it downward."

[1372] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1373] Step 1:

[1374] The server uses cameras and sensors installed on factory robots to record the movements and facial expressions of workers in real time.

[1375] Input: Real-time data of worker's movements and facial expressions

[1376] Output: Recorded movement and facial expression data

[1377] Specific actions: Cameras and sensors capture the worker's movements and facial expressions and send the data to a server.

[1378] Step 2:

[1379] The server analyzes the recorded data through image recognition software.

[1380] Input: Recorded movement and facial expression data

[1381] Output: Analyzed behavior and facial expression feature data

[1382] Specific actions: Image recognition software analyzes movement and facial expression data to extract features.

[1383] Step 3:

[1384] The server evaluates the worker's emotions using an emotion recognition engine based on the analyzed data.

[1385] Input: Analyzed behavior and facial expression feature data

[1386] Output: Emotion evaluation data of workers

[1387] Specific operation: The emotion recognition engine analyzes the feature data and identifies the worker's emotional state (concentration, anxiety, hesitation, etc.).

[1388] Step 4:

[1389] The server visualizes the worker's flow of thought and changes in attitude over time based on the emotional evaluation data.

[1390] Input: Emotional evaluation data of workers

[1391] Output: Visualization of thought patterns and attitude changes over time

[1392] What it does: The data analysis tool organizes the emotion assessment data over time and displays it visually.

[1393] Step 5:

[1394] The server uses a generative AI model to evaluate the work process.

[1395] Input: Visualization of thought patterns and attitude changes over time

[1396] Output: Evaluation data of work processes

[1397] How it works: A generative AI model analyzes the visualization data and evaluates the efficiency and quality of the work process.

[1398] Step 6:

[1399] The server evaluates the worker's emotions and adjusts the evaluation of the work process.

[1400] Input: Work process evaluation data, worker emotion evaluation data

[1401] Output: Evaluation data of the adjusted work process

[1402] Specific operation: The server adjusts the evaluation of the work process upward or downward based on the emotional evaluation data.

[1403] Step 7:

[1404] The server provides feedback of the final evaluation data to the worker or manager.

[1405] Input: Adjusted work process evaluation data

[1406] Output: Feedback data for workers and managers

[1407] Specific operation: The server compiles the evaluation data into a report format and provides it to workers and managers.

[1408] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1410] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1411] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1412] [Third embodiment]

[1413] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1414] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1415] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1416] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1417] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1418] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1419] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1420] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1421] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1422] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1423] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1424] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1425] "Example 1"

[1426] The system of the present invention has a means for converting a student's learning process into a fair and accurate indicator. Specifically, it collects data such as the correctness or incorrectness of the student's answers, the time it took to answer, and the process leading up to the answer, and then quantifies the student's learning process based on this data. This quantification can be expressed as an indicator such as the percentage of correct answers, the speed of answering, or the efficiency of problem-solving.

[1427] "Example 2"

[1428] Furthermore, the system of the present invention has a means for objectively and comprehensively evaluating the above indicators, students' self-evaluations, and teacher evaluations. Specifically, students themselves perform self-evaluations and input the results into the system. Teachers also evaluate students' learning progress and input the results into the system. The system combines these evaluation results with the above indicators to comprehensively evaluate students' learning progress.

[1429] "Example 3"

[1430] The system of the present invention also has a means for accurately capturing changes in thinking ability and attitude across a timeline of situations and situations. Specifically, the system records the student's answering process along a timeline and analyzes changes in the flow of thought and attitude during that process. This makes it possible to visually grasp changes in the student's thinking ability, judgment, and expressiveness along a timeline.

[1431] The processing flow of each embodiment will be described below.

[1432] "Example 1"

[1433] Step 1: Collect data such as whether students answered the questions correctly, the time it took them to answer, and the process they used to arrive at the answer.

[1434] Step 2: Quantify the student's learning process based on the collected data. This quantification can be expressed as indicators such as the rate of correct answers, answering speed, and problem-solving efficiency.

[1435] "Example 2"

[1436] Step 1: Students self-assess and enter the results into the system.

[1437] Step 2: Teachers also evaluate students' learning progress and enter the results into the system.

[1438] Step 3: The system combines these assessment results with the above indicators to comprehensively evaluate the student's learning status.

[1439] "Example 3"

[1440] Step 1: Record the student's response process over time.

[1441] Step 2: Analyze the flow of thoughts and changes in attitude during the recorded process.

[1442] Step 3: Based on the analysis results, visually grasp the changes in students' thinking, judgment, and expression abilities over time.

[1443] Example 1

[1444] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1445] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, making it particularly difficult for novice teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means for objectively and comprehensively evaluating students' self-assessments and teacher assessments, making it difficult to accurately grasp changes in thinking ability and attitudes across time and situations.

[1446] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1447] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude across a timeline of situations and circumstances, means for collecting data on whether the student answered a question correctly, the time it took to answer, and the process leading up to the answer, means for analyzing the collected data and calculating indexes such as the rate of correct answers, answer speed, and problem-solving efficiency, and means for visualizing the calculated indexes so that users can view them. This allows for a fair and accurate evaluation of a student's learning process, making it possible for even novice teachers to easily and objectively prepare curriculum guidelines and report cards.

[1448] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[1449] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating students' learning outcomes.

[1450] "Self-assessment" refers to the act of students themselves evaluating their own learning outcomes and progress.

[1451] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning outcomes and progress.

[1452] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[1453] "Transformation of thinking ability and attitude" refers to changes in students' ways of thinking and behavior.

[1454] "Correctness of answer" refers to information indicating whether the question answered by the student is correct or incorrect.

[1455] "Response time" refers to the time it takes a student to answer a question.

[1456] The "solution process" refers to the sequence of steps or thought processes a student goes through to solve a problem.

[1457] "Data collection" refers to the act of gathering the necessary information.

[1458] "Data analysis" refers to the act of analyzing collected data and extracting useful information.

[1459] "Correct answer rate" refers to the percentage of questions answered correctly by a student.

[1460] "Speed ​​of answering" refers to the speed at which a student answers a question.

[1461] "Problem-solving efficiency" refers to the effective methods and procedures students use to solve problems.

[1462] "Visualization" refers to the display of data or information in a visual format, such as a graph or chart.

[1463] "Users" refer to teachers and students who use this system.

[1464] This invention is a system for fairly and accurately evaluating a student's learning process. This system operates in cooperation with a server, a terminal, and a user.

[1465] First, when students use a learning application to answer questions, the device records in real time whether the answer is correct, the time it takes to answer, and the process leading up to the answer. Specifically, this data is collected through a dedicated application on the device (e.g., a learning management system app).

[1466] The device then transmits the collected data to a server. This transmission is carried out over the Internet, and the data is encrypted. For example, the device transmits the recorded answer data to the server using the HTTPS protocol.

[1467] The server stores the received data in a database (e.g., MySQL). The stored data is analyzed using a Python data analysis library (e.g., Pandas, NumPy). The server reads the data, imputes missing values, and normalizes the data.

[1468] Based on the analyzed data, the server calculates metrics such as the correct answer rate, answer speed, and problem-solving efficiency. These metrics are calculated using Python data analysis libraries. For example, the server calculates each student's correct answer rate, calculates the average answer time, and calculates a score to evaluate the efficiency of problem-solving.

[1469] The calculated metrics are visualized using data visualization tools (e.g., Matplotlib, Seaborn). The server displays the generated graphs and charts in a web interface for easy access by users. For example, a teacher can open a web browser and view a dashboard showing students' learning progress.

[1470] For example, when a student answers a math problem, the device records whether the answer is correct and measures the time it takes to answer. The device sends this data to a server, which stores it in a database. The server analyzes the stored data and calculates the accuracy rate and answer speed. The calculation results are visualized as graphs and charts, and users can view these results through a web interface.

[1471] Examples of prompts include:

[1472] "Please collect the correct answers, answer time, and process leading to the answer of the questions answered by the students, and use this information to generate a program that quantifies the students' learning process. Calculate and visualize the specific indicators such as the percentage of correct answers, answer speed, and problem-solving efficiency."

[1473] In this way, the system of the present invention fairly and accurately evaluates students' learning processes, allowing even novice teachers to easily and objectively create curriculum guidelines and report cards.

[1474] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1475] Step 1: Data collection

[1476] When a student answers questions using a learning application, the device records in real time whether the answer is correct, the time it took to answer, and the process leading up to the answer. The input is the student's answer data (correct or incorrect, the time it took to answer, and the process), and the output is the collected data. Specifically, the device monitors the student's answers and records the data each time an answer is completed.

[1477] Step 2: Send data

[1478] The terminal sends the collected data to the server. The collected data is the input, and the data sent to the server is generated as the output. Specifically, the terminal encrypts the data using the HTTPS protocol and sends it to the server via the Internet.

[1479] Step 3: Save data

[1480] The server stores the received data in a database. The input is the data sent to the server, and the output is the data stored in the database. Specifically, the server uses a relational database such as MySQL to store each student's answer data in a table.

[1481] Step 4: Data analysis

[1482] The server analyzes the stored data. The input is the data stored in the database, and the output is the analyzed data. Specifically, the server reads the data using a Python data analysis library (e.g., Pandas, NumPy), completes missing values, and normalizes the data.

[1483] Step 5: Calculate the metrics

[1484] The server calculates indicators such as the correct answer rate, answer speed, and problem-solving efficiency based on the analyzed data. The analyzed data is input, and the calculated indicators are generated as output. Specifically, the server calculates each student's correct answer rate, calculates the average answer time, and calculates a score to evaluate the efficiency of problem-solving.

[1485] Step 6: Visualize the results

[1486] The server generates graphs and charts using data visualization tools (e.g., Matplotlib, Seaborn) to visualize the calculated metrics. The calculated metrics are the input, and the visualized graphs and charts are generated as the output. Specifically, the server displays each student's correct answer rate as a bar graph and their answer speed as a line graph.

[1487] Step 7: Provide feedback

[1488] Users (teachers and students) can view the visualized results through their devices. The input is the visualized graphs and charts, and the output is the feedback generated for the users to view. Specifically, the server displays the generated graphs and charts in a web interface for easy user access.

[1489] (Application example 1)

[1490] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1491] Conventional learning process evaluation systems lack the means to fairly and accurately evaluate students' learning processes, and because they rely heavily on the teacher's subjectivity, objective evaluation is difficult. Furthermore, when evaluating the work efficiency of factory robots, there is a lack of means to quantify the accuracy of work, work time, and work process, making it difficult to accurately evaluate work efficiency. To solve these issues, a system is needed that can fairly and accurately evaluate students' learning processes and the work processes of factory robots.

[1492] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1493] In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately grasping changes in thinking ability and attitude by linking situations and circumstances over time, means for converting a work process into a fair and accurate index, means for collecting the index, work accuracy, work time, and work process as data and quantifying work efficiency based on these, and means for calculating the work success rate, work speed, and work process efficiency. This makes it possible to fairly and accurately evaluate a student's learning process and the work process of a factory robot.

[1494] The "student learning process" is the process by which students acquire knowledge and skills through learning activities.

[1495] "Fair and accurate indicators" are standards that can be evaluated objectively and accurately, without relying on subjectivity.

[1496] "Children's self-assessment" refers to children themselves evaluating their own learning situation and achievements.

[1497] "Evaluation from teachers" refers to teachers' evaluation of students' learning status and achievements.

[1498] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[1499] "Transformation in thinking ability and attitude" refers to the change in students' thinking ability and attitude through learning.

[1500] A "work process" refers to the series of steps or actions that a factory robot takes to complete a specific task.

[1501] "Work accuracy" refers to the precision and accuracy with which factory robots perform their work.

[1502] "Work time" refers to the time it takes a factory robot to complete a specific task.

[1503] "Work efficiency" refers to the efficiency with which factory robots perform their work.

[1504] "Task success rate" refers to the percentage of tasks performed by a factory robot that were successful.

[1505] "Work speed" refers to the speed at which factory robots perform work.

[1506] "Collecting as data" refers to recording specific information so that it can be later used for analysis and evaluation.

[1507] "Quantification" refers to expressing the subject of evaluation in numerical or concrete indicators.

[1508] "Calculating means" refers to a method or device that performs a numerical evaluation or analysis based on specific data.

[1509] The system for implementing this invention is composed of three main elements: a server, a terminal, and a user. The specific roles and processes of each element are explained below.

[1510] server

[1511] The server plays a central role in converting the learning process of students and the work process of factory robots into fair and accurate indicators. The server has the following functions:

[1512] Data collection: Collect student answer data and factory robot work data, including correct answers, answer times, task accuracy, and task time.

[1513] Data processing: Based on the collected data, indicators such as accuracy rate, answer speed, task success rate, and task speed are calculated.

[1514] Training the AI ​​model: The generative AI model is trained using the collected data, enabling more accurate evaluation.

[1515] The hardware and software used includes Python, time libraries, and random libraries.

[1516] Terminal

[1517] A terminal is a device through which a user interacts with the system. It can be a smartphone, smart glasses, a head-mounted display, or a robot. A terminal has the following capabilities:

[1518] Data entry: Entering work data for students and factory robots, including entering answers and recording work progress.

[1519] Data display: Displays the evaluation results and indicators sent from the server, allowing users to check their own learning status and work efficiency.

[1520] User

[1521] Users are people who use the system, such as students, teachers, factory operators, etc. Users have the following capabilities:

[1522] Data provision: Input learning and work data into the terminal.

[1523] Check the evaluation: Check the evaluation results sent from the server and use them for your next study or work.

[1524] Specific examples

[1525] For example, if a factory robot performs 10 tasks and calculates the success rate, total time, and number of successful tasks, the server would do the following:

[1526] 1. Every time the robot starts a task, it records the start and end times of the task.

[1527] 2. Use sensors and cameras to determine whether the task was successful.

[1528] 3. Calculate the work hours and add them to the total work hours.

[1529] 4. Count the number of successful tasks and calculate the success rate.

[1530] Prompt Sentence Examples

[1531] An example of a prompt for a generative AI model is:

[1532] Design a system to evaluate the work efficiency of factory robots. This system will collect data on the accuracy, work time, and work process of the robot's work, and quantify the robot's work efficiency based on this data. Using Python, create a program to calculate the work success rate, work speed, and work process efficiency.

[1533] The above is an embodiment of the present invention.

[1534] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1535] Step 1:

[1536] Data collection

[1537] The server collects work data from factory robots. Specifically, it records the start and end times of each task each time the robot starts work. The inputs are the start and end signals of the task, and the output is the start and end times of each task. This data is accurately recorded using a time sensor.

[1538] Step 2:

[1539] Determining the success of the task

[1540] The server determines whether the task was successful based on the collected task data. The input is sensor data showing the progress and results of the task, and the output is a judgment result of whether the task was successful or not. This judgment is made using sensors and cameras.

[1541] Step 3:

[1542] Calculating work hours

[1543] The server calculates the working time of each task based on the start and end times. The inputs are the start and end times, and the output is the working time of each task. This calculation is performed using the time library.

[1544] Step 4:

[1545] Total work time

[1546] The server aggregates the work time for each task and calculates the total work time. The input is the work time for each task, and the output is the total work time. This aggregation is performed based on the work time data stored in the database.

[1547] Step 5:

[1548] Calculating the success rate of work

[1549] The server counts the number of successful tasks and calculates the task success rate. The input is the success determination result for each task, and the output is the task success rate. This calculation is performed by dividing the number of successful tasks by the total number of tasks.

[1550] Step 6:

[1551] Calculating work speed

[1552] The server calculates the work speed based on the work time of each task. The input is the work time of each task, and the output is the work speed. This calculation is performed using an algorithm that calculates the speed based on the work time.

[1553] Step 7:

[1554] Displaying the evaluation results

[1555] The terminal displays the evaluation results and indicators sent from the server. The input is the evaluation data sent from the server, and the output is the evaluation results that the user can check. This display is performed using a terminal such as a smartphone or head-mounted display.

[1556] The above are the specific processing steps of this program.

[1557] Example 2

[1558] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1559] Conventional learning assessment systems make it difficult to fairly and accurately evaluate students' learning processes, and lack a means for objectively and comprehensively evaluating self-assessments and teacher assessments. This makes it difficult to accurately grasp students' learning status, and novice teachers, in particular, face the challenge of creating curriculum guidelines and report cards, which are cumbersome.

[1560] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting the student's learning process into a fair and accurate index, means for objectively comprehensively evaluating the index, the student's self-assessment, and the teacher's evaluation, means for saving the evaluation data in a database, means for performing a comprehensive evaluation by combining the saved evaluation data and the learning index, means for visualizing the comprehensive evaluation result, and means for displaying the evaluation result on a terminal. This makes it possible to accurately and objectively evaluate the student's learning situation, and even a novice teacher can easily create curriculum guidelines and report cards.

[1561] "Means of converting students' learning processes into fair and accurate indicators" are means of providing standards and measures that can be used to objectively evaluate students' learning status.

[1562] "Student self-assessment" is the act of students themselves evaluating their own learning situation and level of understanding.

[1563] "Teacher evaluation" refers to the act of a teacher evaluating a student's learning situation and level of understanding.

[1564] "Means of objective comprehensive evaluation" are methods that combine students' self-evaluations with teacher evaluations to provide a comprehensive evaluation without subjective bias.

[1565] The "means for storing evaluation data in a database" refers to a means for storing collected evaluation data in a database so that it can be accessed later.

[1566] "Means for conducting comprehensive evaluation by combining stored evaluation data and learning indicators" refers to a means for comprehensively evaluating a student's learning situation using evaluation data stored in a database and pre-set learning indicators.

[1567] The "means for visualizing the overall evaluation results" is a means for displaying the results of the overall evaluation in a visual format such as a graph or chart.

[1568] The "means for displaying the evaluation result on the terminal" refers to a means for displaying the generated evaluation result on the screen of the terminal used by the user.

[1569] This invention is a system for fairly and accurately evaluating a student's learning process. This system operates in cooperation with a server, a terminal, and a user.

[1570] The server was developed using Python and MySQL for database management. The server runs a program that comprehensively evaluates students' self-assessments, teacher assessments, and learning indicators. This program performs the following processes:

[1571] 1. Data Entry:

[1572] The user (student) evaluates themselves and inputs the results into the system via a terminal. For example, a student may evaluate their math comprehension as "80 points."

[1573] The user (teacher) evaluates the student's learning status and inputs the results into the system via a terminal. For example, a teacher may evaluate "Student A's level of understanding in mathematics is 75 points."

[1574] 2. Data Retention:

[1575] The server receives the rating data sent from the devices and stores it in a MySQL database by executing SQL queries to insert the rating data into the appropriate tables.

[1576] 3. Data Processing:

[1577] The server combines the saved assessment data with predefined learning indicators to comprehensively evaluate the student's learning status. For this process, it uses the Python Pandas library. For example, it creates a data frame and calculates the average value of each assessment.

[1578] 4. Generating evaluation results:

[1579] The server generates the overall evaluation results. Specifically, it calculates the overall evaluation score based on the evaluation data and learning indicators, and uses the Matplotlib library to visualize the results, for example, by generating bar graphs or pie charts.

[1580] 5. Displaying the results:

[1581] The server sends the generated evaluation results to the terminal. Users (students and teachers) can check the results on the terminal screen. For example, the evaluation results are displayed in dashboard format.

[1582] Examples:

[1583] The user (student) enters "My level of understanding of mathematics is 80 points" into the evaluation input form on the device and presses the send button.

[1584] The user (teacher) enters "Student A's level of understanding of mathematics is 75 points" into the evaluation input form on the device and presses the send button.

[1585] The server receives this rating data and stores it in a "ratings" table in a MySQL database.

[1586] The server creates a data frame using the Pandas library and calculates the average value of the evaluation data.

[1587] The server generates the overall evaluation results as a bar graph using the Matplotlib library.

[1588] The server sends the generated bar graph to the terminal, and the users (students and teachers) can view the graph on the terminal screen.

[1589] Example prompt for a generative AI model:

[1590] "Student A's self-assessment score is 80, and Teacher B's score is 75. Based on these assessments and learning indicators, please calculate Student A's overall score."

[1591] In this way, the system can comprehensively evaluate the student's learning status.

[1592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1593] Step 1: Data entry

[1594] The user (student) evaluates themselves and inputs the results into the system via a terminal. For example, a student may evaluate their understanding of mathematics as "80 points."

[1595] The user (teacher) evaluates the student's learning status and inputs the results into the system via a terminal. For example, a teacher may evaluate "Student A's level of understanding in mathematics is 75 points."

[1596] Input: Student self-assessment data, teacher assessment data

[1597] Output: Rating data is sent from the device to the server.

[1598] Step 2: Save data

[1599] The server receives the rating data sent from the devices and stores it in a MySQL database by executing SQL queries to insert the rating data into the appropriate tables.

[1600] Input: Evaluation data sent from the device

[1601] Output: The evaluation data is saved in the database.

[1602] Step 3: Data processing

[1603] The server combines the saved assessment data with predefined learning indicators to comprehensively evaluate the student's learning status. For this process, it uses the Python Pandas library. For example, it creates a data frame and calculates the average value of each assessment.

[1604] Input: Evaluation data stored in a database, learning metrics

[1605] Output: Overall evaluation score

[1606] Step 4: Generate evaluation results

[1607] The server generates the overall evaluation results. Specifically, it calculates the overall evaluation score based on the evaluation data and learning indicators, and uses the Matplotlib library to visualize the results, for example, by generating bar graphs or pie charts.

[1608] Input: Overall rating score

[1609] Output: Visualized evaluation results (graphs and charts)

[1610] Step 5: View the results

[1611] The server sends the generated evaluation results to the terminal. Users (students and teachers) can check the results on the terminal screen. For example, the evaluation results are displayed in dashboard format.

[1612] Input: Visualized evaluation results

[1613] Output: Evaluation results displayed on the terminal

[1614] (Application example 2)

[1615] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1616] In conventional educational systems, it is difficult to fairly and accurately evaluate students' learning progress, making it difficult for inexperienced teachers to create curriculum guidelines and report cards. Furthermore, there is a lack of means to comprehensively assess students' self-assessments and teacher assessments, and appropriate learning content is often not recommended. This poses a challenge in providing optimal learning support to each student.

[1617] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting the student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the teacher's assessment, means for accurately capturing changes in thinking ability and attitude by linking situations and circumstances over time, means for recommending appropriate learning content based on the evaluation results, means for performing a comprehensive evaluation using a generative AI model, and means for generating prompt sentences and inputting the evaluation results. This makes it possible to comprehensively evaluate the student's learning situation and recommend appropriate learning content.

[1618] "Means of replacing students' learning processes with fair and accurate indicators" are methods that provide standards and measures for objectively evaluating students' learning status.

[1619] "Means for objectively and comprehensively evaluating the indicators, the student's self-assessment, and the teacher's assessment" is a method for making a comprehensive judgment by combining the student's own assessment and the teacher's assessment.

[1620] "A means of accurately capturing changes in thinking ability and attitudes by linking scenes and situations over time" is a method for accurately grasping changes in students' thinking ability and attitudes over time.

[1621] "Means for recommending appropriate learning content based on evaluation results" refers to a method for suggesting optimal learning materials and teaching materials to students based on the results of a comprehensive evaluation.

[1622] "Means for comprehensive evaluation using a generative AI model" refers to a method for comprehensively evaluating students' learning status using an artificial intelligence model.

[1623] The "means for generating a prompt sentence and inputting the evaluation result" is a method for generating an instruction sentence for inputting the evaluation result.

[1624] The system for implementing this invention is configured as follows: First, the server has a means for converting a student's learning process into a fair and accurate index. This means provides standards and measures for objectively evaluating the student's learning situation. Next, the server has a means for objectively comprehensively evaluating the index, the student's self-evaluation, and the teacher's evaluation. This means makes it possible to make a comprehensive judgment by combining the student's own evaluation and the teacher's evaluation.

[1625] Furthermore, the server has a means of accurately capturing changes in thinking ability and attitudes across situations and circumstances over time. This allows for accurate understanding of changes in students' thinking ability and attitudes over time. The server also includes a means of recommending appropriate learning content based on the evaluation results, making it possible to propose optimal learning materials and teaching materials to students based on the results of the overall evaluation.

[1626] The server has a means for performing a comprehensive evaluation using a generative AI model. This means allows a comprehensive evaluation of a student's learning status using an artificial intelligence model. The server also includes a means for generating a prompt sentence and inputting the evaluation result. This means allows an instruction sentence for inputting the evaluation result to be generated.

[1627] This system is implemented using devices such as smartphones and tablets. The devices provide an interface for inputting students' self-assessments and teachers' assessments. The server processes and calculates the data using software such as Python, NumPy, and scikit-learn. Specifically, the server inputs students' self-assessment data and teachers' assessment data as NumPy arrays and sets indicators for comprehensive assessment. A generative AI model is used to perform a comprehensive assessment, and appropriate learning content is recommended based on the results.

[1628] As a specific example, consider the case where Student A's self-assessment scores are 80, 85, and 90, and the teacher's scores are 75, 80, and 85. The indices are set to 0.5, 0.3, and 0.2. An example of a prompt that performs an overall assessment based on this assessment data and recommends appropriate learning content is as follows:

[1629] "Student A's self-assessments are 80, 85, and 90. Teacher ratings are 75, 80, and 85. Please use these ratings to give an overall rating and recommend appropriate learning content. The index values ​​are 0.5, 0.3, and 0.2."

[1630] In this way, the present invention makes it possible to comprehensively evaluate a student's learning situation and recommend appropriate learning content.

[1631] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1632] Step 1:

[1633] Users (students) use a device to input self-assessment data. Specifically, they input their self-assessment scores for each subject and skill through the interface of a smartphone or tablet. The input data is sent to the server. Examples of input data include 80 points for mathematics, 85 points for English, and 90 points for science.

[1634] Step 2:

[1635] The user (teacher) uses a terminal to input student evaluation data. The teacher observes the student's learning status and inputs evaluation scores for each subject and skill. The input data is sent to the server. Examples of input data include 75 points for mathematics, 80 points for English, and 85 points for science.

[1636] Step 3:

[1637] The server stores the received student self-assessment data and teacher assessment data as NumPy arrays. This makes it easy to process and calculate the data. Specifically, the data is stored as follows:

[1638] Student self-assessment data: [80, 85, 90]

[1639] Teacher evaluation data: [75, 80, 85]

[1640] Step 4:

[1641] The server sets an index for the overall evaluation. The index is used to weight each piece of evaluation data. Examples of indices are 0.5, 0.3, and 0.2. This determines how much each piece of evaluation data affects the overall evaluation.

[1642] Step 5:

[1643] The server performs an overall evaluation using the generative AI model. Specifically, it uses the set indicators to combine the student's self-evaluation data and the teacher's evaluation data to calculate the overall evaluation. The calculation result is output as an overall evaluation score. For example, the overall evaluation score is 82.5.

[1644] Step 6:

[1645] The server recommends appropriate learning content based on the overall evaluation score. Specifically, if the overall evaluation score is high, advanced learning materials are recommended, and if the overall evaluation score is low, basic learning materials are recommended. The recommended learning content is sent to the device. For example, if the overall evaluation score is 82.5, intermediate-level learning content is recommended.

[1646] Step 7:

[1647] The server generates prompts and provides a means for inputting the evaluation results. Specifically, based on the evaluation results, it generates prompts to be input into the generative AI model. The generated prompts are displayed on the terminal. An example of a prompt is, "Student A's self-evaluation scores are 80, 85, and 90. The teacher's ratings are 75, 80, and 85. Please conduct an overall evaluation based on these ratings and recommend appropriate learning content. The indices are 0.5, 0.3, and 0.2."

[1648] In this way, the system can comprehensively evaluate students' learning status and recommend appropriate learning content.

[1649] Example 3

[1650] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1651] Conventional learning assessment systems have difficulty accurately capturing changes in students' learning processes and thinking, making assessments subjective. Furthermore, it is difficult for novice educators to base their assessments on objective data when creating curriculum guidelines and assessment forms. Furthermore, there is a lack of a way to visually grasp changes in students' thinking, judgment, and expression abilities over time.

[1652] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for converting a student's learning process into a fair and accurate index, means for objectively and comprehensively evaluating the index, the student's self-assessment, and the educator's assessment, means for accurately capturing changes in thinking ability and attitude across situations and circumstances on a time axis, means for recording the student's answering process in real time and saving the answer input timing and change history, means for analyzing the saved data and extracting changes in thought flow and attitude, and means for visualizing the analysis results and displaying changes in thinking ability, judgment, and expression along a time axis. This allows for objective and accurate evaluation of a student's learning process, enabling educators to easily create curriculum guidelines and evaluation sheets.

[1653] "Student learning process" refers to the process by which students acquire knowledge and skills through learning activities.

[1654] "Fair and accurate indicators" refer to standards for objectively and accurately evaluating students' learning outcomes.

[1655] "Self-assessment" refers to students evaluating their own learning outcomes and progress.

[1656] "Evaluation by educators" refers to educators' evaluation of students' learning outcomes and attitudes.

[1657] "Comprehensive evaluation" refers to the integration of multiple evaluation criteria to make an overall evaluation.

[1658] "Transformation of thinking ability and attitude" refers to the change in students' thought process and learning attitude over time.

[1659] "Real-time recording" refers to instantly saving students' answers and actions as data.

[1660] "Timing of answer input" refers to the specific time when the student inputs the answer.

[1661] "Change history" refers to past records of when a student has revised their answers.

[1662] "Saved data" refers to records of students' answers and actions stored on the server.

[1663] "Analyzing data" refers to using collected data to extract specific patterns or trends.

[1664] "Changes in thought processes and attitudes" refers to showing how students' thought processes and learning attitudes have changed.

[1665] "Visualization" refers to the display of data in a visual format such as a graph or chart.

[1666] "Displaying along a time axis" refers to displaying data in order according to the progression of time.

[1667] "Thinking, judgment and expression skills" refers to students' ability to solve problems, make sound decisions and express their ideas.

[1668] This invention is a system that allows educators to fairly and accurately evaluate students' learning processes and easily create curriculum guidelines and evaluation sheets. A specific embodiment of this system is described below.

[1669] First, the device provides an interface for students to enter their answers. For example, they can use online forms such as Google Forms or Microsoft Forms. When students enter their answers, the data is sent to the server in real time. Specifically, the device displays a form like this:

[1670] Math problem: "Please solve x + 3 = 10."

[1671] Answer column: "x = [ ]"

[1672] When a student enters "x = 7" and presses the submit button, the data is sent to the server.

[1673] Next, the server saves the received answer data in a database. MySQL or PostgreSQL is used as the database. The server also saves the answer input timing and change history. For example, the server saves the answer "x = 7" at "2023-10-01 10:00:00".

[1674] The server then analyzes the saved data using Python's Pandas library. Specifically, it analyzes the change history of answers and the timing of input, and extracts changes in thought process and attitude. For example, the server sorts the data chronologically and analyzes what changes occurred and when.

[1675] Finally, the server displays the analysis results as graphs and charts using visualization libraries such as Matplotlib and Seaborn. This makes it possible to visually grasp changes in students' thinking, judgment, and expression skills over time. For example, the server can display the change history of answers as a graph, visualizing changes in thinking over time.

[1676] As a concrete example, consider the case of recording a student's process of solving a math problem. For example, if the student first enters "x = 5" and then later corrects it to "x = 7," the timing of these inputs and the history of changes are recorded and saved in a database. The data is then analyzed using Pandas and visualized as a graph using Matplotlib.

[1677] An example of a prompt sentence might be:

[1678] "Record the process by which students solve math problems and analyze changes in their thought process and attitude. Specifically, record the timing of inputting answers and the history of changes, extract the timing and type of changes in thinking, and visualize them as a graph."

[1679] In this way, the server can accurately grasp changes in the students' thinking ability, judgment ability, and expressive ability over time. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1680] Step 1: Data collection

[1681] The device provides an interface for students to input their answers, for example, using an online form. When students input their answers, the data is sent to the server in real time. The input includes the student's answer and the timing of input. The output is the answer data sent to the server.

[1682] Specifically, the terminal will display a form like this:

[1683] Math problem: "Please solve x + 3 = 10."

[1684] Answer column: "x = [ ]"

[1685] Step 2: Save data

[1686] The server stores the received answer data in a database. MySQL or PostgreSQL is used as the database. The input includes the answer data sent from the device and the input timing. The output is the answer data stored in the database.

[1687] Specifically, the server executes the following SQL query:

[1688] Insert the answer data into the database.

[1689] Step 3: Data analysis

[1690] The server analyzes the stored data using Python's Pandas library. The input includes answer data retrieved from the database and the timing of input. The output is an analysis showing changes in thought patterns and attitudes.

[1691] Specifically, the server performs the following steps:

[1692] Retrieve data from the database and convert it into a Pandas dataframe.

[1693] Analyze answer change history and input timing.

[1694] Step 4: Data visualization

[1695] The server displays the analysis results as graphs and charts using visualization libraries such as Matplotlib and Seaborn. The input includes the results of the data analysis. The output is the visualized graphs and charts.

[1696] Specifically, the server performs the following steps:

[1697] Create a graph based on the analysis results.

[1698] Generates HTML and image files to display the graph.

[1699] Step 5: Delivering results

[1700] The server sends the visualized results to the terminal for user viewing. The input includes the visualized graphs and charts. The output is displayed in a user-viewable format.

[1701] Specifically, the server performs the following steps:

[1702] Generate the visualized results as a web page.

[1703] Send a web page to the device.

[1704] In this way, the ...

Claims

1. A means for collecting data on the correctness of the questions answered by the students, the time it took to answer, and the process leading to the answer; A means for analyzing the collected data and calculating indicators including a correct answer rate, an answer speed, and an efficiency of problem solving; A means for visualizing the calculated indicators; A means for providing a visualized version of the indicator to a user; A system including:

2. The system according to claim 1, wherein the means for collecting data collects the data from a terminal that records in real time whether the answer is correct, the time it takes to answer, and the process leading to the answer when the student answers questions using a learning application.

3. The system of claim 1 further comprising means for analyzing the collected data to impute missing values ​​and normalize data.

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