system

Abstract

To provide a system that provides effective education tailored to individual learning styles, contributes to improving learners' motivation, and reduces the burden on educators. [Solution] A system including: a means for a user to input information regarding learning style and learning goals; a means for a server to analyze learning style based on the input information; a means for the server to generate customized multimedia teaching materials based on the analysis results; a means for the server to provide the generated teaching materials to the user's terminal; a means for collecting feedback from the user and reflecting it in the generation of the next teaching materials; a means for the server to record and monitor the user's learning progress and periodically provide evaluation tests; a means for the server to recommend the next learning content based on the results of the evaluation tests; and a means for the server to generate regular reports based on learning progress and results and distribute them to educators and parents.

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] Traditional education systems are difficult to customize to suit individual learners' learning styles and levels. This results in poor learning outcomes and a decline in learners' motivation. Furthermore, regular assessments and progress management by teachers and educational institutions are often carried out manually, placing a burden on them. Addressing these challenges calls for efficient and effective learning management. [Means for solving the problem]

[0005] The present invention provides effective education tailored to individual learning styles, contributing to improved learner motivation and reduced educator workloads through a system that includes: means for users to input information regarding learning styles and learning goals; means for a server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the server to provide the generated learning materials to the user's terminal; means for collecting feedback from the user and reflecting it in the generation of the next learning materials; means for the server to record and monitor the user's learning progress and periodically provide evaluation tests; means for the server to recommend the next learning content based on the results of the evaluation tests; and means for the server to generate regular reports based on the learning progress and results and distribute them to educators and parents.

[0006] "User" refers to a student, educator, or member of an educational institution who uses the System.

[0007] A "server" is a computer device that controls the entire system and performs tasks such as saving and processing data, generating teaching materials, and managing progress and evaluation.

[0008] A "terminal" is a device that a user uses to access the system, input information, and receive educational materials, and includes, for example, a PC, tablet, or smartphone.

[0009] "Learning styles" refer to the methods or formats in which learners learn most effectively, and include different learning methods such as visual, auditory, and tactile.

[0010] "Learning objectives" refer to specific knowledge or skills that learners want to achieve, including specific learning content and goals.

[0011] "Multimedia teaching materials" refers to educational materials that combine multiple media formats, such as text, images, video, and interactive content.

[0012] "Feedback" refers to information such as opinions, impressions, and evaluation of understanding provided by users regarding the learning materials they have studied.

[0013] "Progress" refers to information indicating what a user has achieved and the progress they have made in the learning process.

[0014] An "assessment test" refers to an exam or quiz that is used to check what a learner has learned and measure their level of understanding.

[0015] A "report" is a document or data summarizing a user's learning progress and achievements, and is provided to educators and parents. [Brief explanation of the drawings]

[0016] [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. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0037] This invention relates to an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, and provides these to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[0038] 1. User Registration and Login

[0039] User: A new user enters the required information into the device to register an account, including name, email address, and password, and submits it.

[0040] Server: Receives the entered information and stores it in the database. Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[0041] 2. Collecting learning style questionnaires

[0042] User: Initiates a survey on their device about their learning style and learning goals, including their preferred learning modality (visual, auditory, tactile, etc.) and the specific content they want to learn.

[0043] Server: Stores the received survey responses in a database and analyzes the user's overall learning style.

[0044] 3. Learning Style Analysis

[0045] Server: The survey results stored in the database are input into a machine learning algorithm to determine the user's learning style. Based on this determination, the type of learning material that is best suited to the user is selected.

[0046] 4. Generation of multimedia teaching materials

[0047] Server: Retrieves learning material templates from a database based on the identified learning style, including text, images, videos, and interactive quizzes.

[0048] Server: Based on the acquired templates, it generates original multimedia teaching materials tailored to the user's level and interests.

[0049] 5. Distribution of teaching materials

[0050] Server: Sends the generated learning materials to the user's device. For example, a user learning calculus can be provided with visual video learning materials and text with illustrations.

[0051] Terminal: Displays the received learning materials to the user.

[0052] 6. Collecting and analyzing feedback

[0053] Users: Enter and submit feedback on the learning materials they have studied, including an assessment of the difficulty and level of comprehension of the material.

[0054] Server: Receives and analyzes feedback and reflects it in the next generation of teaching materials.

[0055] 7. Progress Management and Evaluation

[0056] Server: Records the user's learning progress and provides periodic assessment tests as needed. For example, it generates and delivers short quizzes assessing basic concepts of calculus to the user.

[0057] Device: Receives the assessment test, presents it to the user, and sends the answers to the server, which analyzes them and recommends the next course of study.

[0058] 8. Generate and share reports

[0059] Server: Generates periodic reports based on learning progress and results, including information such as how much content the user has understood, how long it took, and what content they should learn next.

[0060] Server: Delivers the generated reports to educators and parents via email or other means.

[0061] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles, improves learners' motivation, and reduces the burden on educators.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] A user enters information such as name, email address, and password into a terminal to create a new account.

[0065] Step 2:

[0066] The terminal transmits the input information to the server.

[0067] Step 3:

[0068] The server stores the received information in a database and notifies the user that the registration was successful.

[0069] Step 4:

[0070] The user starts a survey on their device about their learning style and learning goals.

[0071] Step 5:

[0072] The terminal receives the survey questions from the server and presents them to the user.

[0073] Step 6:

[0074] The user answers the survey questions and enters the answers into the terminal.

[0075] Step 7:

[0076] The terminal transmits the user's questionnaire responses to the server.

[0077] Step 8:

[0078] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze learning styles.

[0079] Step 9:

[0080] The server uses machine learning algorithms to determine the user's learning style.

[0081] Step 10:

[0082] Based on the learning style determined by the server, an appropriate teaching material template is retrieved from the database.

[0083] Step 11:

[0084] The server generates multimedia teaching materials for each user based on the template.

[0085] Step 12:

[0086] The server transmits the generated teaching materials to the user's terminal.

[0087] Step 13:

[0088] The terminal displays the received educational material to the user.

[0089] Step 14:

[0090] Users access the learning materials and enter feedback into a terminal after each session.

[0091] Step 15:

[0092] The terminal transmits the user's feedback information to the server.

[0093] Step 16:

[0094] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0095] Step 17:

[0096] The server records the user's learning progress in a database and updates it as the learning progresses.

[0097] Step 18:

[0098] The server periodically generates assessment tests and distributes them to users.

[0099] Step 19:

[0100] The terminal presents an evaluation test to the user, who answers it.

[0101] Step 20:

[0102] The terminal transmits the user's answer results to the server.

[0103] Step 21:

[0104] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0105] Step 22:

[0106] Recommends what the server should learn next.

[0107] Step 23:

[0108] The server generates reports based on learning progress and results and distributes them to educators and parents.

[0109] That's all.

[0110] Example 1

[0111] Next, a description will be given of 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."

[0112] Conventional educational support systems do not provide learning materials that match the individual learning style of each user, making it difficult to achieve effective learning. Furthermore, the process for effectively collecting and analyzing users' learning progress and feedback and reflecting it in the next set of learning materials is insufficient, which can lead to a decline in the quality of learning. Furthermore, the provision of appropriate progress reports to educators and parents is also insufficient, making it necessary to strengthen the learning support system.

[0113] 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.

[0114] In this invention, the server includes a means for generating learning materials using templates based on the user's learning style, a means for customizing specific learning content according to the templates, and a means for analyzing user feedback through text analysis. This allows for the generation and provision of learning materials suited to the user's individual learning style, effectively incorporating feedback, and accurately managing progress. It also enables the provision of reliable progress reports to educators and parents.

[0115] "User" refers to an individual who uses the system to learn.

[0116] "Learning style" refers to the method or format in which a user learns most effectively, for example, visual, auditory, or tactile learning styles.

[0117] "Learning Objective" refers to a specific learning outcome or skill that a user wants to achieve.

[0118] "Server" refers to a computer system for receiving input information from users, processing and analyzing data, and generating and providing results.

[0119] "Information" refers to data provided by the user, specifically including learning style, learning goals, and feedback content.

[0120] A "template" is a framework that provides a basic structure and format for generating learning materials, allowing for efficient and consistent generation of learning materials.

[0121] "Multimedia learning materials" refers to learning content that combines multiple media formats, such as text, images, video, audio, and interactive quizzes.

[0122] "Feedback" refers to the opinions and ratings provided by users after using learning materials, which helps improve the quality and content of the materials.

[0123] "Text analysis" refers to the process of analyzing user feedback using natural language processing techniques to extract meaning.

[0124] "Study progress" refers to an indicator that shows how much progress a user has made as they progress through their studies. Specifically, it includes the degree of understanding and completion of the learning content.

[0125] An "assessment test" refers to a test that allows a user to check what they have learned and evaluate their level of understanding.

[0126] "Report" refers to a report summarizing a user's learning progress and results, including the user's level of understanding of the learning content, study time, next study steps, etc.

[0127] "Educator" refers to a person who supports and guides users in their learning. Specifically, this includes teachers and coaches.

[0128] "Guardian" refers to a parent or guardian who is responsible for supervising the user's learning status if the user is a minor.

[0129] MODE FOR CARRYING OUT THE INVENTION

[0130] In the educational support system of the present invention, a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, which are then provided to the user's terminal. This system can manage the user's learning progress and collect feedback to reflect in the generation of the next learning material.

[0131] User Registration and Login

[0132] User: A new user enters the required information, such as their name, email address, and password, into the terminal and submits it. For example, to create an account, Taro Tanaka enters tanaka@example.com and the password he set, and submits it.

[0133] Server: Receives the entered information and stores it in a database (specifically, MySQL (registered trademark) or similar). Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[0134] Collecting learning style questionnaires

[0135] User: Completes a questionnaire on their device about their learning style and goals. For example, the user might answer "I prefer visual learning materials" and specifically state "I want to learn the basics of calculus."

[0136] Server: Stores the received survey responses in a database and adds them to a dataset for analysis.

[0137] Learning Style Analysis

[0138] Server: Analyzes the learning style answers stored in the database using Python and machine learning libraries such as scikit-learn. Specifically, the answers are extracted as features and a clustering algorithm (e.g., K-means) is applied to determine the user's learning style.

[0139] Server: The results of the assessment are stored in a database, and this information is used to prepare data for selecting the most appropriate learning materials.

[0140] Multimedia teaching material generation

[0141] Server: Retrieves learning material templates from the database that correspond to the identified learning style. For example, selects video or infographic templates for a user who prefers "visual learning materials."

[0142] Server: Generates unique multimedia learning materials based on templates, tailored to the level and interests of specific users. For example, it creates learning materials including videos and exercises on the theme of "Fundamentals of Calculus."

[0143] Distribution of teaching materials

[0144] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (such as the URL of the video file or text file) via HTTP request.

[0145] Terminal: Analyzes the received HTTP response and creates a screen that displays links and data for the learning materials. Users can view or download the learning materials through the terminal.

[0146] Collecting and analyzing feedback

[0147] User: Enter feedback about the learning material (e.g., "The explanation in the video was easy to understand, but the questions were difficult") into the input form on the device and click the submit button.

[0148] Server: Receives feedback and stores it in a database. Analyzes the feedback data using text analysis tools (such as NLTK) to help improve the quality of the learning materials.

[0149] Progress management and evaluation

[0150] Server: Retrieves user learning progress from a database and periodically generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a multiple choice quiz to assess "Fundamental Concepts of Calculus."

[0151] Terminal: Presents the assessment test received from the server to the user, collects the user's answers, and sends them to the server.

[0152] Server: Analyzes the user's answers and generates feedback to recommend what to study next.

[0153] Generate and share reports

[0154] Server: Generates periodic reports based on learning progress and results. For example, it creates a PDF report summarizing the user's learning time, level of understanding, and what they should learn next.

[0155] Server: Delivers the generated reports to educators and parents via email (using the SMTP protocol) or other formats.

[0156] Prompt Sentence Examples

[0157] Describe the process for generating videos and text about the basics of calculus for new user Taro Tanaka, who creates an account and responds to a survey indicating that he prefers visual learning materials.

[0158] By following the above steps, the educational support system of the present invention can provide users with an optimal learning environment and maximize learning effectiveness. It also strengthens the educational support system by providing educators and parents with accurate learning progress information.

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] The flow of this system's program processing

[0161] Step 1: User Registration and Login

[0162] Input: The user enters their name, email address, and password.

[0163] Operation:

[0164] User: Enters the required information into the form on the device and clicks the submit button.

[0165] Terminal: Generates a request to send the entered data to the server.

[0166] Server: Receives the request and validates the data. If it's a new user, saves the information to the database and returns a success message. If it's an existing user, checks the authentication information and, if it matches, creates a session ID and returns a successful login message.

[0167] Output: Account creation success message for new users, login success message and session ID for existing users.

[0168] Step 2: Collect a learning style questionnaire

[0169] Input: Users fill out a questionnaire about their learning style and learning goals.

[0170] Operation:

[0171] Users: Complete and submit a survey form with information about their learning style and learning goals.

[0172] Terminal: Generates a request that collects the user's answers and sends them to the server.

[0173] Server: Receives survey responses and stores them in a database.

[0174] Output: Survey responses are saved in a database.

[0175] Step 3: Analyze your learning styles

[0176] Input: Survey results stored in a database.

[0177] Operation:

[0178] Server: Retrieves survey results from the database and analyzes them using machine learning algorithms. Specifically, it extracts learning styles as features and performs clustering (e.g., K-means).

[0179] Server: Based on the analysis results, the user's learning style is determined and the results are stored in a database.

[0180] Output: The learning style assessment results are stored in a database.

[0181] Step 4: Creating multimedia materials

[0182] Input: Learning style assessment results and template database.

[0183] Operation:

[0184] Server: Obtains learning material templates corresponding to learning styles from a template database.

[0185] Server: Based on the acquired templates, the server generates customized multimedia teaching materials tailored to the user's level and learning goals. Specifically, it uses video editing software and a text editor.

[0186] Output: Customized multimedia teaching materials.

[0187] Step 5: Distributing the materials

[0188] Input: customized multimedia teaching materials.

[0189] Operation:

[0190] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (video files and text files) via HTTP requests.

[0191] Terminal: Displays the received educational material data. Specifically, displays the educational material using a video player or text viewer.

[0192] Output: The educational material displayed on the user's device.

[0193] Step 6: Collect and analyze feedback

[0194] Input: User feedback.

[0195] Operation:

[0196] User: Enter your feedback about the learning material and click the submit button.

[0197] Terminal: Generates a request to send feedback data to the server.

[0198] Server: Receives feedback, stores it in a database, and analyzes it using natural language processing tools (e.g., NLTK).

[0199] Output: The analysis data of the feedback results is saved.

[0200] Step 7: Progress Management and Evaluation

[0201] Input: User's learning progress data.

[0202] Operation:

[0203] Server: Retrieves user learning progress data from a database and generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a test to assess "Fundamental Concepts of Calculus."

[0204] Terminal: Presents the assessment test received from the server to the user. Sends the user's answers to the server.

[0205] Server: Analyzes the user's answers and recommends the next learning content.

[0206] Output: Learning progress data and assessment results.

[0207] Step 8: Generate and share the report

[0208] Input: Learning progress data and assessment results.

[0209] Operation:

[0210] Server: Generates reports based on learning progress and results. Specifically, it creates reports in PDF format and summarizes the user's learning time and level of understanding.

[0211] Server: Generates reports and delivers them to educators and parents via email.

[0212] Output: Learning progress reports sent to educators and parents.

[0213] (Application example 1)

[0214] Next, a description will be given of Application 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."

[0215] Upgrading the skills of factory workers and training them in robot operation and maintenance is extremely important, but the lack of customized learning materials tailored to each worker's learning style and progress results in a decline in learning efficiency.In addition, there is an insufficient system for reflecting feedback on learning outcomes in the creation of next learning materials, which means that worker motivation and skill improvement are not being fully achieved.

[0216] 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.

[0217] In this invention, the server includes: means for a user to input information regarding learning styles and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the customized learning materials to be displayed in visual or multimedia format, including robot operation and maintenance training for factory workers; means for collecting feedback from the user and reflecting it in the generation of next learning materials; means for the server to record and monitor the user's learning progress and periodically provide assessment tests; means for the server to recommend next learning content based on the results of the assessment tests; and means for the server to generate regular reports based on the learning progress and results and distribute them to educators and managers. This makes it possible to improve the learning efficiency of factory workers and provide optimal training materials for each worker.

[0218] "Users" refer to factory workers who use the system to learn.

[0219] "Learning style" refers to the format or method in which a user most effectively learns, and includes different formats such as visual, auditory, and tactile.

[0220] A "learning goal" refers to a specific skill or knowledge acquisition goal that a user is trying to achieve.

[0221] "Means for inputting information" refers to the interface that allows users to provide information about their learning style and learning goals to the system.

[0222] "Server" refers to a computer system that collects information input by users, analyzes it, and generates and distributes customized educational materials.

[0223] "Multimedia teaching materials" refers to teaching materials that combine multiple media formats, such as text, video, and audio.

[0224] "Feedback" refers to the opinions, evaluations, and reports of understanding provided by users after learning.

[0225] "Assessment Test" refers to a test provided to assess a user's learning progress and level of understanding.

[0226] "Training" refers to a series of learning activities that allow a user to acquire specific skills or knowledge.

[0227] "Report" refers to a report summarizing a user's learning progress and results.

[0228] The term "administrator" refers to a person who monitors the learning status of users and provides guidance.

[0229] "Generated learning materials" refers to learning content that is customized and created by the server according to the user's learning style and goals.

[0230] "Visual format" refers to a format that uses images or pictures to present information.

[0231] "Factory workers" refer to employees who require training to operate machines and robots in factories.

[0232] This invention is an educational system for effectively supporting training in improving the skills of factory workers and robot operation. The system generates multimedia learning materials customized based on the user's learning style and learning goals, and provides them to the user's terminal.

[0233] First, users access the system using a device such as a smartphone or head-mounted display. They use an interface to input their learning style and learning goals, providing information such as whether they prefer visual or auditory learning. This allows the server to classify and analyze the user's learning style.

[0234] The server uses machine learning algorithms to analyze the user's learning style based on the input information. Specifically, it uses "scikit-learn" and "TENSORFLOW (registered trademark)" to optimally determine the user's learning style and generate learning materials accordingly. The generated learning materials include visual, auditory, and tactile multimedia materials.

[0235] The server stores the generated customized teaching materials in the cloud using AWS (registered trademark) S3 and YouTube (registered trademark) API, and distributes them to the user's device. Users can visually check the content and learn robot operation and maintenance procedures.

[0236] After studying, users send feedback to the system. This feedback includes their evaluation and opinions of the learning content. The server analyzes the feedback using natural language processing libraries such as "NLTK" and reflects the analysis results in the next generation of learning materials. This feedback allows for continuous improvement of the quality of the learning materials and the user experience.

[0237] The server also records the user's learning progress and provides regular assessment tests. The results of the assessment tests are analyzed using Jupyter Notebook and Pandas, and recommendations on what the user should learn next are provided to the user. Periodic reports are also generated and distributed to educators and administrators. This allows for effective management of worker skill development.

[0238] Examples:

[0239] For example, if a factory worker is trying to learn how to operate a new robot, the system allows him to receive visual video training on his smartphone, adapting the training to his optimal learning style and applying it to the actual task.

[0240] Example prompt sentence:

[0241] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] Users access the system using a smartphone or head-mounted display, where they input information about their learning style and goals, including learning style preferences (visual, auditory, tactile, etc.) and the specific skills or knowledge they wish to learn. The input data is then sent to the server.

[0245] Step 2:

[0246] The server receives information about the user's learning style and learning goals. The received data is fed into a machine learning algorithm using "scikit-learn" or "TensorFlow" to analyze the user's learning style. As a result of the analysis, the optimal learning material format for the user is determined.

[0247] Step 3:

[0248] The server generates customized multimedia teaching materials based on the analysis results. At this stage, depending on the specified teaching material format (e.g., materials that make extensive use of visual videos or illustrations), a teaching material template is retrieved from "AWS S3" or "YouTube API" and individual teaching materials are created based on that. The generated teaching materials are stored in the cloud.

[0249] Step 4:

[0250] The server delivers the customized learning materials to the user's device. The user uses a smartphone or head-mounted display to view and study the delivered materials. The learning materials are provided in multimedia formats such as video, text, and images, supporting both visual and tactile learning styles.

[0251] Step 5:

[0252] After completing the study, the user inputs and sends feedback to the system. The feedback includes the level of understanding of the material, the difficulty level, and areas for improvement. The input feedback is sent to the server. The server analyzes the feedback using a natural language processing library such as "NLTK" and saves the analysis results as a new dataset.

[0253] Step 6:

[0254] The server uses the user's feedback information to generate the next set of learning materials. During this process, a machine learning algorithm is used to evaluate the user's learning progress and prepare to generate new customized learning materials according to the user's next learning goal. The server also periodically records the user's learning progress and provides assessment tests. These assessment tests are also automatically generated by the server and delivered to the user's device.

[0255] Step 7:

[0256] The server receives and analyzes the results of the assessment test. Based on the assessment results, it recommends what to learn next and generates new learning materials as needed. It also generates regular reports reflecting the user's progress and distributes them to educators and managers. This allows for effective management of the skill development of factory workers.

[0257] Example prompt sentence:

[0258] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[0259] 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.

[0260] This invention combines an emotion engine with an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, which are then provided to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[0261] 1. User Registration and Login

[0262] A user enters information such as name, email address, and password into a terminal to create a new account.

[0263] The terminal transmits the input information to the server.

[0264] The server stores the received information in a database and notifies the user that the registration was successful.

[0265] 2. Collecting learning style questionnaires

[0266] The user starts a survey on their device about their learning style and learning goals.

[0267] The terminal receives the survey questions from the server and presents them to the user.

[0268] The user answers the survey questions and enters the answers into the terminal.

[0269] The terminal transmits the user's questionnaire responses to the server.

[0270] 3. Learning Style Analysis

[0271] The server stores the survey response data in a database and analyzes the user's learning style using machine learning algorithms.

[0272] 4. Utilizing the Emotion Engine

[0273] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone, using, for example, facial expression analysis and voice analysis.

[0274] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[0275] 5. Generation of multimedia teaching materials

[0276] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[0277] The server generates multimedia teaching materials for each user based on the template.

[0278] 6. Distribution of teaching materials

[0279] The server transmits the generated teaching materials to the user's terminal.

[0280] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[0281] 7. Collecting and analyzing feedback

[0282] The user inputs and sends feedback about the learning materials from the terminal.

[0283] The terminal transmits the user's feedback information to the server.

[0284] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0285] The server can also incorporate emotional states to achieve even more precise customization.

[0286] 8. Progress Management and Evaluation

[0287] The server records the user's learning progress and periodically provides assessment tests as needed, for example generating and delivering short quizzes assessing basic concepts of calculus to the user.

[0288] The terminal presents the evaluation test to the user and transmits the answer results to the server.

[0289] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0290] 9. Recommended next learning content

[0291] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will move on to the next level, and if the emotional state indicates anxiety or stress, it will recommend relaxing content.

[0292] 10. Generate and share reports

[0293] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[0294] The server delivers the generated reports to educators and parents via email or other formats.

[0295] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles and emotional states, improving learners' motivation and reducing the burden on educators.

[0296] The processing flow will be explained below.

[0297] Step 1:

[0298] A user enters information such as name, email address, and password into a terminal to create a new account.

[0299] Step 2:

[0300] The terminal transmits the input information to the server.

[0301] Step 3:

[0302] The server stores the received information in a database and notifies the user that the registration was successful.

[0303] Step 4:

[0304] The user starts a survey on their device about their learning style and learning goals.

[0305] Step 5:

[0306] The terminal receives the survey questions from the server and presents them to the user.

[0307] Step 6:

[0308] The user answers the survey questions and enters the answers into the terminal.

[0309] Step 7:

[0310] The terminal transmits the user's questionnaire responses to the server.

[0311] Step 8:

[0312] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze the user's learning style.

[0313] Step 9:

[0314] The server determines the user's learning style and stores it in a database.

[0315] Step 10:

[0316] The server uses an emotion engine to analyze data from the user's webcam and microphone and recognize their emotional state in real time.

[0317] Step 11:

[0318] The server stores the emotional state data in a database and reflects it in further customizing learning styles.

[0319] Step 12:

[0320] Based on the learning style and emotional state determined by the server, an appropriate learning material template is retrieved from a database.

[0321] Step 13:

[0322] The server generates multimedia teaching materials for each user based on the template.

[0323] Step 14:

[0324] The server transmits the generated teaching materials to the user's terminal.

[0325] Step 15:

[0326] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[0327] Step 16:

[0328] Users access the learning materials and enter feedback into a terminal after each session.

[0329] Step 17:

[0330] The terminal transmits the user's feedback information to the server.

[0331] Step 18:

[0332] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0333] Step 19:

[0334] The server records the user's learning progress in a database and monitors the progress in real time.

[0335] Step 20:

[0336] The server periodically generates assessment tests and distributes them to users.

[0337] Step 21:

[0338] The terminal presents an evaluation test to the user, who answers it.

[0339] Step 22:

[0340] The terminal transmits the user's answer results to the server.

[0341] Step 23:

[0342] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0343] Step 24:

[0344] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[0345] Step 25:

[0346] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[0347] Step 26:

[0348] The server delivers the generated reports to educators and parents via email or other formats.

[0349] That's all.

[0350] Example 2

[0351] Next, a description will be given of 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."

[0352] Conventional educational support systems are not sufficiently customized based on the user's learning style and goals, and are unable to reflect the user's emotional state in real time while learning. As a result, learning efficiency declines and it becomes difficult to maintain user motivation.

[0353] 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.

[0354] In this invention, the server includes means for a user to input information about learning style and learning goals, means for the server to analyze the learning style based on the input information, means for the server to generate customized multimedia learning materials based on the analysis results, means for the server to analyze the user's emotional state using an emotion engine and reflect the results in further customization of the learning style, and means for the server to adjust the learning pace and learning material content based on the user's emotional state, thereby making it possible to provide a highly customized learning experience based on the user's learning style and real-time emotional state.

[0355] A "user" is a person who uses the system, who inputs information about their learning style and learning goals, and who receives learning materials.

[0356] A "terminal" is an electronic device operated by a user, used to input information, display educational materials, etc. Examples include PCs, smartphones, and tablets.

[0357] A "server" is a computer system that receives data sent by users, processes and analyzes it, and generates and distributes appropriate educational materials.

[0358] "Means for inputting information" refers to the interface or method by which users input information about their learning styles and learning goals into the system. Examples include web forms and questionnaire-style input screens.

[0359] "Means for analyzing learning styles" refers to the process by which the server uses machine learning algorithms and statistical analysis to identify the optimal learning method based on the information the user has entered.

[0360] "Multimedia learning materials" are learning resources that combine multiple media formats, such as text, images, audio, and video.

[0361] An "emotion engine" is software or an algorithm for analyzing a user's emotional state, recognizing the user's emotions in real time through facial expression analysis, voice analysis, etc.

[0362] "Means for adjusting learning pace and learning material content" refers to a function that flexibly changes the learning content and its progress speed based on the user's emotional state and learning style. For example, it includes slowing down the pace for users who are under stress.

[0363] "Means for collecting feedback" are methods by which users can input their opinions about their learning experience and learning materials into the system. Examples include survey forms and rating systems.

[0364] "Means for recording learning progress" refers to a method for tracking a user's learning activities and accumulating data on their progress. For example, a learning management system (LMS) would be an example of this.

[0365] A "means for providing assessment tests" is a method for generating and delivering tests to users to assess their understanding or progress. Examples include online quizzes and exams.

[0366] "Means for recommending learning content" is a process that selects and suggests what the user should learn next based on the results of an assessment test and their emotional state.

[0367] A "means for generating a report" is a method for organizing a user's learning progress and achievements and creating a report in a visual format (e.g., PDF).

[0368] "Means of delivery to educators and parents" refers to the methods by which the generated reports are delivered to others involved in education, primarily via email or online portals.

[0369] This is an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, and provides them to the user's terminal. Furthermore, an emotion engine is used to analyze the user's emotional state in real time, dynamically customizing the learning experience.

[0370] User Registration and Login

[0371] To create a new account, a user enters information such as name, email address, and password into the device. The device sends the information to the server, which stores it in a database. The user then receives a success notification.

[0372] Collecting learning style questionnaires

[0373] The user starts a questionnaire about their learning style and learning goals on their device. The device receives the questionnaire questions from the server and presents them to the user. The user answers the questionnaire, and the answers are sent from the device to the server.

[0374] Learning Style Analysis

[0375] The server stores the survey responses in a database, then uses machine learning algorithms to analyze the user's learning style, for example, by using Scikit-learn to perform clustering and identify the user's learning style.

[0376] Utilizing the Emotion Engine

[0377] The server uses the user's webcam and microphone to recognize the user's emotional state during learning. For example, it performs facial expression and voice analysis using OpenCV. The server analyzes the emotional state data and reflects it in further customizing the learning style.

[0378] Multimedia teaching material generation

[0379] The server retrieves appropriate learning material templates from a database based on the identified learning style and emotional state, and then generates personalized multimedia learning materials using Adobe Creative Suite and HTML5.

[0380] Distribution of teaching materials

[0381] The generated learning materials are sent from the server to the user's device, which then displays them to the user. For example, the device can provide infographic-based learning materials to users who prefer visual learning, and change the pace if the emotion engine detects stress.

[0382] Collecting and analyzing feedback

[0383] Users input and submit feedback about learning materials from their devices. The devices then send the feedback information to the server, which stores it in a database. The analyzed feedback information is reflected in the next generation of learning materials.

[0384] Progress management and evaluation

[0385] The server records the user's learning progress and provides periodic assessment tests as needed. The device presents the assessment tests to the user and sends the results to the server. The server analyzes the results and determines the user's level of understanding.

[0386] Recommended next learning content

[0387] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will suggest moving on to the next level, and if the emotional state indicates anxiety or stress, it will suggest relaxing content.

[0388] Generate and share reports

[0389] The server periodically generates reports based on learning progress and results, including information on how much content the user understood, how long it took, what content they should learn next, how their emotional state affected the learning, etc. The generated reports are sent to educators and parents via email or other means.

[0390] Examples of concrete examples and prompts

[0391] For example, if a busy working adult learner sets the goal of "studying efficiently after work," the system can provide appropriate infographics or short video materials while monitoring the learner's emotional state in real time. If the emotion engine detects fatigue or stress, it will display relaxing content or inspirational messages.

[0392] Example prompt sentence:

[0393] "Provide instructional materials with easy-to-understand visual illustrations. Adjust your pace to slow down if you experience fatigue."

[0394] In this way, a highly customized learning experience is provided that is tailored to individual learning styles and emotional states.

[0395] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0396] Step 1:

[0397] Entering user information

[0398] A user enters information such as name, email address, and password into a terminal to create a new account.

[0399] Input: Name, Email Address, Password

[0400] Output: Data entered into the input form on the terminal

[0401] What happens: A user fills out a web form and clicks the "Submit" button.

[0402] Step 2:

[0403] Sending input information

[0404] The terminal transmits the input information to the server.

[0405] Input: User information entered into the terminal

[0406] Output: User information sent to the server

[0407] Specific operation: The device sends an HTTP request to the server using the POST method.

[0408] Step 3:

[0409] Saving user information

[0410] The server stores the received information in a database and notifies the user that the registration was successful.

[0411] Input: HTTP request containing user information

[0412] Output: User information stored in the database

[0413] Specific operation: The server inserts the user information into a database (e.g., MySQL) and returns a success response.

[0414] Step 4:

[0415] Survey begins

[0416] The user starts a survey on their device about their learning style and learning goals.

[0417] Input: User requests to start a survey

[0418] Output: Survey start request to the server

[0419] Specific behavior: The user clicks the "Start Survey" button.

[0420] Step 5:

[0421] Presenting the survey questions

[0422] The terminal receives the survey questions from the server and presents them to the user.

[0423] Input: Survey questions sent from the server

[0424] Output: The survey question that is displayed to the user

[0425] Specific operation: The device sends an HTTP request to the server, receives survey questions in JSON format, and displays the questions in an HTML form.

[0426] Step 6:

[0427] Entering survey answers

[0428] The user answers the survey questions and enters the answers into the terminal.

[0429] Input: User survey responses

[0430] Output: Survey responses entered into the device

[0431] Specific behavior: A user enters answers into a web form and clicks the "Submit" button.

[0432] Step 7:

[0433] Submit your answer

[0434] The terminal transmits the user's questionnaire responses to the server.

[0435] Input: Survey answers entered into the device

[0436] Output: Survey responses sent to the server

[0437] Specific operation: The device sends an HTTP request to the server using the POST method.

[0438] Step 8:

[0439] Saving response data

[0440] The server stores the survey response data in a database.

[0441] Input: Survey responses sent to the server

[0442] Output: Survey responses stored in a database

[0443] Specific operation: The server inserts the response data into the database.

[0444] Step 9:

[0445] Learning Style Analysis

[0446] The server uses machine learning algorithms to analyze the user's learning style.

[0447] Input: Survey responses stored in the database

[0448] Output: Analyzed learning styles

[0449] What it does: The server uses Scikit-learn to perform clustering and regression analysis, for example, using K-means clustering to find clusters of learning styles.

[0450] Step 10:

[0451] Acquiring emotion data

[0452] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone.

[0453] Input: User camera video and audio data

[0454] Output: Parsed emotional state data

[0455] Specific operation: The server acquires camera images using WebRTC and performs facial expression and voice analysis using OpenCV.

[0456] Step 11:

[0457] Emotional Data Analysis

[0458] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[0459] Input: Parsed emotional state data

[0460] Output: Customized learning style data

[0461] Specific operation: The server updates the learning style data in the database based on the analysis results.

[0462] Step 12:

[0463] Obtaining teaching material templates

[0464] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[0465] Input: Learning style data, emotional state data

[0466] Output: Teaching material template data

[0467] Specific operation: The server uses an SQL query to select the teaching material template.

[0468] Step 13:

[0469] Customizing teaching materials

[0470] The server generates multimedia teaching materials for each user based on the template.

[0471] Input: Teaching material template data, user's learning style and emotional state

[0472] Output: Customized multimedia teaching materials

[0473] Specific operation: The server creates teaching materials based on templates and visually customizes them using HTML5 and CSS.

[0474] Step 14:

[0475] Sending educational materials

[0476] The server transmits the generated teaching materials to the user's terminal.

[0477] Input: Customized multimedia materials

[0478] Output: The learning materials delivered to the user's device

[0479] Specific operation: The server returns the learning material data (e.g., PDF or HTML) in an HTTP response.

[0480] Step 15:

[0481] Display of teaching materials

[0482] The terminal displays the received educational material to the user.

[0483] Input: Delivered multimedia materials

[0484] Output: The material displayed to the user

[0485] Specific operation: The device displays the contents of the teaching material in a browser.

[0486] Step 16:

[0487] Enter your feedback

[0488] The user inputs and sends feedback about the learning materials from the terminal.

[0489] Input: Feedback

[0490] Output: HTTP request with feedback data

[0491] What happens: A user fills out a feedback form and clicks the "Submit" button.

[0492] Step 17:

[0493] Send Feedback

[0494] The terminal transmits the user's feedback information to the server.

[0495] Input: User feedback

[0496] Output: Feedback information sent to the server

[0497] Specific operation: The device posts feedback data via an HTTP request.

[0498] Step 18:

[0499] Feedback storage and analysis

[0500] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0501] Input: Feedback information sent to the server

[0502] Output: Parsed feedback information

[0503] Specific operation: The server stores the feedback data in a database and uses an analysis algorithm to extract areas for improvement.

[0504] Step 19:

[0505] Record your learning progress

[0506] The server records the user's learning progress.

[0507] Input: User's learning progress data

[0508] Output: Learning progress data stored in a database

[0509] Specific operation: The server inserts or updates the learning progress data into the database.

[0510] Step 20:

[0511] Providing assessment tests

[0512] The server provides evaluation tests periodically as needed.

[0513] Input: Triggers the server's assessment test delivery routine

[0514] Output: Assessment test provided to the user

[0515] Specific behavior: The server sends the assessment test to the user, and the device displays the test.

[0516] Step 21:

[0517] Analyzing test results

[0518] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0519] Input: User evaluation test result data

[0520] Output: Parsed test results

[0521] What happens: The server analyzes the test result data and runs an algorithm to assess the user's level of understanding.

[0522] Step 22:

[0523] Determining the recommendations

[0524] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[0525] Input: Parsed test results, emotional state data

[0526] Output: Suggested next steps

[0527] Specific operation: The server runs an algorithm to recommend the next learning content based on the analysis results and notifies the user.

[0528] Step 23:

[0529] Generate reports

[0530] The server periodically generates reports based on learning progress and results.

[0531] Input: User learning progress data, achievement data

[0532] Output: Generated report

[0533] Specific operation: The server creates a PDF report using a report generation tool such as Python.

[0534] Step 24:

[0535] Report distribution

[0536] The server delivers the generated reports to educators and parents via email or other formats.

[0537] Input: Generated report

[0538] Output: Reports delivered to educators and parents

[0539] What happens: The server sends the email using the SMTP protocol.

[0540] (Application example 2)

[0541] Next, a description will be given of Application 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."

[0542] In modern factory training support systems, it is extremely important to provide customized instruction that takes into account the learning style and emotional state of each worker and adapts to their individual needs. However, conventional systems have difficulty capturing the learner's real-time emotional state, making it difficult to effectively manage learning progress and provide appropriate learning materials. This has resulted in poor improvement in workers' learning efficiency and motivation, and has placed a heavy burden on educators. To solve this problem, a system that can generate and deliver customized learning materials based on learning style and emotional state is needed.

[0543] 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.

[0544] In this invention, the server includes: means for a user to input information regarding learning style and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the server to provide the generated learning materials to the user's terminal; means for recognizing and analyzing the user's emotional state in real time; means for generating customized teaching materials based on the emotional state; means for the server to record and monitor the user's learning progress and periodically provide evaluation tests; means for the server to recommend next learning content based on the results of the evaluation tests; means for the server to generate periodic reports based on the learning progress and results and distribute them to educators and parents; means for reflecting the feedback information in the generation of next learning materials; and means for providing learning materials based on the user's working environment and learning style. This improves the learning efficiency and motivation of workers and reduces the burden on educators.

[0545] A "user" is an individual who uses the educational support system by inputting information about their learning style and learning goals.

[0546] The "server" is a central computer system that handles a series of processes such as analyzing learning styles, generating customized multimedia learning materials, recording learning progress, and providing assessment tests.

[0547] "Learning style" refers to individual characteristics such as a user's preferred learning method or medium, including visual, auditory, and tactile learning.

[0548] A "learning goal" is a specific learning outcome or skill that a user is trying to achieve.

[0549] "Teaching materials" refers to learning materials and content provided to assist users in their learning.

[0550] "Emotional state" refers to the user's current mental and emotional state, including stress, concentration, excitement, etc.

[0551] "Customized instructional materials" refers to learning materials that are individually tailored based on a user's learning style and emotional state.

[0552] "Feedback information" refers to opinions and impressions regarding learning collected from users.

[0553] An "assessment test" is a test provided by the server to measure the user's level of understanding of the learning content.

[0554] A "report" is a detailed report summarizing learning progress and achievements that is distributed to educators and parents.

[0555] The system for realizing this invention mainly consists of a server, a user terminal, and various input devices. The system aims to provide customized instruction based on learning style and emotional state.

[0556] First, the user inputs information about their learning style and learning goals into a device, such as a tablet or smartphone. This input information is sent to a server, which processes the information using a cloud-based computing system (e.g., AWS or Google® Cloud).

[0557] The server analyzes the learning style based on the information received from the user. To do this, it uses a machine learning algorithm (e.g., TensorFlow or PyTorch) to store the user's questionnaire response data in a database (e.g., MySQL or PostgreSQL) and analyzes it. It also uses data from cameras and microphones installed on the device or robot to recognize the learner's emotional state in real time. It uses an emotion analysis engine (e.g., Microsoft® Azure® Emotion API) to identify the learner's emotional state from facial expressions and voice.

[0558] The server selects appropriate learning material templates based on the learning style and emotional state data, and generates customized teaching materials. The materials are customized to provide visual content for users who prefer visual learning and audio content for users who prefer auditory learning. The generated teaching materials are quickly delivered to the user's device.

[0559] The server also collects feedback information from users and reflects it in the generation of the next learning material. Feedback is collected and entered from the device and sent to the server. The server analyzes the feedback data and reflects it in the next learning content, providing a more effective learning experience.

[0560] The server also records the user's learning progress and provides assessment tests, which evaluate the learner's level of understanding and recommend what they should study next. Periodically generated reports clearly show the learner's progress and results and are distributed to educators and parents, making it possible to keep track of the learner's learning situation at all times.

[0561] For example, enter data based on the following prompt:

[0562] Username: Suzuki Taro

[0563] Learning objective: Learn how to operate a new machine tool

[0564] Emotional state: High stress level, low concentration

[0565] Feedback: I find the content of the instruction difficult.

[0566] This system improves workers' learning efficiency and motivation, and reduces the burden on instructors.

[0567] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0568] Step 1:

[0569] Users enter information about their learning style and learning goals.

[0570] Users use a tablet or smartphone to answer a questionnaire about their learning style and learning goals, and enter the necessary information, including their name, email address, learning goals, etc. This information is first saved on the device.

[0571] Input: Learning style, learning goals, personal information

[0572] Output: Input information saved on the device

[0573] Step 2:

[0574] The terminal transmits the input information to the server.

[0575] The device sends the stored information to the server using a communication protocol (e.g., REST API).

[0576] Input: Input information saved on the device

[0577] Output: Learning style and learning goal information sent to the server

[0578] Step 3:

[0579] The server analyzes learning styles based on the input information.

[0580] The server stores the received information in a database (e.g., MySQL or PostgreSQL) and analyzes learning styles using machine learning algorithms (e.g., TensorFlow or PyTorch).

[0581] Input: Learning style and learning goal information sent to the server

[0582] Output: Learning style analysis results

[0583] Step 4:

[0584] The server recognizes and analyzes the user's emotional state in real time.

[0585] The server receives data from the camera and microphone installed on the device or robot, and uses an emotion analysis engine (such as Microsoft Azure Emotion API) to analyze the user's facial expressions and voice to identify emotional state data, such as stress level and concentration.

[0586] Input: Real-time camera and microphone data from devices and robots

[0587] Output: Emotional state data

[0588] Step 5:

[0589] The server generates customized instructional materials based on learning style and emotional state.

[0590] The server retrieves appropriate teaching material templates from a database based on the results of the learning style analysis and emotional state data, and generates customized teaching materials.

[0591] Input: Learning style analysis results, emotional state data

[0592] Output: Customized teaching materials

[0593] Step 6:

[0594] The server provides the generated educational materials to the user's terminal.

[0595] The server sends the generated customized training materials to the user's device using a communication protocol (e.g., REST API).

[0596] Input: Customized instructional materials

[0597] Output: Instructional materials provided to the device

[0598] Step 7:

[0599] The user enters and submits feedback about the learning materials.

[0600] Users can input feedback about the learning materials through their terminals and send it to the server. The feedback includes the difficulty level of the materials, the level of understanding, and requests for improvement.

[0601] Input: Feedback on the learning materials

[0602] Output: Feedback information sent to the server

[0603] Step 8:

[0604] The server analyzes the feedback information and reflects it in the next generation of teaching materials.

[0605] The server stores the feedback information in a database and analyzes it, so that the feedback is reflected in the next teaching material provided.

[0606] Input: Feedback information

[0607] Output: Data for next teaching material generation that reflects the feedback

[0608] Step 9:

[0609] The server records and monitors the user's learning progress and periodically provides assessment tests.

[0610] The server records the user's learning progress in a database, generates assessment tests at regular intervals, and provides them to the terminal. The results of the assessment tests are analyzed to grasp the user's learning progress.

[0611] Input: Learning progress, assessment test

[0612] Output: Evaluation test results, learning progress data

[0613] Step 10:

[0614] The server recommends the next learning content based on the results of the evaluation test.

[0615] The server recommends what to learn next based on the results of the evaluation test and emotional state data, and the recommendations are provided to the user's device.

[0616] Input: Evaluation test results, emotional state data

[0617] Output: Recommended learning content

[0618] Step 11:

[0619] The server generates regular reports based on learning progress and results and distributes them to educators and parents.

[0620] The server extracts learning progress and results from the database and periodically generates reports, which are then sent to educators and parents via email or other means.

[0621] Input: Learning progress and achievement data

[0622] Output: Regular reports delivered to educators and parents

[0623] For example, enter data based on the following prompt:

[0624] Username: Suzuki Taro

[0625] Learning objective: Learn how to operate a new machine tool

[0626] Emotional state: High stress level, low concentration

[0627] Feedback: I find the content of the instruction difficult.

[0628] 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.

[0629] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0630] 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.

[0631] [Second embodiment]

[0632] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0644] This invention relates to an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, and provides these to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[0645] 1. User Registration and Login

[0646] User: A new user enters the required information into the device to register an account, including name, email address, and password, and submits it.

[0647] Server: Receives the entered information and stores it in the database. Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[0648] 2. Collecting learning style questionnaires

[0649] User: Initiates a survey on their device about their learning style and learning goals, including their preferred learning modality (visual, auditory, tactile, etc.) and the specific content they want to learn.

[0650] Server: Stores the received survey responses in a database and analyzes the user's overall learning style.

[0651] 3. Learning Style Analysis

[0652] Server: The survey results stored in the database are input into a machine learning algorithm to determine the user's learning style. Based on this determination, the type of learning material that is best suited to the user is selected.

[0653] 4. Generation of multimedia teaching materials

[0654] Server: Retrieves learning material templates from a database based on the identified learning style, including text, images, videos, and interactive quizzes.

[0655] Server: Based on the acquired templates, it generates original multimedia teaching materials tailored to the user's level and interests.

[0656] 5. Distribution of teaching materials

[0657] Server: Sends the generated learning materials to the user's device. For example, a user learning calculus can be provided with visual video learning materials and text with illustrations.

[0658] Terminal: Displays the received learning materials to the user.

[0659] 6. Collecting and analyzing feedback

[0660] Users: Enter and submit feedback on the learning materials they have studied, including an assessment of the difficulty and level of comprehension of the material.

[0661] Server: Receives and analyzes feedback and reflects it in the next generation of teaching materials.

[0662] 7. Progress Management and Evaluation

[0663] Server: Records the user's learning progress and provides periodic assessment tests as needed. For example, it generates and delivers short quizzes assessing basic concepts of calculus to the user.

[0664] Device: Receives the assessment test, presents it to the user, and sends the answers to the server, which analyzes them and recommends the next course of study.

[0665] 8. Generate and share reports

[0666] Server: Generates periodic reports based on learning progress and results, including information such as how much content the user has understood, how long it took, and what content they should learn next.

[0667] Server: Delivers the generated reports to educators and parents via email or other means.

[0668] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles, improves learners' motivation, and reduces the burden on educators.

[0669] The processing flow will be explained below.

[0670] Step 1:

[0671] A user enters information such as name, email address, and password into a terminal to create a new account.

[0672] Step 2:

[0673] The terminal transmits the input information to the server.

[0674] Step 3:

[0675] The server stores the received information in a database and notifies the user that the registration was successful.

[0676] Step 4:

[0677] The user starts a survey on their device about their learning style and learning goals.

[0678] Step 5:

[0679] The terminal receives the survey questions from the server and presents them to the user.

[0680] Step 6:

[0681] The user answers the survey questions and enters the answers into the terminal.

[0682] Step 7:

[0683] The terminal transmits the user's questionnaire responses to the server.

[0684] Step 8:

[0685] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze learning styles.

[0686] Step 9:

[0687] The server uses machine learning algorithms to determine the user's learning style.

[0688] Step 10:

[0689] Based on the learning style determined by the server, an appropriate teaching material template is retrieved from the database.

[0690] Step 11:

[0691] The server generates multimedia teaching materials for each user based on the template.

[0692] Step 12:

[0693] The server transmits the generated teaching materials to the user's terminal.

[0694] Step 13:

[0695] The terminal displays the received educational material to the user.

[0696] Step 14:

[0697] Users access the learning materials and enter feedback into a terminal after each session.

[0698] Step 15:

[0699] The terminal transmits the user's feedback information to the server.

[0700] Step 16:

[0701] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0702] Step 17:

[0703] The server records the user's learning progress in a database and updates it as the learning progresses.

[0704] Step 18:

[0705] The server periodically generates assessment tests and distributes them to users.

[0706] Step 19:

[0707] The terminal presents an evaluation test to the user, who answers it.

[0708] Step 20:

[0709] The terminal transmits the user's answer results to the server.

[0710] Step 21:

[0711] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0712] Step 22:

[0713] Recommends what the server should learn next.

[0714] Step 23:

[0715] The server generates reports based on learning progress and results and distributes them to educators and parents.

[0716] That's all.

[0717] Example 1

[0718] Next, a description will be given of 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."

[0719] Conventional educational support systems do not provide learning materials that match the individual learning style of each user, making it difficult to achieve effective learning. Furthermore, the process for effectively collecting and analyzing users' learning progress and feedback and reflecting it in the next set of learning materials is insufficient, which can lead to a decline in the quality of learning. Furthermore, the provision of appropriate progress reports to educators and parents is also insufficient, making it necessary to strengthen the learning support system.

[0720] 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.

[0721] In this invention, the server includes a means for generating learning materials using templates based on the user's learning style, a means for customizing specific learning content according to the templates, and a means for analyzing user feedback through text analysis. This allows for the generation and provision of learning materials suited to the user's individual learning style, effectively incorporating feedback, and accurately managing progress. It also enables the provision of reliable progress reports to educators and parents.

[0722] "User" refers to an individual who uses the system to learn.

[0723] "Learning style" refers to the method or format in which a user learns most effectively, for example, visual, auditory, or tactile learning styles.

[0724] "Learning Objective" refers to a specific learning outcome or skill that a user wants to achieve.

[0725] "Server" refers to a computer system for receiving input information from users, processing and analyzing data, and generating and providing results.

[0726] "Information" refers to data provided by the user, specifically including learning style, learning goals, and feedback content.

[0727] A "template" is a framework that provides a basic structure and format for generating learning materials, allowing for efficient and consistent generation of learning materials.

[0728] "Multimedia learning materials" refers to learning content that combines multiple media formats, such as text, images, video, audio, and interactive quizzes.

[0729] "Feedback" refers to the opinions and ratings provided by users after using learning materials, which helps improve the quality and content of the materials.

[0730] "Text analysis" refers to the process of analyzing user feedback using natural language processing techniques to extract meaning.

[0731] "Study progress" refers to an indicator that shows how much progress a user has made as they progress through their studies. Specifically, it includes the degree of understanding and completion of the learning content.

[0732] An "assessment test" refers to a test that allows a user to check what they have learned and evaluate their level of understanding.

[0733] "Report" refers to a report summarizing a user's learning progress and results, including the user's level of understanding of the learning content, study time, next study steps, etc.

[0734] "Educator" refers to a person who supports and guides users in their learning. Specifically, this includes teachers and coaches.

[0735] "Guardian" refers to a parent or guardian who is responsible for supervising the user's learning status if the user is a minor.

[0736] MODE FOR CARRYING OUT THE INVENTION

[0737] In the educational support system of the present invention, a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, which are then provided to the user's terminal. This system can manage the user's learning progress and collect feedback to reflect in the generation of the next learning material.

[0738] User Registration and Login

[0739] User: A new user enters the required information, such as their name, email address, and password, into the terminal and submits it. For example, to create an account, Taro Tanaka enters tanaka@example.com and the password he set, and submits it.

[0740] Server: Receives the entered information and stores it in a database (specifically, MySQL, etc.). Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[0741] Collecting learning style questionnaires

[0742] User: Completes a questionnaire on their device about their learning style and goals. For example, the user might answer "I prefer visual learning materials" and specifically state "I want to learn the basics of calculus."

[0743] Server: Stores the received survey responses in a database and adds them to a dataset for analysis.

[0744] Learning Style Analysis

[0745] Server: Analyzes the learning style answers stored in the database using Python and machine learning libraries such as scikit-learn. Specifically, the answers are extracted as features and a clustering algorithm (e.g., K-means) is applied to determine the user's learning style.

[0746] Server: The results of the assessment are stored in a database, and this information is used to prepare data for selecting the most appropriate learning materials.

[0747] Multimedia teaching material generation

[0748] Server: Retrieves learning material templates from the database that correspond to the identified learning style. For example, selects video or infographic templates for a user who prefers "visual learning materials."

[0749] Server: Generates unique multimedia learning materials based on templates, tailored to the level and interests of specific users. For example, it creates learning materials including videos and exercises on the theme of "Fundamentals of Calculus."

[0750] Distribution of teaching materials

[0751] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (such as the URL of the video file or text file) via HTTP request.

[0752] Terminal: Analyzes the received HTTP response and creates a screen that displays links and data for the learning materials. Users can view or download the learning materials through the terminal.

[0753] Collecting and analyzing feedback

[0754] User: Enter feedback about the learning material (e.g., "The explanation in the video was easy to understand, but the questions were difficult") into the input form on the device and click the submit button.

[0755] Server: Receives feedback and stores it in a database. Analyzes the feedback data using text analysis tools (such as NLTK) to help improve the quality of the learning materials.

[0756] Progress management and evaluation

[0757] Server: Retrieves user learning progress from a database and periodically generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a multiple choice quiz to assess "Fundamental Concepts of Calculus."

[0758] Terminal: Presents the assessment test received from the server to the user, collects the user's answers, and sends them to the server.

[0759] Server: Analyzes the user's answers and generates feedback to recommend what to study next.

[0760] Generate and share reports

[0761] Server: Generates periodic reports based on learning progress and results. For example, it creates a PDF report summarizing the user's learning time, level of understanding, and what they should learn next.

[0762] Server: Delivers the generated reports to educators and parents via email (using the SMTP protocol) or other formats.

[0763] Prompt Sentence Examples

[0764] Describe the process for generating videos and text about the basics of calculus for new user Taro Tanaka, who creates an account and responds to a survey indicating that he prefers visual learning materials.

[0765] By following the above steps, the educational support system of the present invention can provide users with an optimal learning environment and maximize learning effectiveness. It also strengthens the educational support system by providing educators and parents with accurate learning progress information.

[0766] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0767] The flow of this system's program processing

[0768] Step 1: User Registration and Login

[0769] Input: The user enters their name, email address, and password.

[0770] Operation:

[0771] User: Enters the required information into the form on the device and clicks the submit button.

[0772] Terminal: Generates a request to send the entered data to the server.

[0773] Server: Receives the request and validates the data. If it's a new user, saves the information to the database and returns a success message. If it's an existing user, checks the authentication information and, if it matches, creates a session ID and returns a successful login message.

[0774] Output: Account creation success message for new users, login success message and session ID for existing users.

[0775] Step 2: Collect a learning style questionnaire

[0776] Input: Users fill out a questionnaire about their learning style and learning goals.

[0777] Operation:

[0778] Users: Complete and submit a survey form with information about their learning style and learning goals.

[0779] Terminal: Generates a request that collects the user's answers and sends them to the server.

[0780] Server: Receives survey responses and stores them in a database.

[0781] Output: Survey responses are saved in a database.

[0782] Step 3: Analyze your learning styles

[0783] Input: Survey results stored in a database.

[0784] Operation:

[0785] Server: Retrieves survey results from the database and analyzes them using machine learning algorithms. Specifically, it extracts learning styles as features and performs clustering (e.g., K-means).

[0786] Server: Based on the analysis results, the user's learning style is determined and the results are stored in a database.

[0787] Output: The learning style assessment results are stored in a database.

[0788] Step 4: Creating multimedia materials

[0789] Input: Learning style assessment results and template database.

[0790] Operation:

[0791] Server: Obtains learning material templates corresponding to learning styles from a template database.

[0792] Server: Based on the acquired templates, the server generates customized multimedia teaching materials tailored to the user's level and learning goals. Specifically, it uses video editing software and a text editor.

[0793] Output: Customized multimedia teaching materials.

[0794] Step 5: Distributing the materials

[0795] Input: customized multimedia teaching materials.

[0796] Operation:

[0797] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (video files and text files) via HTTP requests.

[0798] Terminal: Displays the received educational material data. Specifically, displays the educational material using a video player or text viewer.

[0799] Output: The educational material displayed on the user's device.

[0800] Step 6: Collect and analyze feedback

[0801] Input: User feedback.

[0802] Operation:

[0803] User: Enter your feedback about the learning material and click the submit button.

[0804] Terminal: Generates a request to send feedback data to the server.

[0805] Server: Receives feedback, stores it in a database, and analyzes it using natural language processing tools (e.g., NLTK).

[0806] Output: The analysis data of the feedback results is saved.

[0807] Step 7: Progress Management and Evaluation

[0808] Input: User's learning progress data.

[0809] Operation:

[0810] Server: Retrieves user learning progress data from a database and generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a test to assess "Fundamental Concepts of Calculus."

[0811] Terminal: Presents the assessment test received from the server to the user. Sends the user's answers to the server.

[0812] Server: Analyzes the user's answers and recommends the next learning content.

[0813] Output: Learning progress data and assessment results.

[0814] Step 8: Generate and share the report

[0815] Input: Learning progress data and assessment results.

[0816] Operation:

[0817] Server: Generates reports based on learning progress and results. Specifically, it creates reports in PDF format and summarizes the user's learning time and level of understanding.

[0818] Server: Generates reports and delivers them to educators and parents via email.

[0819] Output: Learning progress reports sent to educators and parents.

[0820] (Application example 1)

[0821] Next, a description will be given of Application 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."

[0822] Upgrading the skills of factory workers and training them in robot operation and maintenance is extremely important, but the lack of customized learning materials tailored to each worker's learning style and progress results in a decline in learning efficiency.In addition, there is an insufficient system for reflecting feedback on learning outcomes in the creation of next learning materials, which means that worker motivation and skill improvement are not being fully achieved.

[0823] 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.

[0824] In this invention, the server includes: means for a user to input information regarding learning styles and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the customized learning materials to be displayed in visual or multimedia format, including robot operation and maintenance training for factory workers; means for collecting feedback from the user and reflecting it in the generation of next learning materials; means for the server to record and monitor the user's learning progress and periodically provide assessment tests; means for the server to recommend next learning content based on the results of the assessment tests; and means for the server to generate regular reports based on the learning progress and results and distribute them to educators and managers. This makes it possible to improve the learning efficiency of factory workers and provide optimal training materials for each worker.

[0825] "Users" refer to factory workers who use the system to learn.

[0826] "Learning style" refers to the format or method in which a user most effectively learns, and includes different formats such as visual, auditory, and tactile.

[0827] A "learning goal" refers to a specific skill or knowledge acquisition goal that a user is trying to achieve.

[0828] "Means for inputting information" refers to the interface that allows users to provide information about their learning style and learning goals to the system.

[0829] "Server" refers to a computer system that collects information input by users, analyzes it, and generates and distributes customized educational materials.

[0830] "Multimedia teaching materials" refers to teaching materials that combine multiple media formats, such as text, video, and audio.

[0831] "Feedback" refers to the opinions, evaluations, and reports of understanding provided by users after learning.

[0832] "Assessment Test" refers to a test provided to assess a user's learning progress and level of understanding.

[0833] "Training" refers to a series of learning activities that allow a user to acquire specific skills or knowledge.

[0834] "Report" refers to a report summarizing a user's learning progress and results.

[0835] The term "administrator" refers to a person who monitors the learning status of users and provides guidance.

[0836] "Generated learning materials" refers to learning content that is customized and created by the server according to the user's learning style and goals.

[0837] "Visual format" refers to a format that uses images or pictures to present information.

[0838] "Factory workers" refer to employees who require training to operate machines and robots in factories.

[0839] This invention is an educational system for effectively supporting training in improving the skills of factory workers and robot operation. The system generates multimedia learning materials customized based on the user's learning style and learning goals, and provides them to the user's terminal.

[0840] First, users access the system using a device such as a smartphone or head-mounted display. They use an interface to input their learning style and learning goals, providing information such as whether they prefer visual or auditory learning. This allows the server to classify and analyze the user's learning style.

[0841] The server uses machine learning algorithms to analyze the user's learning style based on the input information. Specifically, it uses "scikit-learn" and "TensorFlow" to optimally determine the user's learning style and generate learning materials accordingly. The generated learning materials include visual, auditory, and tactile multimedia materials.

[0842] The server stores the generated customized teaching materials in the cloud using AWS S3 and YouTube API, and delivers them to the user's device. Users can visually check the content and learn robot operation and maintenance procedures.

[0843] After studying, users send feedback to the system. This feedback includes their evaluation and opinions of the learning content. The server analyzes the feedback using natural language processing libraries such as "NLTK" and reflects the analysis results in the next generation of learning materials. This feedback allows for continuous improvement of the quality of the learning materials and the user experience.

[0844] The server also records the user's learning progress and provides regular assessment tests. The results of the assessment tests are analyzed using Jupyter Notebook and Pandas, and recommendations on what the user should learn next are provided to the user. Periodic reports are also generated and distributed to educators and administrators. This allows for effective management of worker skill development.

[0845] Examples:

[0846] For example, if a factory worker is trying to learn how to operate a new robot, the system allows him to receive visual video training on his smartphone, adapting the training to his optimal learning style and applying it to the actual task.

[0847] Example prompt sentence:

[0848] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[0849] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0850] Step 1:

[0851] Users access the system using a smartphone or head-mounted display, where they input information about their learning style and goals, including learning style preferences (visual, auditory, tactile, etc.) and the specific skills or knowledge they wish to learn. The input data is then sent to the server.

[0852] Step 2:

[0853] The server receives information about the user's learning style and learning goals. The received data is fed into a machine learning algorithm using "scikit-learn" or "TensorFlow" to analyze the user's learning style. As a result of the analysis, the optimal learning material format for the user is determined.

[0854] Step 3:

[0855] The server generates customized multimedia teaching materials based on the analysis results. At this stage, depending on the specified teaching material format (e.g., materials that make extensive use of visual videos or illustrations), a teaching material template is retrieved from "AWS S3" or "YouTube API" and individual teaching materials are created based on that. The generated teaching materials are stored in the cloud.

[0856] Step 4:

[0857] The server delivers the customized learning materials to the user's device. The user uses a smartphone or head-mounted display to view and study the delivered materials. The learning materials are provided in multimedia formats such as video, text, and images, supporting both visual and tactile learning styles.

[0858] Step 5:

[0859] After completing the study, the user inputs and sends feedback to the system. The feedback includes the level of understanding of the material, the difficulty level, and areas for improvement. The input feedback is sent to the server. The server analyzes the feedback using a natural language processing library such as "NLTK" and saves the analysis results as a new dataset.

[0860] Step 6:

[0861] The server uses the user's feedback information to generate the next set of learning materials. During this process, a machine learning algorithm is used to evaluate the user's learning progress and prepare to generate new customized learning materials according to the user's next learning goal. The server also periodically records the user's learning progress and provides assessment tests. These assessment tests are also automatically generated by the server and delivered to the user's device.

[0862] Step 7:

[0863] The server receives and analyzes the results of the assessment test. Based on the assessment results, it recommends what to learn next and generates new learning materials as needed. It also generates regular reports reflecting the user's progress and distributes them to educators and managers. This allows for effective management of the skill development of factory workers.

[0864] Example prompt sentence:

[0865] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[0866] 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.

[0867] This invention combines an emotion engine with an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, which are then provided to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[0868] 1. User Registration and Login

[0869] A user enters information such as name, email address, and password into a terminal to create a new account.

[0870] The terminal transmits the input information to the server.

[0871] The server stores the received information in a database and notifies the user that the registration was successful.

[0872] 2. Collecting learning style questionnaires

[0873] The user starts a survey on their device about their learning style and learning goals.

[0874] The terminal receives the survey questions from the server and presents them to the user.

[0875] The user answers the survey questions and enters the answers into the terminal.

[0876] The terminal transmits the user's questionnaire responses to the server.

[0877] 3. Learning Style Analysis

[0878] The server stores the survey response data in a database and analyzes the user's learning style using machine learning algorithms.

[0879] 4. Utilizing the Emotion Engine

[0880] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone, using, for example, facial expression analysis and voice analysis.

[0881] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[0882] 5. Generation of multimedia teaching materials

[0883] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[0884] The server generates multimedia teaching materials for each user based on the template.

[0885] 6. Distribution of teaching materials

[0886] The server transmits the generated teaching materials to the user's terminal.

[0887] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[0888] 7. Collecting and analyzing feedback

[0889] The user inputs and sends feedback about the learning materials from the terminal.

[0890] The terminal transmits the user's feedback information to the server.

[0891] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0892] The server can also incorporate emotional states to achieve even more precise customization.

[0893] 8. Progress Management and Evaluation

[0894] The server records the user's learning progress and periodically provides assessment tests as needed, for example generating and delivering short quizzes assessing basic concepts of calculus to the user.

[0895] The terminal presents the evaluation test to the user and transmits the answer results to the server.

[0896] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0897] 9. Recommended next learning content

[0898] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will move on to the next level, and if the emotional state indicates anxiety or stress, it will recommend relaxing content.

[0899] 10. Generate and share reports

[0900] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[0901] The server delivers the generated reports to educators and parents via email or other formats.

[0902] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles and emotional states, improving learners' motivation and reducing the burden on educators.

[0903] The processing flow will be explained below.

[0904] Step 1:

[0905] A user enters information such as name, email address, and password into a terminal to create a new account.

[0906] Step 2:

[0907] The terminal transmits the input information to the server.

[0908] Step 3:

[0909] The server stores the received information in a database and notifies the user that the registration was successful.

[0910] Step 4:

[0911] The user starts a survey on their device about their learning style and learning goals.

[0912] Step 5:

[0913] The terminal receives the survey questions from the server and presents them to the user.

[0914] Step 6:

[0915] The user answers the survey questions and enters the answers into the terminal.

[0916] Step 7:

[0917] The terminal transmits the user's questionnaire responses to the server.

[0918] Step 8:

[0919] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze the user's learning style.

[0920] Step 9:

[0921] The server determines the user's learning style and stores it in a database.

[0922] Step 10:

[0923] The server uses an emotion engine to analyze data from the user's webcam and microphone and recognize their emotional state in real time.

[0924] Step 11:

[0925] The server stores the emotional state data in a database and reflects it in further customizing learning styles.

[0926] Step 12:

[0927] Based on the learning style and emotional state determined by the server, an appropriate learning material template is retrieved from a database.

[0928] Step 13:

[0929] The server generates multimedia teaching materials for each user based on the template.

[0930] Step 14:

[0931] The server transmits the generated teaching materials to the user's terminal.

[0932] Step 15:

[0933] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[0934] Step 16:

[0935] Users access the learning materials and enter feedback into a terminal after each session.

[0936] Step 17:

[0937] The terminal transmits the user's feedback information to the server.

[0938] Step 18:

[0939] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[0940] Step 19:

[0941] The server records the user's learning progress in a database and monitors the progress in real time.

[0942] Step 20:

[0943] The server periodically generates assessment tests and distributes them to users.

[0944] Step 21:

[0945] The terminal presents an evaluation test to the user, who answers it.

[0946] Step 22:

[0947] The terminal transmits the user's answer results to the server.

[0948] Step 23:

[0949] The server analyzes the results of the assessment test and determines the user's level of understanding.

[0950] Step 24:

[0951] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[0952] Step 25:

[0953] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[0954] Step 26:

[0955] The server delivers the generated reports to educators and parents via email or other formats.

[0956] That's all.

[0957] Example 2

[0958] Next, a description will be given of 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."

[0959] Conventional educational support systems are not sufficiently customized based on the user's learning style and goals, and are unable to reflect the user's emotional state in real time while learning. As a result, learning efficiency declines and it becomes difficult to maintain user motivation.

[0960] 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.

[0961] In this invention, the server includes means for a user to input information about learning style and learning goals, means for the server to analyze the learning style based on the input information, means for the server to generate customized multimedia learning materials based on the analysis results, means for the server to analyze the user's emotional state using an emotion engine and reflect the results in further customization of the learning style, and means for the server to adjust the learning pace and learning material content based on the user's emotional state, thereby making it possible to provide a highly customized learning experience based on the user's learning style and real-time emotional state.

[0962] A "user" is a person who uses the system, who inputs information about their learning style and learning goals, and who receives learning materials.

[0963] A "terminal" is an electronic device operated by a user, used to input information, display educational materials, etc. Examples include PCs, smartphones, and tablets.

[0964] A "server" is a computer system that receives data sent by users, processes and analyzes it, and generates and distributes appropriate educational materials.

[0965] "Means for inputting information" refers to the interface or method by which users input information about their learning styles and learning goals into the system. Examples include web forms and questionnaire-style input screens.

[0966] "Means for analyzing learning styles" refers to the process by which the server uses machine learning algorithms and statistical analysis to identify the optimal learning method based on the information the user has entered.

[0967] "Multimedia learning materials" are learning resources that combine multiple media formats, such as text, images, audio, and video.

[0968] An "emotion engine" is software or an algorithm for analyzing a user's emotional state, recognizing the user's emotions in real time through facial expression analysis, voice analysis, etc.

[0969] "Means for adjusting learning pace and learning material content" refers to a function that flexibly changes the learning content and its progress speed based on the user's emotional state and learning style. For example, it includes slowing down the pace for users who are under stress.

[0970] "Means for collecting feedback" are methods by which users can input their opinions about their learning experience and learning materials into the system. Examples include survey forms and rating systems.

[0971] "Means for recording learning progress" refers to a method for tracking a user's learning activities and accumulating data on their progress. For example, a learning management system (LMS) would be an example of this.

[0972] A "means for providing assessment tests" is a method for generating and delivering tests to users to assess their understanding or progress. Examples include online quizzes and exams.

[0973] "Means for recommending learning content" is a process that selects and suggests what the user should learn next based on the results of an assessment test and their emotional state.

[0974] A "means for generating a report" is a method for organizing a user's learning progress and achievements and creating a report in a visual format (e.g., PDF).

[0975] "Means of delivery to educators and parents" refers to the methods by which the generated reports are delivered to others involved in education, primarily via email or online portals.

[0976] This is an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, and provides them to the user's terminal. Furthermore, an emotion engine is used to analyze the user's emotional state in real time, dynamically customizing the learning experience.

[0977] User Registration and Login

[0978] To create a new account, a user enters information such as name, email address, and password into the device. The device sends the information to the server, which stores it in a database. The user then receives a success notification.

[0979] Collecting learning style questionnaires

[0980] The user starts a questionnaire about their learning style and learning goals on their device. The device receives the questionnaire questions from the server and presents them to the user. The user answers the questionnaire, and the answers are sent from the device to the server.

[0981] Learning Style Analysis

[0982] The server stores the survey responses in a database, then uses machine learning algorithms to analyze the user's learning style, for example, by using Scikit-learn to perform clustering and identify the user's learning style.

[0983] Utilizing the Emotion Engine

[0984] The server uses the user's webcam and microphone to recognize the user's emotional state during learning. For example, it performs facial expression and voice analysis using OpenCV. The server analyzes the emotional state data and reflects it in further customizing the learning style.

[0985] Multimedia teaching material generation

[0986] The server retrieves appropriate learning material templates from a database based on the identified learning style and emotional state, and then generates personalized multimedia learning materials using Adobe Creative Suite and HTML5.

[0987] Distribution of teaching materials

[0988] The generated learning materials are sent from the server to the user's device, which then displays them to the user. For example, the device can provide infographic-based learning materials to users who prefer visual learning, and change the pace if the emotion engine detects stress.

[0989] Collecting and analyzing feedback

[0990] Users input and submit feedback about learning materials from their devices. The devices then send the feedback information to the server, which stores it in a database. The analyzed feedback information is reflected in the next generation of learning materials.

[0991] Progress management and evaluation

[0992] The server records the user's learning progress and provides periodic assessment tests as needed. The device presents the assessment tests to the user and sends the results to the server. The server analyzes the results and determines the user's level of understanding.

[0993] Recommended next learning content

[0994] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will suggest moving on to the next level, and if the emotional state indicates anxiety or stress, it will suggest relaxing content.

[0995] Generate and share reports

[0996] The server periodically generates reports based on learning progress and results, including information on how much content the user understood, how long it took, what content they should learn next, how their emotional state affected the learning, etc. The generated reports are sent to educators and parents via email or other means.

[0997] Examples of concrete examples and prompts

[0998] For example, if a busy working adult learner sets the goal of "studying efficiently after work," the system can provide appropriate infographics or short video materials while monitoring the learner's emotional state in real time. If the emotion engine detects fatigue or stress, it will display relaxing content or inspirational messages.

[0999] Example prompt sentence:

[1000] "Provide instructional materials with easy-to-understand visual illustrations. Adjust your pace to slow down if you experience fatigue."

[1001] In this way, a highly customized learning experience is provided that is tailored to individual learning styles and emotional states.

[1002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] Entering user information

[1005] A user enters information such as name, email address, and password into a terminal to create a new account.

[1006] Input: Name, Email Address, Password

[1007] Output: Data entered into the input form on the terminal

[1008] What happens: A user fills out a web form and clicks the "Submit" button.

[1009] Step 2:

[1010] Sending input information

[1011] The terminal transmits the input information to the server.

[1012] Input: User information entered into the terminal

[1013] Output: User information sent to the server

[1014] Specific operation: The device sends an HTTP request to the server using the POST method.

[1015] Step 3:

[1016] Saving user information

[1017] The server stores the received information in a database and notifies the user that the registration was successful.

[1018] Input: HTTP request containing user information

[1019] Output: User information stored in the database

[1020] Specific operation: The server inserts the user information into a database (e.g., MySQL) and returns a success response.

[1021] Step 4:

[1022] Survey begins

[1023] The user starts a survey on their device about their learning style and learning goals.

[1024] Input: User requests to start a survey

[1025] Output: Survey start request to the server

[1026] Specific behavior: The user clicks the "Start Survey" button.

[1027] Step 5:

[1028] Presenting the survey questions

[1029] The terminal receives the survey questions from the server and presents them to the user.

[1030] Input: Survey questions sent from the server

[1031] Output: The survey question that is displayed to the user

[1032] Specific operation: The device sends an HTTP request to the server, receives survey questions in JSON format, and displays the questions in an HTML form.

[1033] Step 6:

[1034] Entering survey answers

[1035] The user answers the survey questions and enters the answers into the terminal.

[1036] Input: User survey responses

[1037] Output: Survey responses entered into the device

[1038] Specific behavior: A user enters answers into a web form and clicks the "Submit" button.

[1039] Step 7:

[1040] Submit your answer

[1041] The terminal transmits the user's questionnaire responses to the server.

[1042] Input: Survey answers entered into the device

[1043] Output: Survey responses sent to the server

[1044] Specific operation: The device sends an HTTP request to the server using the POST method.

[1045] Step 8:

[1046] Saving response data

[1047] The server stores the survey response data in a database.

[1048] Input: Survey responses sent to the server

[1049] Output: Survey responses stored in a database

[1050] Specific operation: The server inserts the response data into the database.

[1051] Step 9:

[1052] Learning Style Analysis

[1053] The server uses machine learning algorithms to analyze the user's learning style.

[1054] Input: Survey responses stored in the database

[1055] Output: Analyzed learning styles

[1056] What it does: The server uses Scikit-learn to perform clustering and regression analysis, for example, using K-means clustering to find clusters of learning styles.

[1057] Step 10:

[1058] Acquiring emotion data

[1059] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone.

[1060] Input: User camera video and audio data

[1061] Output: Parsed emotional state data

[1062] Specific operation: The server acquires camera images using WebRTC and performs facial expression and voice analysis using OpenCV.

[1063] Step 11:

[1064] Emotional Data Analysis

[1065] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[1066] Input: Parsed emotional state data

[1067] Output: Customized learning style data

[1068] Specific operation: The server updates the learning style data in the database based on the analysis results.

[1069] Step 12:

[1070] Obtaining teaching material templates

[1071] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[1072] Input: Learning style data, emotional state data

[1073] Output: Teaching material template data

[1074] Specific operation: The server uses an SQL query to select the teaching material template.

[1075] Step 13:

[1076] Customizing teaching materials

[1077] The server generates multimedia teaching materials for each user based on the template.

[1078] Input: Teaching material template data, user's learning style and emotional state

[1079] Output: Customized multimedia teaching materials

[1080] Specific operation: The server creates teaching materials based on templates and visually customizes them using HTML5 and CSS.

[1081] Step 14:

[1082] Sending educational materials

[1083] The server transmits the generated teaching materials to the user's terminal.

[1084] Input: Customized multimedia materials

[1085] Output: The learning materials delivered to the user's device

[1086] Specific operation: The server returns the learning material data (e.g., PDF or HTML) in an HTTP response.

[1087] Step 15:

[1088] Display of teaching materials

[1089] The terminal displays the received educational material to the user.

[1090] Input: Delivered multimedia materials

[1091] Output: The material displayed to the user

[1092] Specific operation: The device displays the contents of the teaching material in a browser.

[1093] Step 16:

[1094] Enter your feedback

[1095] The user inputs and sends feedback about the learning materials from the terminal.

[1096] Input: Feedback

[1097] Output: HTTP request with feedback data

[1098] What happens: A user fills out a feedback form and clicks the "Submit" button.

[1099] Step 17:

[1100] Send Feedback

[1101] The terminal transmits the user's feedback information to the server.

[1102] Input: User feedback

[1103] Output: Feedback information sent to the server

[1104] Specific operation: The device posts feedback data via an HTTP request.

[1105] Step 18:

[1106] Feedback storage and analysis

[1107] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[1108] Input: Feedback information sent to the server

[1109] Output: Parsed feedback information

[1110] Specific operation: The server stores the feedback data in a database and uses an analysis algorithm to extract areas for improvement.

[1111] Step 19:

[1112] Record your learning progress

[1113] The server records the user's learning progress.

[1114] Input: User's learning progress data

[1115] Output: Learning progress data stored in a database

[1116] Specific operation: The server inserts or updates the learning progress data into the database.

[1117] Step 20:

[1118] Providing assessment tests

[1119] The server provides evaluation tests periodically as needed.

[1120] Input: Triggers the server's assessment test delivery routine

[1121] Output: Assessment test provided to the user

[1122] Specific behavior: The server sends the assessment test to the user, and the device displays the test.

[1123] Step 21:

[1124] Analyzing test results

[1125] The server analyzes the results of the assessment test and determines the user's level of understanding.

[1126] Input: User evaluation test result data

[1127] Output: Parsed test results

[1128] What happens: The server analyzes the test result data and runs an algorithm to assess the user's level of understanding.

[1129] Step 22:

[1130] Determining the recommendations

[1131] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[1132] Input: Parsed test results, emotional state data

[1133] Output: Suggested next steps

[1134] Specific operation: The server runs an algorithm to recommend the next learning content based on the analysis results and notifies the user.

[1135] Step 23:

[1136] Generate reports

[1137] The server periodically generates reports based on learning progress and results.

[1138] Input: User learning progress data, achievement data

[1139] Output: Generated report

[1140] Specific operation: The server creates a PDF report using a report generation tool such as Python.

[1141] Step 24:

[1142] Report distribution

[1143] The server delivers the generated reports to educators and parents via email or other formats.

[1144] Input: Generated report

[1145] Output: Reports delivered to educators and parents

[1146] What happens: The server sends the email using the SMTP protocol.

[1147] (Application example 2)

[1148] Next, a description will be given of Application 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."

[1149] In modern factory training support systems, it is extremely important to provide customized instruction that takes into account the learning style and emotional state of each worker and adapts to their individual needs. However, conventional systems have difficulty capturing the learner's real-time emotional state, making it difficult to effectively manage learning progress and provide appropriate learning materials. This has resulted in poor improvement in workers' learning efficiency and motivation, and has placed a heavy burden on educators. To solve this problem, a system that can generate and deliver customized learning materials based on learning style and emotional state is needed.

[1150] 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.

[1151] In this invention, the server includes: means for a user to input information regarding learning style and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the server to provide the generated learning materials to the user's terminal; means for recognizing and analyzing the user's emotional state in real time; means for generating customized teaching materials based on the emotional state; means for the server to record and monitor the user's learning progress and periodically provide evaluation tests; means for the server to recommend next learning content based on the results of the evaluation tests; means for the server to generate periodic reports based on the learning progress and results and distribute them to educators and parents; means for reflecting the feedback information in the generation of next learning materials; and means for providing learning materials based on the user's working environment and learning style. This improves the learning efficiency and motivation of workers and reduces the burden on educators.

[1152] A "user" is an individual who uses the educational support system by inputting information about their learning style and learning goals.

[1153] The "server" is a central computer system that handles a series of processes such as analyzing learning styles, generating customized multimedia learning materials, recording learning progress, and providing assessment tests.

[1154] "Learning style" refers to individual characteristics such as a user's preferred learning method or medium, including visual, auditory, and tactile learning.

[1155] A "learning goal" is a specific learning outcome or skill that a user is trying to achieve.

[1156] "Teaching materials" refers to learning materials and content provided to assist users in their learning.

[1157] "Emotional state" refers to the user's current mental and emotional state, including stress, concentration, excitement, etc.

[1158] "Customized instructional materials" refers to learning materials that are individually tailored based on a user's learning style and emotional state.

[1159] "Feedback information" refers to opinions and impressions regarding learning collected from users.

[1160] An "assessment test" is a test provided by the server to measure the user's level of understanding of the learning content.

[1161] A "report" is a detailed report summarizing learning progress and achievements that is distributed to educators and parents.

[1162] The system for realizing this invention mainly consists of a server, a user terminal, and various input devices. The system aims to provide customized instruction based on learning style and emotional state.

[1163] First, the user inputs information about their learning style and learning goals into a device, such as a tablet or smartphone. This input information is then sent to a server, which processes the information using a cloud-based computing system (e.g., AWS or Google Cloud).

[1164] The server analyzes the learning style based on the information received from the user. To do this, it uses a machine learning algorithm (e.g., TensorFlow or PyTorch) to store the user's questionnaire response data in a database (e.g., MySQL or PostgreSQL) and analyzes it. It also uses data from cameras and microphones installed on the devices and robots to recognize the learner's emotional state in real time. It uses an emotion analysis engine (e.g., Microsoft Azure Emotion API) to identify the learner's emotional state from facial expressions and voice.

[1165] The server selects appropriate learning material templates based on the learning style and emotional state data, and generates customized teaching materials. The materials are customized to provide visual content for users who prefer visual learning and audio content for users who prefer auditory learning. The generated teaching materials are quickly delivered to the user's device.

[1166] The server also collects feedback information from users and reflects it in the generation of the next learning material. Feedback is collected and entered from the device and sent to the server. The server analyzes the feedback data and reflects it in the next learning content, providing a more effective learning experience.

[1167] The server also records the user's learning progress and provides assessment tests, which evaluate the learner's level of understanding and recommend what they should study next. Periodically generated reports clearly show the learner's progress and results and are distributed to educators and parents, making it possible to keep track of the learner's learning situation at all times.

[1168] For example, enter data based on the following prompt:

[1169] Username: Suzuki Taro

[1170] Learning objective: Learn how to operate a new machine tool

[1171] Emotional state: High stress level, low concentration

[1172] Feedback: I find the content of the instruction difficult.

[1173] This system improves workers' learning efficiency and motivation, and reduces the burden on instructors.

[1174] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1175] Step 1:

[1176] Users enter information about their learning style and learning goals.

[1177] Users use a tablet or smartphone to answer a questionnaire about their learning style and learning goals, and enter the necessary information, including their name, email address, learning goals, etc. This information is first saved on the device.

[1178] Input: Learning style, learning goals, personal information

[1179] Output: Input information saved on the device

[1180] Step 2:

[1181] The terminal transmits the input information to the server.

[1182] The device sends the stored information to the server using a communication protocol (e.g., REST API).

[1183] Input: Input information saved on the device

[1184] Output: Learning style and learning goal information sent to the server

[1185] Step 3:

[1186] The server analyzes learning styles based on the input information.

[1187] The server stores the received information in a database (e.g., MySQL or PostgreSQL) and analyzes learning styles using machine learning algorithms (e.g., TensorFlow or PyTorch).

[1188] Input: Learning style and learning goal information sent to the server

[1189] Output: Learning style analysis results

[1190] Step 4:

[1191] The server recognizes and analyzes the user's emotional state in real time.

[1192] The server receives data from the camera and microphone installed on the device or robot, and uses an emotion analysis engine (such as Microsoft Azure Emotion API) to analyze the user's facial expressions and voice to identify emotional state data, such as stress level and concentration.

[1193] Input: Real-time camera and microphone data from devices and robots

[1194] Output: Emotional state data

[1195] Step 5:

[1196] The server generates customized instructional materials based on learning style and emotional state.

[1197] The server retrieves appropriate teaching material templates from a database based on the results of the learning style analysis and emotional state data, and generates customized teaching materials.

[1198] Input: Learning style analysis results, emotional state data

[1199] Output: Customized teaching materials

[1200] Step 6:

[1201] The server provides the generated educational materials to the user's terminal.

[1202] The server sends the generated customized training materials to the user's device using a communication protocol (e.g., REST API).

[1203] Input: Customized instructional materials

[1204] Output: Instructional materials provided to the device

[1205] Step 7:

[1206] The user enters and submits feedback about the learning materials.

[1207] Users can input feedback about the learning materials through their terminals and send it to the server. The feedback includes the difficulty level of the materials, the level of understanding, and requests for improvement.

[1208] Input: Feedback on the learning materials

[1209] Output: Feedback information sent to the server

[1210] Step 8:

[1211] The server analyzes the feedback information and reflects it in the next generation of teaching materials.

[1212] The server stores the feedback information in a database and analyzes it, so that the feedback is reflected in the next teaching material provided.

[1213] Input: Feedback information

[1214] Output: Data for next teaching material generation that reflects the feedback

[1215] Step 9:

[1216] The server records and monitors the user's learning progress and periodically provides assessment tests.

[1217] The server records the user's learning progress in a database, generates assessment tests at regular intervals, and provides them to the terminal. The results of the assessment tests are analyzed to grasp the user's learning progress.

[1218] Input: Learning progress, assessment test

[1219] Output: Evaluation test results, learning progress data

[1220] Step 10:

[1221] The server recommends the next learning content based on the results of the evaluation test.

[1222] The server recommends what to learn next based on the results of the evaluation test and emotional state data, and the recommendations are provided to the user's device.

[1223] Input: Evaluation test results, emotional state data

[1224] Output: Recommended learning content

[1225] Step 11:

[1226] The server generates regular reports based on learning progress and results and distributes them to educators and parents.

[1227] The server extracts learning progress and results from the database and periodically generates reports, which are then sent to educators and parents via email or other means.

[1228] Input: Learning progress and achievement data

[1229] Output: Regular reports delivered to educators and parents

[1230] For example, enter data based on the following prompt:

[1231] Username: Suzuki Taro

[1232] Learning objective: Learn how to operate a new machine tool

[1233] Emotional state: High stress level, low concentration

[1234] Feedback: I find the content of the instruction difficult.

[1235] 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.

[1236] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1237] 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.

[1238] [Third embodiment]

[1239] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1240] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1241] 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).

[1242] 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.

[1243] 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.

[1244] 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).

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1251] This invention relates to an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, and provides these to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[1252] 1. User Registration and Login

[1253] User: A new user enters the required information into the device to register an account, including name, email address, and password, and submits it.

[1254] Server: Receives the entered information and stores it in the database. Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[1255] 2. Collecting learning style questionnaires

[1256] User: Initiates a survey on their device about their learning style and learning goals, including their preferred learning modality (visual, auditory, tactile, etc.) and the specific content they want to learn.

[1257] Server: Stores the received survey responses in a database and analyzes the user's overall learning style.

[1258] 3. Learning Style Analysis

[1259] Server: The survey results stored in the database are input into a machine learning algorithm to determine the user's learning style. Based on this determination, the type of learning material that is best suited to the user is selected.

[1260] 4. Generation of multimedia teaching materials

[1261] Server: Retrieves learning material templates from a database based on the identified learning style, including text, images, videos, and interactive quizzes.

[1262] Server: Based on the acquired templates, it generates original multimedia teaching materials tailored to the user's level and interests.

[1263] 5. Distribution of teaching materials

[1264] Server: Sends the generated learning materials to the user's device. For example, a user learning calculus can be provided with visual video learning materials and text with illustrations.

[1265] Terminal: Displays the received learning materials to the user.

[1266] 6. Collecting and analyzing feedback

[1267] Users: Enter and submit feedback on the learning materials they have studied, including an assessment of the difficulty and level of comprehension of the material.

[1268] Server: Receives and analyzes feedback and reflects it in the next generation of teaching materials.

[1269] 7. Progress Management and Evaluation

[1270] Server: Records the user's learning progress and provides periodic assessment tests as needed. For example, it generates and delivers short quizzes assessing basic concepts of calculus to the user.

[1271] Device: Receives the assessment test, presents it to the user, and sends the answers to the server, which analyzes them and recommends the next course of study.

[1272] 8. Generate and share reports

[1273] Server: Generates periodic reports based on learning progress and results, including information such as how much content the user has understood, how long it took, and what content they should learn next.

[1274] Server: Delivers the generated reports to educators and parents via email or other means.

[1275] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles, improves learners' motivation, and reduces the burden on educators.

[1276] The processing flow will be explained below.

[1277] Step 1:

[1278] A user enters information such as name, email address, and password into a terminal to create a new account.

[1279] Step 2:

[1280] The terminal transmits the input information to the server.

[1281] Step 3:

[1282] The server stores the received information in a database and notifies the user that the registration was successful.

[1283] Step 4:

[1284] The user starts a survey on their device about their learning style and learning goals.

[1285] Step 5:

[1286] The terminal receives the survey questions from the server and presents them to the user.

[1287] Step 6:

[1288] The user answers the survey questions and enters the answers into the terminal.

[1289] Step 7:

[1290] The terminal transmits the user's questionnaire responses to the server.

[1291] Step 8:

[1292] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze learning styles.

[1293] Step 9:

[1294] The server uses machine learning algorithms to determine the user's learning style.

[1295] Step 10:

[1296] Based on the learning style determined by the server, an appropriate teaching material template is retrieved from the database.

[1297] Step 11:

[1298] The server generates multimedia teaching materials for each user based on the template.

[1299] Step 12:

[1300] The server transmits the generated teaching materials to the user's terminal.

[1301] Step 13:

[1302] The terminal displays the received educational material to the user.

[1303] Step 14:

[1304] Users access the learning materials and enter feedback into a terminal after each session.

[1305] Step 15:

[1306] The terminal transmits the user's feedback information to the server.

[1307] Step 16:

[1308] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[1309] Step 17:

[1310] The server records the user's learning progress in a database and updates it as the learning progresses.

[1311] Step 18:

[1312] The server periodically generates assessment tests and distributes them to users.

[1313] Step 19:

[1314] The terminal presents an evaluation test to the user, who answers it.

[1315] Step 20:

[1316] The terminal transmits the user's answer results to the server.

[1317] Step 21:

[1318] The server analyzes the results of the assessment test and determines the user's level of understanding.

[1319] Step 22:

[1320] Recommends what the server should learn next.

[1321] Step 23:

[1322] The server generates reports based on learning progress and results and distributes them to educators and parents.

[1323] That's all.

[1324] Example 1

[1325] Next, a description will be given of 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."

[1326] Conventional educational support systems do not provide learning materials that match the individual learning style of each user, making it difficult to achieve effective learning. Furthermore, the process for effectively collecting and analyzing users' learning progress and feedback and reflecting it in the next set of learning materials is insufficient, which can lead to a decline in the quality of learning. Furthermore, the provision of appropriate progress reports to educators and parents is also insufficient, making it necessary to strengthen the learning support system.

[1327] 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.

[1328] In this invention, the server includes a means for generating learning materials using templates based on the user's learning style, a means for customizing specific learning content according to the templates, and a means for analyzing user feedback through text analysis. This allows for the generation and provision of learning materials suited to the user's individual learning style, effectively incorporating feedback, and accurately managing progress. It also enables the provision of reliable progress reports to educators and parents.

[1329] "User" refers to an individual who uses the system to learn.

[1330] "Learning style" refers to the method or format in which a user learns most effectively, for example, visual, auditory, or tactile learning styles.

[1331] "Learning Objective" refers to a specific learning outcome or skill that a user wants to achieve.

[1332] "Server" refers to a computer system for receiving input information from users, processing and analyzing data, and generating and providing results.

[1333] "Information" refers to data provided by the user, specifically including learning style, learning goals, and feedback content.

[1334] A "template" is a framework that provides a basic structure and format for generating learning materials, allowing for efficient and consistent generation of learning materials.

[1335] "Multimedia learning materials" refers to learning content that combines multiple media formats, such as text, images, video, audio, and interactive quizzes.

[1336] "Feedback" refers to the opinions and ratings provided by users after using learning materials, which helps improve the quality and content of the materials.

[1337] "Text analysis" refers to the process of analyzing user feedback using natural language processing techniques to extract meaning.

[1338] "Study progress" refers to an indicator that shows how much progress a user has made as they progress through their studies. Specifically, it includes the degree of understanding and completion of the learning content.

[1339] An "assessment test" refers to a test that allows a user to check what they have learned and evaluate their level of understanding.

[1340] "Report" refers to a report summarizing a user's learning progress and results, including the user's level of understanding of the learning content, study time, next study steps, etc.

[1341] "Educator" refers to a person who supports and guides users in their learning. Specifically, this includes teachers and coaches.

[1342] "Guardian" refers to a parent or guardian who is responsible for supervising the user's learning status if the user is a minor.

[1343] MODE FOR CARRYING OUT THE INVENTION

[1344] In the educational support system of the present invention, a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, which are then provided to the user's terminal. This system can manage the user's learning progress and collect feedback to reflect in the generation of the next learning material.

[1345] User Registration and Login

[1346] User: A new user enters the required information, such as their name, email address, and password, into the terminal and submits it. For example, to create an account, Taro Tanaka enters tanaka@example.com and the password he set, and submits it.

[1347] Server: Receives the entered information and stores it in a database (specifically, MySQL, etc.). Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[1348] Collecting learning style questionnaires

[1349] User: Completes a questionnaire on their device about their learning style and goals. For example, the user might answer "I prefer visual learning materials" and specifically state "I want to learn the basics of calculus."

[1350] Server: Stores the received survey responses in a database and adds them to a dataset for analysis.

[1351] Learning Style Analysis

[1352] Server: Analyzes the learning style answers stored in the database using Python and machine learning libraries such as scikit-learn. Specifically, the answers are extracted as features and a clustering algorithm (e.g., K-means) is applied to determine the user's learning style.

[1353] Server: The results of the assessment are stored in a database, and this information is used to prepare data for selecting the most appropriate learning materials.

[1354] Multimedia teaching material generation

[1355] Server: Retrieves learning material templates from the database that correspond to the identified learning style. For example, selects video or infographic templates for a user who prefers "visual learning materials."

[1356] Server: Generates unique multimedia learning materials based on templates, tailored to the level and interests of specific users. For example, it creates learning materials including videos and exercises on the theme of "Fundamentals of Calculus."

[1357] Distribution of teaching materials

[1358] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (such as the URL of the video file or text file) via HTTP request.

[1359] Terminal: Analyzes the received HTTP response and creates a screen that displays links and data for the learning materials. Users can view or download the learning materials through the terminal.

[1360] Collecting and analyzing feedback

[1361] User: Enter feedback about the learning material (e.g., "The explanation in the video was easy to understand, but the questions were difficult") into the input form on the device and click the submit button.

[1362] Server: Receives feedback and stores it in a database. Analyzes the feedback data using text analysis tools (such as NLTK) to help improve the quality of the learning materials.

[1363] Progress management and evaluation

[1364] Server: Retrieves user learning progress from a database and periodically generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a multiple choice quiz to assess "Fundamental Concepts of Calculus."

[1365] Terminal: Presents the assessment test received from the server to the user, collects the user's answers, and sends them to the server.

[1366] Server: Analyzes the user's answers and generates feedback to recommend what to study next.

[1367] Generate and share reports

[1368] Server: Generates periodic reports based on learning progress and results. For example, it creates a PDF report summarizing the user's learning time, level of understanding, and what they should learn next.

[1369] Server: Delivers the generated reports to educators and parents via email (using the SMTP protocol) or other formats.

[1370] Prompt Sentence Examples

[1371] Describe the process for generating videos and text about the basics of calculus for new user Taro Tanaka, who creates an account and responds to a survey indicating that he prefers visual learning materials.

[1372] By following the above steps, the educational support system of the present invention can provide users with an optimal learning environment and maximize learning effectiveness. It also strengthens the educational support system by providing educators and parents with accurate learning progress information.

[1373] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1374] The flow of this system's program processing

[1375] Step 1: User Registration and Login

[1376] Input: The user enters their name, email address, and password.

[1377] Operation:

[1378] User: Enters the required information into the form on the device and clicks the submit button.

[1379] Terminal: Generates a request to send the entered data to the server.

[1380] Server: Receives the request and validates the data. If it's a new user, saves the information to the database and returns a success message. If it's an existing user, checks the authentication information and, if it matches, creates a session ID and returns a successful login message.

[1381] Output: Account creation success message for new users, login success message and session ID for existing users.

[1382] Step 2: Collect a learning style questionnaire

[1383] Input: Users fill out a questionnaire about their learning style and learning goals.

[1384] Operation:

[1385] Users: Complete and submit a survey form with information about their learning style and learning goals.

[1386] Terminal: Generates a request that collects the user's answers and sends them to the server.

[1387] Server: Receives survey responses and stores them in a database.

[1388] Output: Survey responses are saved in a database.

[1389] Step 3: Analyze your learning styles

[1390] Input: Survey results stored in a database.

[1391] Operation:

[1392] Server: Retrieves survey results from the database and analyzes them using machine learning algorithms. Specifically, it extracts learning styles as features and performs clustering (e.g., K-means).

[1393] Server: Based on the analysis results, the user's learning style is determined and the results are stored in a database.

[1394] Output: The learning style assessment results are stored in a database.

[1395] Step 4: Creating multimedia materials

[1396] Input: Learning style assessment results and template database.

[1397] Operation:

[1398] Server: Obtains learning material templates corresponding to learning styles from a template database.

[1399] Server: Based on the acquired templates, the server generates customized multimedia teaching materials tailored to the user's level and learning goals. Specifically, it uses video editing software and a text editor.

[1400] Output: Customized multimedia teaching materials.

[1401] Step 5: Distributing the materials

[1402] Input: customized multimedia teaching materials.

[1403] Operation:

[1404] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (video files and text files) via HTTP requests.

[1405] Terminal: Displays the received educational material data. Specifically, displays the educational material using a video player or text viewer.

[1406] Output: The educational material displayed on the user's device.

[1407] Step 6: Collect and analyze feedback

[1408] Input: User feedback.

[1409] Operation:

[1410] User: Enter your feedback about the learning material and click the submit button.

[1411] Terminal: Generates a request to send feedback data to the server.

[1412] Server: Receives feedback, stores it in a database, and analyzes it using natural language processing tools (e.g., NLTK).

[1413] Output: The analysis data of the feedback results is saved.

[1414] Step 7: Progress Management and Evaluation

[1415] Input: User's learning progress data.

[1416] Operation:

[1417] Server: Retrieves user learning progress data from a database and generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a test to assess "Fundamental Concepts of Calculus."

[1418] Terminal: Presents the assessment test received from the server to the user. Sends the user's answers to the server.

[1419] Server: Analyzes the user's answers and recommends the next learning content.

[1420] Output: Learning progress data and assessment results.

[1421] Step 8: Generate and share the report

[1422] Input: Learning progress data and assessment results.

[1423] Operation:

[1424] Server: Generates reports based on learning progress and results. Specifically, it creates reports in PDF format and summarizes the user's learning time and level of understanding.

[1425] Server: Generates reports and delivers them to educators and parents via email.

[1426] Output: Learning progress reports sent to educators and parents.

[1427] (Application example 1)

[1428] Next, a description will be given of Application 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."

[1429] Upgrading the skills of factory workers and training them in robot operation and maintenance is extremely important, but the lack of customized learning materials tailored to each worker's learning style and progress results in a decline in learning efficiency.In addition, there is an insufficient system for reflecting feedback on learning outcomes in the creation of next learning materials, which means that worker motivation and skill improvement are not being fully achieved.

[1430] 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.

[1431] In this invention, the server includes: means for a user to input information regarding learning styles and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the customized learning materials to be displayed in visual or multimedia format, including robot operation and maintenance training for factory workers; means for collecting feedback from the user and reflecting it in the generation of next learning materials; means for the server to record and monitor the user's learning progress and periodically provide assessment tests; means for the server to recommend next learning content based on the results of the assessment tests; and means for the server to generate regular reports based on the learning progress and results and distribute them to educators and managers. This makes it possible to improve the learning efficiency of factory workers and provide optimal training materials for each worker.

[1432] "Users" refer to factory workers who use the system to learn.

[1433] "Learning style" refers to the format or method in which a user most effectively learns, and includes different formats such as visual, auditory, and tactile.

[1434] A "learning goal" refers to a specific skill or knowledge acquisition goal that a user is trying to achieve.

[1435] "Means for inputting information" refers to the interface that allows users to provide information about their learning style and learning goals to the system.

[1436] "Server" refers to a computer system that collects information input by users, analyzes it, and generates and distributes customized educational materials.

[1437] "Multimedia teaching materials" refers to teaching materials that combine multiple media formats, such as text, video, and audio.

[1438] "Feedback" refers to the opinions, evaluations, and reports of understanding provided by users after learning.

[1439] "Assessment Test" refers to a test provided to assess a user's learning progress and level of understanding.

[1440] "Training" refers to a series of learning activities that allow a user to acquire specific skills or knowledge.

[1441] "Report" refers to a report summarizing a user's learning progress and results.

[1442] The term "administrator" refers to a person who monitors the learning status of users and provides guidance.

[1443] "Generated learning materials" refers to learning content that is customized and created by the server according to the user's learning style and goals.

[1444] "Visual format" refers to a format that uses images or pictures to present information.

[1445] "Factory workers" refer to employees who require training to operate machines and robots in factories.

[1446] This invention is an educational system for effectively supporting training in improving the skills of factory workers and robot operation. The system generates multimedia learning materials customized based on the user's learning style and learning goals, and provides them to the user's terminal.

[1447] First, users access the system using a device such as a smartphone or head-mounted display. They use an interface to input their learning style and learning goals, providing information such as whether they prefer visual or auditory learning. This allows the server to classify and analyze the user's learning style.

[1448] The server uses machine learning algorithms to analyze the user's learning style based on the input information. Specifically, it uses "scikit-learn" and "TensorFlow" to optimally determine the user's learning style and generate learning materials accordingly. The generated learning materials include visual, auditory, and tactile multimedia materials.

[1449] The server stores the generated customized teaching materials in the cloud using AWS S3 and YouTube API, and delivers them to the user's device. Users can visually check the content and learn robot operation and maintenance procedures.

[1450] After studying, users send feedback to the system. This feedback includes their evaluation and opinions of the learning content. The server analyzes the feedback using natural language processing libraries such as "NLTK" and reflects the analysis results in the next generation of learning materials. This feedback allows for continuous improvement of the quality of the learning materials and the user experience.

[1451] The server also records the user's learning progress and provides regular assessment tests. The results of the assessment tests are analyzed using Jupyter Notebook and Pandas, and recommendations on what the user should learn next are provided to the user. Periodic reports are also generated and distributed to educators and administrators. This allows for effective management of worker skill development.

[1452] Examples:

[1453] For example, if a factory worker is trying to learn how to operate a new robot, the system allows him to receive visual video training on his smartphone, adapting the training to his optimal learning style and applying it to the actual task.

[1454] Example prompt sentence:

[1455] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[1456] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1457] Step 1:

[1458] Users access the system using a smartphone or head-mounted display, where they input information about their learning style and goals, including learning style preferences (visual, auditory, tactile, etc.) and the specific skills or knowledge they wish to learn. The input data is then sent to the server.

[1459] Step 2:

[1460] The server receives information about the user's learning style and learning goals. The received data is fed into a machine learning algorithm using "scikit-learn" or "TensorFlow" to analyze the user's learning style. As a result of the analysis, the optimal learning material format for the user is determined.

[1461] Step 3:

[1462] The server generates customized multimedia teaching materials based on the analysis results. At this stage, depending on the specified teaching material format (e.g., materials that make extensive use of visual videos or illustrations), a teaching material template is retrieved from "AWS S3" or "YouTube API" and individual teaching materials are created based on that. The generated teaching materials are stored in the cloud.

[1463] Step 4:

[1464] The server delivers the customized learning materials to the user's device. The user uses a smartphone or head-mounted display to view and study the delivered materials. The learning materials are provided in multimedia formats such as video, text, and images, supporting both visual and tactile learning styles.

[1465] Step 5:

[1466] After completing the study, the user inputs and sends feedback to the system. The feedback includes the level of understanding of the material, the difficulty level, and areas for improvement. The input feedback is sent to the server. The server analyzes the feedback using a natural language processing library such as "NLTK" and saves the analysis results as a new dataset.

[1467] Step 6:

[1468] The server uses the user's feedback information to generate the next set of learning materials. During this process, a machine learning algorithm is used to evaluate the user's learning progress and prepare to generate new customized learning materials according to the user's next learning goal. The server also periodically records the user's learning progress and provides assessment tests. These assessment tests are also automatically generated by the server and delivered to the user's device.

[1469] Step 7:

[1470] The server receives and analyzes the results of the assessment test. Based on the assessment results, it recommends what to learn next and generates new learning materials as needed. It also generates regular reports reflecting the user's progress and distributes them to educators and managers. This allows for effective management of the skill development of factory workers.

[1471] Example prompt sentence:

[1472] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[1473] 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.

[1474] This invention combines an emotion engine with an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, which are then provided to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[1475] 1. User Registration and Login

[1476] A user enters information such as name, email address, and password into a terminal to create a new account.

[1477] The terminal transmits the input information to the server.

[1478] The server stores the received information in a database and notifies the user that the registration was successful.

[1479] 2. Collecting learning style questionnaires

[1480] The user starts a survey on their device about their learning style and learning goals.

[1481] The terminal receives the survey questions from the server and presents them to the user.

[1482] The user answers the survey questions and enters the answers into the terminal.

[1483] The terminal transmits the user's questionnaire responses to the server.

[1484] 3. Learning Style Analysis

[1485] The server stores the survey response data in a database and analyzes the user's learning style using machine learning algorithms.

[1486] 4. Utilizing the Emotion Engine

[1487] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone, using, for example, facial expression analysis and voice analysis.

[1488] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[1489] 5. Generation of multimedia teaching materials

[1490] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[1491] The server generates multimedia teaching materials for each user based on the template.

[1492] 6. Distribution of teaching materials

[1493] The server transmits the generated teaching materials to the user's terminal.

[1494] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[1495] 7. Collecting and analyzing feedback

[1496] The user inputs and sends feedback about the learning materials from the terminal.

[1497] The terminal transmits the user's feedback information to the server.

[1498] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[1499] The server can also incorporate emotional states to achieve even more precise customization.

[1500] 8. Progress Management and Evaluation

[1501] The server records the user's learning progress and periodically provides assessment tests as needed, for example generating and delivering short quizzes assessing basic concepts of calculus to the user.

[1502] The terminal presents the evaluation test to the user and transmits the answer results to the server.

[1503] The server analyzes the results of the assessment test and determines the user's level of understanding.

[1504] 9. Recommended next learning content

[1505] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will move on to the next level, and if the emotional state indicates anxiety or stress, it will recommend relaxing content.

[1506] 10. Generate and share reports

[1507] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[1508] The server delivers the generated reports to educators and parents via email or other formats.

[1509] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles and emotional states, improving learners' motivation and reducing the burden on educators.

[1510] The processing flow will be explained below.

[1511] Step 1:

[1512] A user enters information such as name, email address, and password into a terminal to create a new account.

[1513] Step 2:

[1514] The terminal transmits the input information to the server.

[1515] Step 3:

[1516] The server stores the received information in a database and notifies the user that the registration was successful.

[1517] Step 4:

[1518] The user starts a survey on their device about their learning style and learning goals.

[1519] Step 5:

[1520] The terminal receives the survey questions from the server and presents them to the user.

[1521] Step 6:

[1522] The user answers the survey questions and enters the answers into the terminal.

[1523] Step 7:

[1524] The terminal transmits the user's questionnaire responses to the server.

[1525] Step 8:

[1526] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze the user's learning style.

[1527] Step 9:

[1528] The server determines the user's learning style and stores it in a database.

[1529] Step 10:

[1530] The server uses an emotion engine to analyze data from the user's webcam and microphone and recognize their emotional state in real time.

[1531] Step 11:

[1532] The server stores the emotional state data in a database and reflects it in further customizing learning styles.

[1533] Step 12:

[1534] Based on the learning style and emotional state determined by the server, an appropriate learning material template is retrieved from a database.

[1535] Step 13:

[1536] The server generates multimedia teaching materials for each user based on the template.

[1537] Step 14:

[1538] The server transmits the generated teaching materials to the user's terminal.

[1539] Step 15:

[1540] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[1541] Step 16:

[1542] Users access the learning materials and enter feedback into a terminal after each session.

[1543] Step 17:

[1544] The terminal transmits the user's feedback information to the server.

[1545] Step 18:

[1546] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[1547] Step 19:

[1548] The server records the user's learning progress in a database and monitors the progress in real time.

[1549] Step 20:

[1550] The server periodically generates assessment tests and distributes them to users.

[1551] Step 21:

[1552] The terminal presents an evaluation test to the user, who answers it.

[1553] Step 22:

[1554] The terminal transmits the user's answer results to the server.

[1555] Step 23:

[1556] The server analyzes the results of the assessment test and determines the user's level of understanding.

[1557] Step 24:

[1558] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[1559] Step 25:

[1560] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[1561] Step 26:

[1562] The server delivers the generated reports to educators and parents via email or other formats.

[1563] That's all.

[1564] Example 2

[1565] Next, a description will be given of 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."

[1566] Conventional educational support systems are not sufficiently customized based on the user's learning style and goals, and are unable to reflect the user's emotional state in real time while learning. As a result, learning efficiency declines and it becomes difficult to maintain user motivation.

[1567] 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.

[1568] In this invention, the server includes means for a user to input information about learning style and learning goals, means for the server to analyze the learning style based on the input information, means for the server to generate customized multimedia learning materials based on the analysis results, means for the server to analyze the user's emotional state using an emotion engine and reflect the results in further customization of the learning style, and means for the server to adjust the learning pace and learning material content based on the user's emotional state, thereby making it possible to provide a highly customized learning experience based on the user's learning style and real-time emotional state.

[1569] A "user" is a person who uses the system, who inputs information about their learning style and learning goals, and who receives learning materials.

[1570] A "terminal" is an electronic device operated by a user, used to input information, display educational materials, etc. Examples include PCs, smartphones, and tablets.

[1571] A "server" is a computer system that receives data sent by users, processes and analyzes it, and generates and distributes appropriate educational materials.

[1572] "Means for inputting information" refers to the interface or method by which users input information about their learning styles and learning goals into the system. Examples include web forms and questionnaire-style input screens.

[1573] "Means for analyzing learning styles" refers to the process by which the server uses machine learning algorithms and statistical analysis to identify the optimal learning method based on the information the user has entered.

[1574] "Multimedia learning materials" are learning resources that combine multiple media formats, such as text, images, audio, and video.

[1575] An "emotion engine" is software or an algorithm for analyzing a user's emotional state, recognizing the user's emotions in real time through facial expression analysis, voice analysis, etc.

[1576] "Means for adjusting learning pace and learning material content" refers to a function that flexibly changes the learning content and its progress speed based on the user's emotional state and learning style. For example, it includes slowing down the pace for users who are under stress.

[1577] "Means for collecting feedback" are methods by which users can input their opinions about their learning experience and learning materials into the system. Examples include survey forms and rating systems.

[1578] "Means for recording learning progress" refers to a method for tracking a user's learning activities and accumulating data on their progress. For example, a learning management system (LMS) would be an example of this.

[1579] A "means for providing assessment tests" is a method for generating and delivering tests to users to assess their understanding or progress. Examples include online quizzes and exams.

[1580] "Means for recommending learning content" is a process that selects and suggests what the user should learn next based on the results of an assessment test and their emotional state.

[1581] A "means for generating a report" is a method for organizing a user's learning progress and achievements and creating a report in a visual format (e.g., PDF).

[1582] "Means of delivery to educators and parents" refers to the methods by which the generated reports are delivered to others involved in education, primarily via email or online portals.

[1583] This is an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, and provides them to the user's terminal. Furthermore, an emotion engine is used to analyze the user's emotional state in real time, dynamically customizing the learning experience.

[1584] User Registration and Login

[1585] To create a new account, a user enters information such as name, email address, and password into the device. The device sends the information to the server, which stores it in a database. The user then receives a success notification.

[1586] Collecting learning style questionnaires

[1587] The user starts a questionnaire about their learning style and learning goals on their device. The device receives the questionnaire questions from the server and presents them to the user. The user answers the questionnaire, and the answers are sent from the device to the server.

[1588] Learning Style Analysis

[1589] The server stores the survey responses in a database, then uses machine learning algorithms to analyze the user's learning style, for example, by using Scikit-learn to perform clustering and identify the user's learning style.

[1590] Utilizing the Emotion Engine

[1591] The server uses the user's webcam and microphone to recognize the user's emotional state during learning. For example, it performs facial expression and voice analysis using OpenCV. The server analyzes the emotional state data and reflects it in further customizing the learning style.

[1592] Multimedia teaching material generation

[1593] The server retrieves appropriate learning material templates from a database based on the identified learning style and emotional state, and then generates personalized multimedia learning materials using Adobe Creative Suite and HTML5.

[1594] Distribution of teaching materials

[1595] The generated learning materials are sent from the server to the user's device, which then displays them to the user. For example, the device can provide infographic-based learning materials to users who prefer visual learning, and change the pace if the emotion engine detects stress.

[1596] Collecting and analyzing feedback

[1597] Users input and submit feedback about learning materials from their devices. The devices then send the feedback information to the server, which stores it in a database. The analyzed feedback information is reflected in the next generation of learning materials.

[1598] Progress management and evaluation

[1599] The server records the user's learning progress and provides periodic assessment tests as needed. The device presents the assessment tests to the user and sends the results to the server. The server analyzes the results and determines the user's level of understanding.

[1600] Recommended next learning content

[1601] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will suggest moving on to the next level, and if the emotional state indicates anxiety or stress, it will suggest relaxing content.

[1602] Generate and share reports

[1603] The server periodically generates reports based on learning progress and results, including information on how much content the user understood, how long it took, what content they should learn next, how their emotional state affected the learning, etc. The generated reports are sent to educators and parents via email or other means.

[1604] Examples of concrete examples and prompts

[1605] For example, if a busy working adult learner sets the goal of "studying efficiently after work," the system can provide appropriate infographics or short video materials while monitoring the learner's emotional state in real time. If the emotion engine detects fatigue or stress, it will display relaxing content or inspirational messages.

[1606] Example prompt sentence:

[1607] "Provide instructional materials with easy-to-understand visual illustrations. Adjust your pace to slow down if you experience fatigue."

[1608] In this way, a highly customized learning experience is provided that is tailored to individual learning styles and emotional states.

[1609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1610] Step 1:

[1611] Entering user information

[1612] A user enters information such as name, email address, and password into a terminal to create a new account.

[1613] Input: Name, Email Address, Password

[1614] Output: Data entered into the input form on the terminal

[1615] What happens: A user fills out a web form and clicks the "Submit" button.

[1616] Step 2:

[1617] Sending input information

[1618] The terminal transmits the input information to the server.

[1619] Input: User information entered into the terminal

[1620] Output: User information sent to the server

[1621] Specific operation: The device sends an HTTP request to the server using the POST method.

[1622] Step 3:

[1623] Saving user information

[1624] The server stores the received information in a database and notifies the user that the registration was successful.

[1625] Input: HTTP request containing user information

[1626] Output: User information stored in the database

[1627] Specific operation: The server inserts the user information into a database (e.g., MySQL) and returns a success response.

[1628] Step 4:

[1629] Survey begins

[1630] The user starts a survey on their device about their learning style and learning goals.

[1631] Input: User requests to start a survey

[1632] Output: Survey start request to the server

[1633] Specific behavior: The user clicks the "Start Survey" button.

[1634] Step 5:

[1635] Presenting the survey questions

[1636] The terminal receives the survey questions from the server and presents them to the user.

[1637] Input: Survey questions sent from the server

[1638] Output: The survey question that is displayed to the user

[1639] Specific operation: The device sends an HTTP request to the server, receives survey questions in JSON format, and displays the questions in an HTML form.

[1640] Step 6:

[1641] Entering survey answers

[1642] The user answers the survey questions and enters the answers into the terminal.

[1643] Input: User survey responses

[1644] Output: Survey responses entered into the device

[1645] Specific behavior: A user enters answers into a web form and clicks the "Submit" button.

[1646] Step 7:

[1647] Submit your answer

[1648] The terminal transmits the user's questionnaire responses to the server.

[1649] Input: Survey answers entered into the device

[1650] Output: Survey responses sent to the server

[1651] Specific operation: The device sends an HTTP request to the server using the POST method.

[1652] Step 8:

[1653] Saving response data

[1654] The server stores the survey response data in a database.

[1655] Input: Survey responses sent to the server

[1656] Output: Survey responses stored in a database

[1657] Specific operation: The server inserts the response data into the database.

[1658] Step 9:

[1659] Learning Style Analysis

[1660] The server uses machine learning algorithms to analyze the user's learning style.

[1661] Input: Survey responses stored in the database

[1662] Output: Analyzed learning styles

[1663] What it does: The server uses Scikit-learn to perform clustering and regression analysis, for example, using K-means clustering to find clusters of learning styles.

[1664] Step 10:

[1665] Acquiring emotion data

[1666] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone.

[1667] Input: User camera video and audio data

[1668] Output: Parsed emotional state data

[1669] Specific operation: The server acquires camera images using WebRTC and performs facial expression and voice analysis using OpenCV.

[1670] Step 11:

[1671] Emotional Data Analysis

[1672] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[1673] Input: Parsed emotional state data

[1674] Output: Customized learning style data

[1675] Specific operation: The server updates the learning style data in the database based on the analysis results.

[1676] Step 12:

[1677] Obtaining teaching material templates

[1678] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[1679] Input: Learning style data, emotional state data

[1680] Output: Teaching material template data

[1681] Specific operation: The server uses an SQL query to select the teaching material template.

[1682] Step 13:

[1683] Customizing teaching materials

[1684] The server generates multimedia teaching materials for each user based on the template.

[1685] Input: Teaching material template data, user's learning style and emotional state

[1686] Output: Customized multimedia teaching materials

[1687] Specific operation: The server creates teaching materials based on templates and visually customizes them using HTML5 and CSS.

[1688] Step 14:

[1689] Sending educational materials

[1690] The server transmits the generated teaching materials to the user's terminal.

[1691] Input: Customized multimedia materials

[1692] Output: The learning materials delivered to the user's device

[1693] Specific operation: The server returns the learning material data (e.g., PDF or HTML) in an HTTP response.

[1694] Step 15:

[1695] Display of teaching materials

[1696] The terminal displays the received educational material to the user.

[1697] Input: Delivered multimedia materials

[1698] Output: The material displayed to the user

[1699] Specific operation: The device displays the contents of the teaching material in a browser.

[1700] Step 16:

[1701] Enter your feedback

[1702] The user inputs and sends feedback about the learning materials from the terminal.

[1703] Input: Feedback

[1704] Output: HTTP request with feedback data

[1705] What happens: A user fills out a feedback form and clicks the "Submit" button.

[1706] Step 17:

[1707] Send Feedback

[1708] The terminal transmits the user's feedback information to the server.

[1709] Input: User feedback

[1710] Output: Feedback information sent to the server

[1711] Specific operation: The device posts feedback data via an HTTP request.

[1712] Step 18:

[1713] Feedback storage and analysis

[1714] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[1715] Input: Feedback information sent to the server

[1716] Output: Parsed feedback information

[1717] Specific operation: The server stores the feedback data in a database and uses an analysis algorithm to extract areas for improvement.

[1718] Step 19:

[1719] Record your learning progress

[1720] The server records the user's learning progress.

[1721] Input: User's learning progress data

[1722] Output: Learning progress data stored in a database

[1723] Specific operation: The server inserts or updates the learning progress data into the database.

[1724] Step 20:

[1725] Providing assessment tests

[1726] The server provides evaluation tests periodically as needed.

[1727] Input: Triggers the server's assessment test delivery routine

[1728] Output: Assessment test provided to the user

[1729] Specific behavior: The server sends the assessment test to the user, and the device displays the test.

[1730] Step 21:

[1731] Analyzing test results

[1732] The server analyzes the results of the assessment test and determines the user's level of understanding.

[1733] Input: User evaluation test result data

[1734] Output: Parsed test results

[1735] What happens: The server analyzes the test result data and runs an algorithm to assess the user's level of understanding.

[1736] Step 22:

[1737] Determining the recommendations

[1738] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[1739] Input: Parsed test results, emotional state data

[1740] Output: Suggested next steps

[1741] Specific operation: The server runs an algorithm to recommend the next learning content based on the analysis results and notifies the user.

[1742] Step 23:

[1743] Generate reports

[1744] The server periodically generates reports based on learning progress and results.

[1745] Input: User learning progress data, achievement data

[1746] Output: Generated report

[1747] Specific operation: The server creates a PDF report using a report generation tool such as Python.

[1748] Step 24:

[1749] Report distribution

[1750] The server delivers the generated reports to educators and parents via email or other formats.

[1751] Input: Generated report

[1752] Output: Reports delivered to educators and parents

[1753] What happens: The server sends the email using the SMTP protocol.

[1754] (Application example 2)

[1755] Next, a description will be given of Application 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."

[1756] In modern factory training support systems, it is extremely important to provide customized instruction that takes into account the learning style and emotional state of each worker and adapts to their individual needs. However, conventional systems have difficulty capturing the learner's real-time emotional state, making it difficult to effectively manage learning progress and provide appropriate learning materials. This has resulted in poor improvement in workers' learning efficiency and motivation, and has placed a heavy burden on educators. To solve this problem, a system that can generate and deliver customized learning materials based on learning style and emotional state is needed.

[1757] 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.

[1758] In this invention, the server includes: means for a user to input information regarding learning style and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the server to provide the generated learning materials to the user's terminal; means for recognizing and analyzing the user's emotional state in real time; means for generating customized teaching materials based on the emotional state; means for the server to record and monitor the user's learning progress and periodically provide evaluation tests; means for the server to recommend next learning content based on the results of the evaluation tests; means for the server to generate periodic reports based on the learning progress and results and distribute them to educators and parents; means for reflecting the feedback information in the generation of next learning materials; and means for providing learning materials based on the user's working environment and learning style. This improves the learning efficiency and motivation of workers and reduces the burden on educators.

[1759] A "user" is an individual who uses the educational support system by inputting information about their learning style and learning goals.

[1760] The "server" is a central computer system that handles a series of processes such as analyzing learning styles, generating customized multimedia learning materials, recording learning progress, and providing assessment tests.

[1761] "Learning style" refers to individual characteristics such as a user's preferred learning method or medium, including visual, auditory, and tactile learning.

[1762] A "learning goal" is a specific learning outcome or skill that a user is trying to achieve.

[1763] "Teaching materials" refers to learning materials and content provided to assist users in their learning.

[1764] "Emotional state" refers to the user's current mental and emotional state, including stress, concentration, excitement, etc.

[1765] "Customized instructional materials" refers to learning materials that are individually tailored based on a user's learning style and emotional state.

[1766] "Feedback information" refers to opinions and impressions regarding learning collected from users.

[1767] An "assessment test" is a test provided by the server to measure the user's level of understanding of the learning content.

[1768] A "report" is a detailed report summarizing learning progress and achievements that is distributed to educators and parents.

[1769] The system for realizing this invention mainly consists of a server, a user terminal, and various input devices. The system aims to provide customized instruction based on learning style and emotional state.

[1770] First, the user inputs information about their learning style and learning goals into a device, such as a tablet or smartphone. This input information is then sent to a server, which processes the information using a cloud-based computing system (e.g., AWS or Google Cloud).

[1771] The server analyzes the learning style based on the information received from the user. To do this, it uses a machine learning algorithm (e.g., TensorFlow or PyTorch) to store the user's questionnaire response data in a database (e.g., MySQL or PostgreSQL) and analyzes it. It also uses data from cameras and microphones installed on the devices and robots to recognize the learner's emotional state in real time. It uses an emotion analysis engine (e.g., Microsoft Azure Emotion API) to identify the learner's emotional state from facial expressions and voice.

[1772] The server selects appropriate learning material templates based on the learning style and emotional state data, and generates customized teaching materials. The materials are customized to provide visual content for users who prefer visual learning and audio content for users who prefer auditory learning. The generated teaching materials are quickly delivered to the user's device.

[1773] The server also collects feedback information from users and reflects it in the generation of the next learning material. Feedback is collected and entered from the device and sent to the server. The server analyzes the feedback data and reflects it in the next learning content, providing a more effective learning experience.

[1774] The server also records the user's learning progress and provides assessment tests, which evaluate the learner's level of understanding and recommend what they should study next. Periodically generated reports clearly show the learner's progress and results and are distributed to educators and parents, making it possible to keep track of the learner's learning situation at all times.

[1775] For example, enter data based on the following prompt:

[1776] Username: Suzuki Taro

[1777] Learning objective: Learn how to operate a new machine tool

[1778] Emotional state: High stress level, low concentration

[1779] Feedback: I find the content of the instruction difficult.

[1780] This system improves workers' learning efficiency and motivation, and reduces the burden on instructors.

[1781] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1782] Step 1:

[1783] Users enter information about their learning style and learning goals.

[1784] Users use a tablet or smartphone to answer a questionnaire about their learning style and learning goals, and enter the necessary information, including their name, email address, learning goals, etc. This information is first saved on the device.

[1785] Input: Learning style, learning goals, personal information

[1786] Output: Input information saved on the device

[1787] Step 2:

[1788] The terminal transmits the input information to the server.

[1789] The device sends the stored information to the server using a communication protocol (e.g., REST API).

[1790] Input: Input information saved on the device

[1791] Output: Learning style and learning goal information sent to the server

[1792] Step 3:

[1793] The server analyzes learning styles based on the input information.

[1794] The server stores the received information in a database (e.g., MySQL or PostgreSQL) and analyzes learning styles using machine learning algorithms (e.g., TensorFlow or PyTorch).

[1795] Input: Learning style and learning goal information sent to the server

[1796] Output: Learning style analysis results

[1797] Step 4:

[1798] The server recognizes and analyzes the user's emotional state in real time.

[1799] The server receives data from the camera and microphone installed on the device or robot, and uses an emotion analysis engine (such as Microsoft Azure Emotion API) to analyze the user's facial expressions and voice to identify emotional state data, such as stress level and concentration.

[1800] Input: Real-time camera and microphone data from devices and robots

[1801] Output: Emotional state data

[1802] Step 5:

[1803] The server generates customized instructional materials based on learning style and emotional state.

[1804] The server retrieves appropriate teaching material templates from a database based on the results of the learning style analysis and emotional state data, and generates customized teaching materials.

[1805] Input: Learning style analysis results, emotional state data

[1806] Output: Customized teaching materials

[1807] Step 6:

[1808] The server provides the generated educational materials to the user's terminal.

[1809] The server sends the generated customized training materials to the user's device using a communication protocol (e.g., REST API).

[1810] Input: Customized instructional materials

[1811] Output: Instructional materials provided to the device

[1812] Step 7:

[1813] The user enters and submits feedback about the learning materials.

[1814] Users can input feedback about the learning materials through their terminals and send it to the server. The feedback includes the difficulty level of the materials, the level of understanding, and requests for improvement.

[1815] Input: Feedback on the learning materials

[1816] Output: Feedback information sent to the server

[1817] Step 8:

[1818] The server analyzes the feedback information and reflects it in the next generation of teaching materials.

[1819] The server stores the feedback information in a database and analyzes it, so that the feedback is reflected in the next teaching material provided.

[1820] Input: Feedback information

[1821] Output: Data for next teaching material generation that reflects the feedback

[1822] Step 9:

[1823] The server records and monitors the user's learning progress and periodically provides assessment tests.

[1824] The server records the user's learning progress in a database, generates assessment tests at regular intervals, and provides them to the terminal. The results of the assessment tests are analyzed to grasp the user's learning progress.

[1825] Input: Learning progress, assessment test

[1826] Output: Evaluation test results, learning progress data

[1827] Step 10:

[1828] The server recommends the next learning content based on the results of the evaluation test.

[1829] The server recommends what to learn next based on the results of the evaluation test and emotional state data, and the recommendations are provided to the user's device.

[1830] Input: Evaluation test results, emotional state data

[1831] Output: Recommended learning content

[1832] Step 11:

[1833] The server generates regular reports based on learning progress and results and distributes them to educators and parents.

[1834] The server extracts learning progress and results from the database and periodically generates reports, which are then sent to educators and parents via email or other means.

[1835] Input: Learning progress and achievement data

[1836] Output: Regular reports delivered to educators and parents

[1837] For example, enter data based on the following prompt:

[1838] Username: Suzuki Taro

[1839] Learning objective: Learn how to operate a new machine tool

[1840] Emotional state: High stress level, low concentration

[1841] Feedback: I find the content of the instruction difficult.

[1842] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 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.

[1843] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1844] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1845] [Fourth embodiment]

[1846] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1847] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1848] 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).

[1849] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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 control target 443 are also connected to the bus 52.

[1850] 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.

[1851] 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).

[1852] 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.

[1853] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1854] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, 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.

[1855] 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.

[1856] 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.

[1857] In the robot 414, the processor 46 performs the reception output process. 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.

[1858] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1859] This invention relates to an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, and provides these to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[1860] 1. User Registration and Login

[1861] User: A new user enters the required information into the device to register an account, including name, email address, and password, and submits it.

[1862] Server: Receives the entered information and stores it in the database. Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[1863] 2. Collecting learning style questionnaires

[1864] User: Initiates a survey on their device about their learning style and learning goals, including their preferred learning modality (visual, auditory, tactile, etc.) and the specific content they want to learn.

[1865] Server: Stores the received survey responses in a database and analyzes the user's overall learning style.

[1866] 3. Learning Style Analysis

[1867] Server: The survey results stored in the database are input into a machine learning algorithm to determine the user's learning style. Based on this determination, the type of learning material that is best suited to the user is selected.

[1868] 4. Generation of multimedia teaching materials

[1869] Server: Retrieves learning material templates from a database based on the identified learning style, including text, images, videos, and interactive quizzes.

[1870] Server: Based on the acquired templates, it generates original multimedia teaching materials tailored to the user's level and interests.

[1871] 5. Distribution of teaching materials

[1872] Server: Sends the generated learning materials to the user's device. For example, a user learning calculus can be provided with visual video learning materials and text with illustrations.

[1873] Terminal: Displays the received learning materials to the user.

[1874] 6. Collecting and analyzing feedback

[1875] Users: Enter and submit feedback on the learning materials they have studied, including an assessment of the difficulty and level of comprehension of the material.

[1876] Server: Receives and analyzes feedback and reflects it in the next generation of teaching materials.

[1877] 7. Progress Management and Evaluation

[1878] Server: Records the user's learning progress and provides periodic assessment tests as needed. For example, it generates and delivers short quizzes assessing basic concepts of calculus to the user.

[1879] Device: Receives the assessment test, presents it to the user, and sends the answers to the server, which analyzes them and recommends the next course of study.

[1880] 8. Generate and share reports

[1881] Server: Generates periodic reports based on learning progress and results, including information such as how much content the user has understood, how long it took, and what content they should learn next.

[1882] Server: Delivers the generated reports to educators and parents via email or other means.

[1883] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles, improves learners' motivation, and reduces the burden on educators.

[1884] The processing flow will be explained below.

[1885] Step 1:

[1886] A user enters information such as name, email address, and password into a terminal to create a new account.

[1887] Step 2:

[1888] The terminal transmits the input information to the server.

[1889] Step 3:

[1890] The server stores the received information in a database and notifies the user that the registration was successful.

[1891] Step 4:

[1892] The user starts a survey on their device about their learning style and learning goals.

[1893] Step 5:

[1894] The terminal receives the survey questions from the server and presents them to the user.

[1895] Step 6:

[1896] The user answers the survey questions and enters the answers into the terminal.

[1897] Step 7:

[1898] The terminal transmits the user's questionnaire responses to the server.

[1899] Step 8:

[1900] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze learning styles.

[1901] Step 9:

[1902] The server uses machine learning algorithms to determine the user's learning style.

[1903] Step 10:

[1904] Based on the learning style determined by the server, an appropriate teaching material template is retrieved from the database.

[1905] Step 11:

[1906] The server generates multimedia teaching materials for each user based on the template.

[1907] Step 12:

[1908] The server transmits the generated teaching materials to the user's terminal.

[1909] Step 13:

[1910] The terminal displays the received educational material to the user.

[1911] Step 14:

[1912] Users access the learning materials and enter feedback into a terminal after each session.

[1913] Step 15:

[1914] The terminal transmits the user's feedback information to the server.

[1915] Step 16:

[1916] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[1917] Step 17:

[1918] The server records the user's learning progress in a database and updates it as the learning progresses.

[1919] Step 18:

[1920] The server periodically generates assessment tests and distributes them to users.

[1921] Step 19:

[1922] The terminal presents an evaluation test to the user, who answers it.

[1923] Step 20:

[1924] The terminal transmits the user's answer results to the server.

[1925] Step 21:

[1926] The server analyzes the results of the assessment test and determines the user's level of understanding.

[1927] Step 22:

[1928] Recommends what the server should learn next.

[1929] Step 23:

[1930] The server generates reports based on learning progress and results and distributes them to educators and parents.

[1931] That's all.

[1932] Example 1

[1933] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1934] Conventional educational support systems do not provide learning materials that match the individual learning style of each user, making it difficult to achieve effective learning. Furthermore, the process for effectively collecting and analyzing users' learning progress and feedback and reflecting it in the next set of learning materials is insufficient, which can lead to a decline in the quality of learning. Furthermore, the provision of appropriate progress reports to educators and parents is also insufficient, making it necessary to strengthen the learning support system.

[1935] 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.

[1936] In this invention, the server includes a means for generating learning materials using templates based on the user's learning style, a means for customizing specific learning content according to the templates, and a means for analyzing user feedback through text analysis. This allows for the generation and provision of learning materials suited to the user's individual learning style, effectively incorporating feedback, and accurately managing progress. It also enables the provision of reliable progress reports to educators and parents.

[1937] "User" refers to an individual who uses the system to learn.

[1938] "Learning style" refers to the method or format in which a user learns most effectively, for example, visual, auditory, or tactile learning styles.

[1939] "Learning Objective" refers to a specific learning outcome or skill that a user wants to achieve.

[1940] "Server" refers to a computer system for receiving input information from users, processing and analyzing data, and generating and providing results.

[1941] "Information" refers to data provided by the user, specifically including learning style, learning goals, and feedback content.

[1942] A "template" is a framework that provides a basic structure and format for generating learning materials, allowing for efficient and consistent generation of learning materials.

[1943] "Multimedia learning materials" refers to learning content that combines multiple media formats, such as text, images, video, audio, and interactive quizzes.

[1944] "Feedback" refers to the opinions and ratings provided by users after using learning materials, which helps improve the quality and content of the materials.

[1945] "Text analysis" refers to the process of analyzing user feedback using natural language processing techniques to extract meaning.

[1946] "Study progress" refers to an indicator that shows how much progress a user has made as they progress through their studies. Specifically, it includes the degree of understanding and completion of the learning content.

[1947] An "assessment test" refers to a test that allows a user to check what they have learned and evaluate their level of understanding.

[1948] "Report" refers to a report summarizing a user's learning progress and results, including the user's level of understanding of the learning content, study time, next study steps, etc.

[1949] "Educator" refers to a person who supports and guides users in their learning. Specifically, this includes teachers and coaches.

[1950] "Guardian" refers to a parent or guardian who is responsible for supervising the user's learning status if the user is a minor.

[1951] MODE FOR CARRYING OUT THE INVENTION

[1952] In the educational support system of the present invention, a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, which are then provided to the user's terminal. This system can manage the user's learning progress and collect feedback to reflect in the generation of the next learning material.

[1953] User Registration and Login

[1954] User: A new user enters the required information, such as their name, email address, and password, into the terminal and submits it. For example, to create an account, Taro Tanaka enters tanaka@example.com and the password he set, and submits it.

[1955] Server: Receives the entered information and stores it in a database (specifically, MySQL, etc.). Existing users log in by entering their email address and password into the terminal. The server verifies the authentication information and issues a session ID.

[1956] Collecting learning style questionnaires

[1957] User: Completes a questionnaire on their device about their learning style and goals. For example, the user might answer "I prefer visual learning materials" and specifically state "I want to learn the basics of calculus."

[1958] Server: Stores the received survey responses in a database and adds them to a dataset for analysis.

[1959] Learning Style Analysis

[1960] Server: Analyzes the learning style answers stored in the database using Python and machine learning libraries such as scikit-learn. Specifically, the answers are extracted as features and a clustering algorithm (e.g., K-means) is applied to determine the user's learning style.

[1961] Server: The results of the assessment are stored in a database, and this information is used to prepare data for selecting the most appropriate learning materials.

[1962] Multimedia teaching material generation

[1963] Server: Retrieves learning material templates from the database that correspond to the identified learning style. For example, selects video or infographic templates for a user who prefers "visual learning materials."

[1964] Server: Generates unique multimedia learning materials based on templates, tailored to the level and interests of specific users. For example, it creates learning materials including videos and exercises on the theme of "Fundamentals of Calculus."

[1965] Distribution of teaching materials

[1966] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (such as the URL of the video file or text file) via HTTP request.

[1967] Terminal: Analyzes the received HTTP response and creates a screen that displays links and data for the learning materials. Users can view or download the learning materials through the terminal.

[1968] Collecting and analyzing feedback

[1969] User: Enter feedback about the learning material (e.g., "The explanation in the video was easy to understand, but the questions were difficult") into the input form on the device and click the submit button.

[1970] Server: Receives feedback and stores it in a database. Analyzes the feedback data using text analysis tools (such as NLTK) to help improve the quality of the learning materials.

[1971] Progress management and evaluation

[1972] Server: Retrieves user learning progress from a database and periodically generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a multiple choice quiz to assess "Fundamental Concepts of Calculus."

[1973] Terminal: Presents the assessment test received from the server to the user, collects the user's answers, and sends them to the server.

[1974] Server: Analyzes the user's answers and generates feedback to recommend what to study next.

[1975] Generate and share reports

[1976] Server: Generates periodic reports based on learning progress and results. For example, it creates a PDF report summarizing the user's learning time, level of understanding, and what they should learn next.

[1977] Server: Delivers the generated reports to educators and parents via email (using the SMTP protocol) or other formats.

[1978] Prompt Sentence Examples

[1979] Describe the process for generating videos and text about the basics of calculus for new user Taro Tanaka, who creates an account and responds to a survey indicating that he prefers visual learning materials.

[1980] By following the above steps, the educational support system of the present invention can provide users with an optimal learning environment and maximize learning effectiveness. It also strengthens the educational support system by providing educators and parents with accurate learning progress information.

[1981] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1982] The flow of this system's program processing

[1983] Step 1: User Registration and Login

[1984] Input: The user enters their name, email address, and password.

[1985] Operation:

[1986] User: Enters the required information into the form on the device and clicks the submit button.

[1987] Terminal: Generates a request to send the entered data to the server.

[1988] Server: Receives the request and validates the data. If it's a new user, saves the information to the database and returns a success message. If it's an existing user, checks the authentication information and, if it matches, creates a session ID and returns a successful login message.

[1989] Output: Account creation success message for new users, login success message and session ID for existing users.

[1990] Step 2: Collect a learning style questionnaire

[1991] Input: Users fill out a questionnaire about their learning style and learning goals.

[1992] Operation:

[1993] Users: Complete and submit a survey form with information about their learning style and learning goals.

[1994] Terminal: Generates a request that collects the user's answers and sends them to the server.

[1995] Server: Receives survey responses and stores them in a database.

[1996] Output: Survey responses are saved in a database.

[1997] Step 3: Analyze your learning styles

[1998] Input: Survey results stored in a database.

[1999] Operation:

[2000] Server: Retrieves survey results from the database and analyzes them using machine learning algorithms. Specifically, it extracts learning styles as features and performs clustering (e.g., K-means).

[2001] Server: Based on the analysis results, the user's learning style is determined and the results are stored in a database.

[2002] Output: The learning style assessment results are stored in a database.

[2003] Step 4: Creating multimedia materials

[2004] Input: Learning style assessment results and template database.

[2005] Operation:

[2006] Server: Obtains learning material templates corresponding to learning styles from a template database.

[2007] Server: Based on the acquired templates, the server generates customized multimedia teaching materials tailored to the user's level and learning goals. Specifically, it uses video editing software and a text editor.

[2008] Output: Customized multimedia teaching materials.

[2009] Step 5: Distributing the materials

[2010] Input: customized multimedia teaching materials.

[2011] Operation:

[2012] Server: Sends the generated learning materials to the user's device. Specifically, it sends the learning material data (video files and text files) via HTTP requests.

[2013] Terminal: Displays the received educational material data. Specifically, displays the educational material using a video player or text viewer.

[2014] Output: The educational material displayed on the user's device.

[2015] Step 6: Collect and analyze feedback

[2016] Input: User feedback.

[2017] Operation:

[2018] User: Enter your feedback about the learning material and click the submit button.

[2019] Terminal: Generates a request to send feedback data to the server.

[2020] Server: Receives feedback, stores it in a database, and analyzes it using natural language processing tools (e.g., NLTK).

[2021] Output: The analysis data of the feedback results is saved.

[2022] Step 7: Progress Management and Evaluation

[2023] Input: User's learning progress data.

[2024] Operation:

[2025] Server: Retrieves user learning progress data from a database and generates assessment tests. For example, it uses Python to run a quiz generation algorithm and create a test to assess "Fundamental Concepts of Calculus."

[2026] Terminal: Presents the assessment test received from the server to the user. Sends the user's answers to the server.

[2027] Server: Analyzes the user's answers and recommends the next learning content.

[2028] Output: Learning progress data and assessment results.

[2029] Step 8: Generate and share the report

[2030] Input: Learning progress data and assessment results.

[2031] Operation:

[2032] Server: Generates reports based on learning progress and results. Specifically, it creates reports in PDF format and summarizes the user's learning time and level of understanding.

[2033] Server: Generates reports and delivers them to educators and parents via email.

[2034] Output: Learning progress reports sent to educators and parents.

[2035] (Application example 1)

[2036] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2037] Upgrading the skills of factory workers and training them in robot operation and maintenance is extremely important, but the lack of customized learning materials tailored to each worker's learning style and progress results in a decline in learning efficiency.In addition, there is an insufficient system for reflecting feedback on learning outcomes in the creation of next learning materials, which means that worker motivation and skill improvement are not being fully achieved.

[2038] 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.

[2039] In this invention, the server includes: means for a user to input information regarding learning styles and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the customized learning materials to be displayed in visual or multimedia format, including robot operation and maintenance training for factory workers; means for collecting feedback from the user and reflecting it in the generation of next learning materials; means for the server to record and monitor the user's learning progress and periodically provide assessment tests; means for the server to recommend next learning content based on the results of the assessment tests; and means for the server to generate regular reports based on the learning progress and results and distribute them to educators and managers. This makes it possible to improve the learning efficiency of factory workers and provide optimal training materials for each worker.

[2040] "Users" refer to factory workers who use the system to learn.

[2041] "Learning style" refers to the format or method in which a user most effectively learns, and includes different formats such as visual, auditory, and tactile.

[2042] A "learning goal" refers to a specific skill or knowledge acquisition goal that a user is trying to achieve.

[2043] "Means for inputting information" refers to the interface that allows users to provide information about their learning style and learning goals to the system.

[2044] "Server" refers to a computer system that collects information input by users, analyzes it, and generates and distributes customized educational materials.

[2045] "Multimedia teaching materials" refers to teaching materials that combine multiple media formats, such as text, video, and audio.

[2046] "Feedback" refers to the opinions, evaluations, and reports of understanding provided by users after learning.

[2047] "Assessment Test" refers to a test provided to assess a user's learning progress and level of understanding.

[2048] "Training" refers to a series of learning activities that allow a user to acquire specific skills or knowledge.

[2049] "Report" refers to a report summarizing a user's learning progress and results.

[2050] The term "administrator" refers to a person who monitors the learning status of users and provides guidance.

[2051] "Generated learning materials" refers to learning content that is customized and created by the server according to the user's learning style and goals.

[2052] "Visual format" refers to a format that uses images or pictures to present information.

[2053] "Factory workers" refer to employees who require training to operate machines and robots in factories.

[2054] This invention is an educational system for effectively supporting training in improving the skills of factory workers and robot operation. The system generates multimedia learning materials customized based on the user's learning style and learning goals, and provides them to the user's terminal.

[2055] First, users access the system using a device such as a smartphone or head-mounted display. They use an interface to input their learning style and learning goals, providing information such as whether they prefer visual or auditory learning. This allows the server to classify and analyze the user's learning style.

[2056] The server uses machine learning algorithms to analyze the user's learning style based on the input information. Specifically, it uses "scikit-learn" and "TensorFlow" to optimally determine the user's learning style and generate learning materials accordingly. The generated learning materials include visual, auditory, and tactile multimedia materials.

[2057] The server stores the generated customized teaching materials in the cloud using AWS S3 and YouTube API, and delivers them to the user's device. Users can visually check the content and learn robot operation and maintenance procedures.

[2058] After studying, users send feedback to the system. This feedback includes their evaluation and opinions of the learning content. The server analyzes the feedback using natural language processing libraries such as "NLTK" and reflects the analysis results in the next generation of learning materials. This feedback allows for continuous improvement of the quality of the learning materials and the user experience.

[2059] The server also records the user's learning progress and provides regular assessment tests. The results of the assessment tests are analyzed using Jupyter Notebook and Pandas, and recommendations on what the user should learn next are provided to the user. Periodic reports are also generated and distributed to educators and administrators. This allows for effective management of worker skill development.

[2060] Examples:

[2061] For example, if a factory worker is trying to learn how to operate a new robot, the system allows him to receive visual video training on his smartphone, adapting the training to his optimal learning style and applying it to the actual task.

[2062] Example prompt sentence:

[2063] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[2064] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2065] Step 1:

[2066] Users access the system using a smartphone or head-mounted display, where they input information about their learning style and goals, including learning style preferences (visual, auditory, tactile, etc.) and the specific skills or knowledge they wish to learn. The input data is then sent to the server.

[2067] Step 2:

[2068] The server receives information about the user's learning style and learning goals. The received data is fed into a machine learning algorithm using "scikit-learn" or "TensorFlow" to analyze the user's learning style. As a result of the analysis, the optimal learning material format for the user is determined.

[2069] Step 3:

[2070] The server generates customized multimedia teaching materials based on the analysis results. At this stage, depending on the specified teaching material format (e.g., materials that make extensive use of visual videos or illustrations), a teaching material template is retrieved from "AWS S3" or "YouTube API" and individual teaching materials are created based on that. The generated teaching materials are stored in the cloud.

[2071] Step 4:

[2072] The server delivers the customized learning materials to the user's device. The user uses a smartphone or head-mounted display to view and study the delivered materials. The learning materials are provided in multimedia formats such as video, text, and images, supporting both visual and tactile learning styles.

[2073] Step 5:

[2074] After completing the study, the user inputs and sends feedback to the system. The feedback includes the level of understanding of the material, the difficulty level, and areas for improvement. The input feedback is sent to the server. The server analyzes the feedback using a natural language processing library such as "NLTK" and saves the analysis results as a new dataset.

[2075] Step 6:

[2076] The server uses the user's feedback information to generate the next set of learning materials. During this process, a machine learning algorithm is used to evaluate the user's learning progress and prepare to generate new customized learning materials according to the user's next learning goal. The server also periodically records the user's learning progress and provides assessment tests. These assessment tests are also automatically generated by the server and delivered to the user's device.

[2077] Step 7:

[2078] The server receives and analyzes the results of the assessment test. Based on the assessment results, it recommends what to learn next and generates new learning materials as needed. It also generates regular reports reflecting the user's progress and distributes them to educators and managers. This allows for effective management of the skill development of factory workers.

[2079] Example prompt sentence:

[2080] "The learner has provided information about his learning style and learning goals. Please generate and provide visual multimedia learning materials for robot operation based on his learning style. Specifically, he prefers visual learning, so he will need materials that make heavy use of videos and illustrations. Please provide the materials in the format that he finds most understandable."

[2081] 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.

[2082] This invention combines an emotion engine with an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia teaching materials based on that information, which are then provided to the user's terminal. The program processing of this system is explained below in natural language, and specific examples are also provided.

[2083] 1. User Registration and Login

[2084] A user enters information such as name, email address, and password into a terminal to create a new account.

[2085] The terminal transmits the input information to the server.

[2086] The server stores the received information in a database and notifies the user that the registration was successful.

[2087] 2. Collecting learning style questionnaires

[2088] The user starts a survey on their device about their learning style and learning goals.

[2089] The terminal receives the survey questions from the server and presents them to the user.

[2090] The user answers the survey questions and enters the answers into the terminal.

[2091] The terminal transmits the user's questionnaire responses to the server.

[2092] 3. Learning Style Analysis

[2093] The server stores the survey response data in a database and analyzes the user's learning style using machine learning algorithms.

[2094] 4. Utilizing the Emotion Engine

[2095] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone, using, for example, facial expression analysis and voice analysis.

[2096] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[2097] 5. Generation of multimedia teaching materials

[2098] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[2099] The server generates multimedia teaching materials for each user based on the template.

[2100] 6. Distribution of teaching materials

[2101] The server transmits the generated teaching materials to the user's terminal.

[2102] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[2103] 7. Collecting and analyzing feedback

[2104] The user inputs and sends feedback about the learning materials from the terminal.

[2105] The terminal transmits the user's feedback information to the server.

[2106] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[2107] The server can also incorporate emotional states to achieve even more precise customization.

[2108] 8. Progress Management and Evaluation

[2109] The server records the user's learning progress and periodically provides assessment tests as needed, for example generating and delivering short quizzes assessing basic concepts of calculus to the user.

[2110] The terminal presents the evaluation test to the user and transmits the answer results to the server.

[2111] The server analyzes the results of the assessment test and determines the user's level of understanding.

[2112] 9. Recommended next learning content

[2113] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will move on to the next level, and if the emotional state indicates anxiety or stress, it will recommend relaxing content.

[2114] 10. Generate and share reports

[2115] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[2116] The server delivers the generated reports to educators and parents via email or other formats.

[2117] Through the above process, the educational support system of the present invention provides effective education tailored to individual learning styles and emotional states, improving learners' motivation and reducing the burden on educators.

[2118] The processing flow will be explained below.

[2119] Step 1:

[2120] A user enters information such as name, email address, and password into a terminal to create a new account.

[2121] Step 2:

[2122] The terminal transmits the input information to the server.

[2123] Step 3:

[2124] The server stores the received information in a database and notifies the user that the registration was successful.

[2125] Step 4:

[2126] The user starts a survey on their device about their learning style and learning goals.

[2127] Step 5:

[2128] The terminal receives the survey questions from the server and presents them to the user.

[2129] Step 6:

[2130] The user answers the survey questions and enters the answers into the terminal.

[2131] Step 7:

[2132] The terminal transmits the user's questionnaire responses to the server.

[2133] Step 8:

[2134] The server stores the survey response data in a database and inputs it into a machine learning algorithm to analyze the user's learning style.

[2135] Step 9:

[2136] The server determines the user's learning style and stores it in a database.

[2137] Step 10:

[2138] The server uses an emotion engine to analyze data from the user's webcam and microphone and recognize their emotional state in real time.

[2139] Step 11:

[2140] The server stores the emotional state data in a database and reflects it in further customizing learning styles.

[2141] Step 12:

[2142] Based on the learning style and emotional state determined by the server, an appropriate learning material template is retrieved from a database.

[2143] Step 13:

[2144] The server generates multimedia teaching materials for each user based on the template.

[2145] Step 14:

[2146] The server transmits the generated teaching materials to the user's terminal.

[2147] Step 15:

[2148] The device then displays the received educational material to the user, for example providing diagrams and infographics for visual learners, or displaying messages to slow down or encouragement if the emotion engine detects stress.

[2149] Step 16:

[2150] Users access the learning materials and enter feedback into a terminal after each session.

[2151] Step 17:

[2152] The terminal transmits the user's feedback information to the server.

[2153] Step 18:

[2154] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[2155] Step 19:

[2156] The server records the user's learning progress in a database and monitors the progress in real time.

[2157] Step 20:

[2158] The server periodically generates assessment tests and distributes them to users.

[2159] Step 21:

[2160] The terminal presents an evaluation test to the user, who answers it.

[2161] Step 22:

[2162] The terminal transmits the user's answer results to the server.

[2163] Step 23:

[2164] The server analyzes the results of the assessment test and determines the user's level of understanding.

[2165] Step 24:

[2166] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[2167] Step 25:

[2168] The server periodically generates reports based on the user's learning progress and achievements, including how much content the user has understood, how long it took, what content the user should learn next, and how their emotional state has affected the learning.

[2169] Step 26:

[2170] The server delivers the generated reports to educators and parents via email or other formats.

[2171] That's all.

[2172] Example 2

[2173] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2174] Conventional educational support systems are not sufficiently customized based on the user's learning style and goals, and are unable to reflect the user's emotional state in real time while learning. As a result, learning efficiency declines and it becomes difficult to maintain user motivation.

[2175] 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.

[2176] In this invention, the server includes means for a user to input information about learning style and learning goals, means for the server to analyze the learning style based on the input information, means for the server to generate customized multimedia learning materials based on the analysis results, means for the server to analyze the user's emotional state using an emotion engine and reflect the results in further customization of the learning style, and means for the server to adjust the learning pace and learning material content based on the user's emotional state, thereby making it possible to provide a highly customized learning experience based on the user's learning style and real-time emotional state.

[2177] A "user" is a person who uses the system, who inputs information about their learning style and learning goals, and who receives learning materials.

[2178] A "terminal" is an electronic device operated by a user, used to input information, display educational materials, etc. Examples include PCs, smartphones, and tablets.

[2179] A "server" is a computer system that receives data sent by users, processes and analyzes it, and generates and distributes appropriate educational materials.

[2180] "Means for inputting information" refers to the interface or method by which users input information about their learning styles and learning goals into the system. Examples include web forms and questionnaire-style input screens.

[2181] "Means for analyzing learning styles" refers to the process by which the server uses machine learning algorithms and statistical analysis to identify the optimal learning method based on the information the user has entered.

[2182] "Multimedia learning materials" are learning resources that combine multiple media formats, such as text, images, audio, and video.

[2183] An "emotion engine" is software or an algorithm for analyzing a user's emotional state, recognizing the user's emotions in real time through facial expression analysis, voice analysis, etc.

[2184] "Means for adjusting learning pace and learning material content" refers to a function that flexibly changes the learning content and its progress speed based on the user's emotional state and learning style. For example, it includes slowing down the pace for users who are under stress.

[2185] "Means for collecting feedback" are methods by which users can input their opinions about their learning experience and learning materials into the system. Examples include survey forms and rating systems.

[2186] "Means for recording learning progress" refers to a method for tracking a user's learning activities and accumulating data on their progress. For example, a learning management system (LMS) would be an example of this.

[2187] A "means for providing assessment tests" is a method for generating and delivering tests to users to assess their understanding or progress. Examples include online quizzes and exams.

[2188] "Means for recommending learning content" is a process that selects and suggests what the user should learn next based on the results of an assessment test and their emotional state.

[2189] A "means for generating a report" is a method for organizing a user's learning progress and achievements and creating a report in a visual format (e.g., PDF).

[2190] "Means of delivery to educators and parents" refers to the methods by which the generated reports are delivered to others involved in education, primarily via email or online portals.

[2191] This is an educational support system in which a user inputs information about their learning style and learning goals, and a server analyzes and generates customized multimedia learning materials based on that information, and provides them to the user's terminal. Furthermore, an emotion engine is used to analyze the user's emotional state in real time, dynamically customizing the learning experience.

[2192] User Registration and Login

[2193] To create a new account, a user enters information such as name, email address, and password into the device. The device sends the information to the server, which stores it in a database. The user then receives a success notification.

[2194] Collecting learning style questionnaires

[2195] The user starts a questionnaire about their learning style and learning goals on their device. The device receives the questionnaire questions from the server and presents them to the user. The user answers the questionnaire, and the answers are sent from the device to the server.

[2196] Learning Style Analysis

[2197] The server stores the survey responses in a database, then uses machine learning algorithms to analyze the user's learning style, for example, by using Scikit-learn to perform clustering and identify the user's learning style.

[2198] Utilizing the Emotion Engine

[2199] The server uses the user's webcam and microphone to recognize the user's emotional state during learning. For example, it performs facial expression and voice analysis using OpenCV. The server analyzes the emotional state data and reflects it in further customizing the learning style.

[2200] Multimedia teaching material generation

[2201] The server retrieves appropriate learning material templates from a database based on the identified learning style and emotional state, and then generates personalized multimedia learning materials using Adobe Creative Suite and HTML5.

[2202] Distribution of teaching materials

[2203] The generated learning materials are sent from the server to the user's device, which then displays them to the user. For example, the device can provide infographic-based learning materials to users who prefer visual learning, and change the pace if the emotion engine detects stress.

[2204] Collecting and analyzing feedback

[2205] Users input and submit feedback about learning materials from their devices. The devices then send the feedback information to the server, which stores it in a database. The analyzed feedback information is reflected in the next generation of learning materials.

[2206] Progress management and evaluation

[2207] The server records the user's learning progress and provides periodic assessment tests as needed. The device presents the assessment tests to the user and sends the results to the server. The server analyzes the results and determines the user's level of understanding.

[2208] Recommended next learning content

[2209] The server recommends what to learn next based on the test results and emotional state data. For example, if the test score is high, it will suggest moving on to the next level, and if the emotional state indicates anxiety or stress, it will suggest relaxing content.

[2210] Generate and share reports

[2211] The server periodically generates reports based on learning progress and results, including information on how much content the user understood, how long it took, what content they should learn next, how their emotional state affected the learning, etc. The generated reports are sent to educators and parents via email or other means.

[2212] Examples of concrete examples and prompts

[2213] For example, if a busy working adult learner sets the goal of "studying efficiently after work," the system can provide appropriate infographics or short video materials while monitoring the learner's emotional state in real time. If the emotion engine detects fatigue or stress, it will display relaxing content or inspirational messages.

[2214] Example prompt sentence:

[2215] "Provide instructional materials with easy-to-understand visual illustrations. Adjust your pace to slow down if you experience fatigue."

[2216] In this way, a highly customized learning experience is provided that is tailored to individual learning styles and emotional states.

[2217] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2218] Step 1:

[2219] Entering user information

[2220] A user enters information such as name, email address, and password into a terminal to create a new account.

[2221] Input: Name, Email Address, Password

[2222] Output: Data entered into the input form on the terminal

[2223] What happens: A user fills out a web form and clicks the "Submit" button.

[2224] Step 2:

[2225] Sending input information

[2226] The terminal transmits the input information to the server.

[2227] Input: User information entered into the terminal

[2228] Output: User information sent to the server

[2229] Specific operation: The device sends an HTTP request to the server using the POST method.

[2230] Step 3:

[2231] Saving user information

[2232] The server stores the received information in a database and notifies the user that the registration was successful.

[2233] Input: HTTP request containing user information

[2234] Output: User information stored in the database

[2235] Specific operation: The server inserts the user information into a database (e.g., MySQL) and returns a success response.

[2236] Step 4:

[2237] Survey begins

[2238] The user starts a survey on their device about their learning style and learning goals.

[2239] Input: User requests to start a survey

[2240] Output: Survey start request to the server

[2241] Specific behavior: The user clicks the "Start Survey" button.

[2242] Step 5:

[2243] Presenting the survey questions

[2244] The terminal receives the survey questions from the server and presents them to the user.

[2245] Input: Survey questions sent from the server

[2246] Output: The survey question that is displayed to the user

[2247] Specific operation: The device sends an HTTP request to the server, receives survey questions in JSON format, and displays the questions in an HTML form.

[2248] Step 6:

[2249] Entering survey answers

[2250] The user answers the survey questions and enters the answers into the terminal.

[2251] Input: User survey responses

[2252] Output: Survey responses entered into the device

[2253] Specific behavior: A user enters answers into a web form and clicks the "Submit" button.

[2254] Step 7:

[2255] Submit your answer

[2256] The terminal transmits the user's questionnaire responses to the server.

[2257] Input: Survey answers entered into the device

[2258] Output: Survey responses sent to the server

[2259] Specific operation: The device sends an HTTP request to the server using the POST method.

[2260] Step 8:

[2261] Saving response data

[2262] The server stores the survey response data in a database.

[2263] Input: Survey responses sent to the server

[2264] Output: Survey responses stored in a database

[2265] Specific operation: The server inserts the response data into the database.

[2266] Step 9:

[2267] Learning Style Analysis

[2268] The server uses machine learning algorithms to analyze the user's learning style.

[2269] Input: Survey responses stored in the database

[2270] Output: Analyzed learning styles

[2271] What it does: The server uses Scikit-learn to perform clustering and regression analysis, for example, using K-means clustering to find clusters of learning styles.

[2272] Step 10:

[2273] Acquiring emotion data

[2274] The server uses an emotion engine to recognize the user's emotional state during learning via input devices such as the user's webcam and microphone.

[2275] Input: User camera video and audio data

[2276] Output: Parsed emotional state data

[2277] Specific operation: The server acquires camera images using WebRTC and performs facial expression and voice analysis using OpenCV.

[2278] Step 11:

[2279] Emotional Data Analysis

[2280] The server analyzes the emotional state data and reflects it in further customizing the learning style.

[2281] Input: Parsed emotional state data

[2282] Output: Customized learning style data

[2283] Specific operation: The server updates the learning style data in the database based on the analysis results.

[2284] Step 12:

[2285] Obtaining teaching material templates

[2286] The server retrieves appropriate learning material templates from a database based on the determined learning style and emotional state.

[2287] Input: Learning style data, emotional state data

[2288] Output: Teaching material template data

[2289] Specific operation: The server uses an SQL query to select the teaching material template.

[2290] Step 13:

[2291] Customizing teaching materials

[2292] The server generates multimedia teaching materials for each user based on the template.

[2293] Input: Teaching material template data, user's learning style and emotional state

[2294] Output: Customized multimedia teaching materials

[2295] Specific operation: The server creates teaching materials based on templates and visually customizes them using HTML5 and CSS.

[2296] Step 14:

[2297] Sending educational materials

[2298] The server transmits the generated teaching materials to the user's terminal.

[2299] Input: Customized multimedia materials

[2300] Output: The learning materials delivered to the user's device

[2301] Specific operation: The server returns the learning material data (e.g., PDF or HTML) in an HTTP response.

[2302] Step 15:

[2303] Display of teaching materials

[2304] The terminal displays the received educational material to the user.

[2305] Input: Delivered multimedia materials

[2306] Output: The material displayed to the user

[2307] Specific operation: The device displays the contents of the teaching material in a browser.

[2308] Step 16:

[2309] Enter your feedback

[2310] The user inputs and sends feedback about the learning materials from the terminal.

[2311] Input: Feedback

[2312] Output: HTTP request with feedback data

[2313] What happens: A user fills out a feedback form and clicks the "Submit" button.

[2314] Step 17:

[2315] Send Feedback

[2316] The terminal transmits the user's feedback information to the server.

[2317] Input: User feedback

[2318] Output: Feedback information sent to the server

[2319] Specific operation: The device posts feedback data via an HTTP request.

[2320] Step 18:

[2321] Feedback storage and analysis

[2322] The server receives and analyzes the feedback information and reflects it in the next generation of teaching materials.

[2323] Input: Feedback information sent to the server

[2324] Output: Parsed feedback information

[2325] Specific operation: The server stores the feedback data in a database and uses an analysis algorithm to extract areas for improvement.

[2326] Step 19:

[2327] Record your learning progress

[2328] The server records the user's learning progress.

[2329] Input: User's learning progress data

[2330] Output: Learning progress data stored in a database

[2331] Specific operation: The server inserts or updates the learning progress data into the database.

[2332] Step 20:

[2333] Providing assessment tests

[2334] The server provides evaluation tests periodically as needed.

[2335] Input: Triggers the server's assessment test delivery routine

[2336] Output: Assessment test provided to the user

[2337] Specific behavior: The server sends the assessment test to the user, and the device displays the test.

[2338] Step 21:

[2339] Analyzing test results

[2340] The server analyzes the results of the assessment test and determines the user's level of understanding.

[2341] Input: User evaluation test result data

[2342] Output: Parsed test results

[2343] What happens: The server analyzes the test result data and runs an algorithm to assess the user's level of understanding.

[2344] Step 22:

[2345] Determining the recommendations

[2346] The server recommends what to learn next based on the results of the assessment test and emotional state data.

[2347] Input: Parsed test results, emotional state data

[2348] Output: Suggested next steps

[2349] Specific operation: The server runs an algorithm to recommend the next learning content based on the analysis results and notifies the user.

[2350] Step 23:

[2351] Generate reports

[2352] The server periodically generates reports based on learning progress and results.

[2353] Input: User learning progress data, achievement data

[2354] Output: Generated report

[2355] Specific operation: The server creates a PDF report using a report generation tool such as Python.

[2356] Step 24:

[2357] Report distribution

[2358] The server delivers the generated reports to educators and parents via email or other formats.

[2359] Input: Generated report

[2360] Output: Reports delivered to educators and parents

[2361] What happens: The server sends the email using the SMTP protocol.

[2362] (Application example 2)

[2363] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2364] In modern factory training support systems, it is extremely important to provide customized instruction that takes into account the learning style and emotional state of each worker and adapts to their individual needs. However, conventional systems have difficulty capturing the learner's real-time emotional state, making it difficult to effectively manage learning progress and provide appropriate learning materials. This has resulted in poor improvement in workers' learning efficiency and motivation, and has placed a heavy burden on educators. To solve this problem, a system that can generate and deliver customized learning materials based on learning style and emotional state is needed.

[2365] 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.

[2366] In this invention, the server includes: means for a user to input information regarding learning style and learning goals; means for the server to analyze learning styles based on the input information; means for the server to generate customized multimedia learning materials based on the analysis results; means for the server to provide the generated learning materials to the user's terminal; means for recognizing and analyzing the user's emotional state in real time; means for generating customized teaching materials based on the emotional state; means for the server to record and monitor the user's learning progress and periodically provide evaluation tests; means for the server to recommend next learning content based on the results of the evaluation tests; means for the server to generate periodic reports based on the learning progress and results and distribute them to educato...

Claims

1. a means for users to input information about their learning styles and learning goals; A means for the server to analyze learning styles based on input information; means for the server to generate customized multimedia teaching materials based on the analysis results; A means for the server to provide the generated teaching materials to a user's terminal; A means of collecting user feedback and reflecting it in the next generation of teaching materials; A means for the server to record and monitor the user's learning progress and periodically provide assessment tests; A means for the server to recommend the next learning content based on the results of the assessment test; The server generates regular reports based on learning progress and results and distributes them to educators and parents. A system including:

2. 2. The system according to claim 1, wherein the server analyzes the user's feedback information and reflects it in the next generation of teaching materials.

3. 2. The system according to claim 1, wherein the server records and monitors the user's learning progress, periodically provides evaluation tests, and recommends the next learning content based on the evaluation results.

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