Learning management system, learning management method, and learning management program

Abstract

To allow a learner to perform an output process and an input process in learning in a well balanced manner.SOLUTION: A learning management system comprises a processor. In the learning management system, the processor receives input from a learner in an input step, determines whether to display an input screen for presenting knowledge information about predetermined learning contents to the learner or an output screen for displaying a question about the predetermined learning contents and receiving answer input from the learner on the basis of information input in the input step in a determination step, and displays either the input screen or the output screen on the basis of the determination in a display step.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a learning management system, a learning management method, and a learning management program.

Background Art

[0002] In learning, in addition to the output process of solving problems, an input process of accumulating knowledge in the brain by reading or listening to unfixed knowledge information is necessary. In order to efficiently perform learning, the output process and the input process need to be carried out with an appropriate balance. Therefore, there is a need for a technique that appropriately causes a learner to perform the output process and the input process.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] Patent Document 1 discloses a learning support system including a concealment management unit that conceals predetermined matters in learning content from a learner and exposes the concealed matters to the learner. The concealment management unit releases the concealment for a predetermined time based on the instruction of the learner to display at least a part of the learning content so that the learner can visually recognize it, and after the predetermined time has elapsed, sets the concealment again. Patent Document 2 discloses a system that outputs efficiency improvement information, which is information for improving the learning efficiency of a user, based on biological data during sleep.

[0005] An object of the present invention is to provide a learning management system that causes a learner to perform the output process and the input process in learning in a well-balanced manner.

Means for Solving the Problem

[0006] To achieve the above object, a learning management system according to one aspect of the present invention includes a processor. In the input step, the processor receives an input from a learner. In the discrimination step, based on the information acquired in the input step, it determines whether to display an input screen for presenting knowledge information regarding predetermined learning content to the learner or an output screen for displaying a problem regarding the predetermined learning content and receiving an answer input from the learner. In the display step, based on the determination, it displays either the input screen or the output screen.

[0007] The discrimination step is an instruction step of giving an instruction to an artificial intelligence to determine next whether to display the input screen or the output screen based on the information acquired in the input step. The artificial intelligence may have a learned model that takes the information input in the input step as an input and outputs information indicating either the input screen or the output screen.

[0008] The learned model may take, as inputs, the display time of the input screen and the display time of the output screen among the information acquired in the input step.

[0009] The learned model may further take, as an input, the knowledge information presented on the input screen or the information on the difficulty level of the problem displayed on the output screen.

[0010] The learned model may further take, as an input, the information on the answer input by the learner for the problem displayed on the output screen.

[0011] The pre-trained model may further take, as input, information on the input history input by the learner for the problems displayed on the output screen.

[0012] The pre-trained model is a model obtained by machine learning of one or more teaching materials. The artificial intelligence has a pre-trained model that takes the information acquired in the input step as input and outputs the knowledge information displayed on the input screen. In the determination step, when it is determined to display the input screen, an instruction to select or generate the knowledge information to be presented on the input screen may be given to the artificial intelligence.

[0013] The pre-trained model is a model obtained by machine learning of one or more teaching materials. The artificial intelligence has a pre-trained model that takes the information acquired in the input step as input and outputs the problems displayed on the output screen. In the determination step, when it is determined to display the output screen, an instruction to select or generate the problems to be presented on the output screen may be given to the artificial intelligence.

[0014] To achieve the above object, a learning management method according to another aspect of the present invention includes an input reception step of receiving an input from a learner by a computer, and based on the information acquired in the input reception step, determining whether to display an input screen for presenting knowledge information on a predetermined learning content to the learner or an output screen for displaying a problem on the predetermined learning content and receiving an answer input from the learner, and a display step of displaying either the input screen or the output screen based on the determination.

[0015] To achieve the above object, a learning management program according to still another aspect of the present invention causes a computer to execute an input reception step of receiving an input from a learner, and based on the information acquired in the input reception step, determines whether to display an input screen for presenting knowledge information regarding predetermined learning content to the learner or an output screen for displaying a problem regarding the predetermined learning content and receiving an answer input from the learner, and a display step of displaying either the input screen or the output screen based on the determination. Note that the computer program can be provided by being stored in various data-readable recording media, or can be provided so as to be downloadable via a network such as the Internet.

Advantages of the Invention

[0016] According to the present invention, it is possible to cause a learner to perform the output process and the input process in learning in a well-balanced manner.

Brief Description of the Drawings

[0017]

Figure 1

Figure 2

Figure 3

Figure 4

Embodiments for Carrying Out the Invention

[0018] Hereinafter, embodiments of a learning management system according to the present invention will be described with reference to the drawings.

[0019] ● Learning Management System (1) ● Figure 1 shows the configuration of the server device 10 according to an embodiment of the present invention. The server device 10 is an example of a learning management system. The server device 10 is a device that generates problems for learners, presents them to the learners, and generates the next problems according to the answers from the learners. The problems are for confirming or evaluating the understanding of the field to be learned. The problems are composed of one or more questions. Each question may be a so-called multiple-choice question that includes a plurality of options and allows the learner to make a selection input, or may be a descriptive question that requires an answer by inputting text or the like. Further, the server device 10 generates information indicating the knowledge that the learner should accumulate, that is, knowledge information, and presents it to the learner.

[0020] As shown in Figure 1, the server device 10 is configured to be communicable with a learner terminal 30 used by the learner via a network NW. The server device 10 may be composed of a hardware device, or some or all of its functions may be realized by a cloud computer. Further, each configuration of the server device 10 may be realized by an API (Application Programming Interface). In this embodiment, the mutual communication between the server device 10 and the learner terminal 30 is wireless, but some or all of the connections may be wired. Furthermore, the server device 10 may be composed of a plurality of hardware configurations. In this case, the plurality of hardware configurations may be connected by wire or wirelessly, and information may be transmitted and received between them.

[0021] ● Learner Terminal 30 The learner terminal 30 is a terminal operated by a learner, such as a smartphone, a tablet terminal, or a personal computer. The learner terminal 30 mainly constitutes a functional block including a display unit 31, an operation reception unit 32, and a communication processing unit 33 by a CPU (Central Processing Unit), a computer program executed by the CPU, a RAM (Random Access Memory) and a ROM (Read Only Memory) for storing the computer program and predetermined data, etc.

[0022] The display unit 31 is realized by a display or the like for outputting data. On the display unit 31, a problem for the learner received from the server device 10 is displayed.

[0023] The operation reception unit 32 is realized by a touch panel, a keyboard, a mouse, a microphone, or the like for inputting data. The operation reception unit 32 can input the field of the problem to be generated. Also, the operation reception unit 32 can input an answer to the problem.

[0024] The communication processing unit 33 is a processing unit that can execute data transmission and reception processing according to a predetermined protocol with the server device 10 via a network NW such as the Internet, and is realized by an application or a web browser or the like.

[0025] ● Server device 10 Here, the configuration of the server device 10 will be described with reference to FIG. 1. The server device 10 is an information processing device that executes learning management processing while exchanging data with the learner terminal 30 via the network NW. The server device 10 stores a problem database DB1 and a teaching material information database DB2. The problem database DB1 stores the phrases used in the problems displayed on the output screen described later. The teaching material information database DB2 stores the knowledge information displayed on the input screen described later. The teaching material information database DB2 stores the content of the teaching materials registered in advance by an administrator or the like. The server device 10 has a function of artificial intelligence (AI: Artificial Intelligence), and uses the information stored in the problem database DB1 to select and output the problems to be displayed on the output screen. Further, the server device 10 uses the function of artificial intelligence to select and output the knowledge information to be displayed on the input screen using the information stored in the teaching material information database DB2.

[0026] Note that the problem database DB1 may store the phrases used in the problem generation process. In this case, the artificial intelligence may be configured to generate and output problems using the information stored in the problem database DB1 and the teaching material information database DB2. Further, the teaching material information database DB2 may store the phrases used in the knowledge information generation process, and the artificial intelligence may be configured to generate and output knowledge information using the information stored in the teaching material information database DB2.

[0027] As shown in FIG. 1, the server device 10 mainly includes function blocks such as a display control unit 11, a storage control unit 12, an AI control unit 13, an artificial intelligence unit 14, and a communication processing unit 20, which are composed of a CPU (Central Processing Unit, an example of the processor in the claims), a computer program executed by the CPU, a RAM (Random Access Memory) that stores the computer program and predetermined data, a ROM (Read Only Memory), and the like.

[0028] The display control unit 11 executes a process for causing the learner terminal 30 to display a screen related to the learning management system. The display control unit 11 performs processes such as generation and transmission of an HTML (Hyper Text Markup Language) file, and causes the learner terminal 30 to display a web page as a learning screen that a learner views when learning in this system. Note that the display control unit 11 may perform processes such as generation and transmission of display data for an application for using the learning management system.

[0029] The display control unit 11 causes the learner terminal 30 to display a registration screen, an input screen, and an output screen for the learning field to be learned. The registration screen for the learning field may be displayed so that a plurality of learning field candidates can be selected, or free description may be accepted. The input screen is a screen that presents knowledge information related to predetermined learning content to the learner. The output screen is a screen that displays a problem related to predetermined learning content and accepts an answer input from the learner.

[0030] The storage control unit 12 is a functional unit that controls an appropriate storage device provided in the server device 10 and performs writing and reading of data. The storage control unit 12 stores, for example, the question information registered on the registration screen in the storage device. The storage control unit 12 may also store the history of questions presented to the learner, the answer history, and the history of correct / incorrect answers in association with the identification information of the learner in the storage device.

[0031] The AI control unit 13 is a functional unit that controls the input to the artificial intelligence unit 14. The AI control unit 13 mainly generates an instruction representing that, taking the information acquired by the learner via the learner terminal 30 as an input, it outputs information regarding the screen to be next displayed on the learner terminal 30. The instruction may be, for example, a prompt, or a programming language, or any appropriate format acceptable to the artificial intelligence unit 14. Note that the prompt is information for instructing the artificial intelligence unit 14 about the generated content, and is, for example, a character string described in natural language. The information acquired via the learner terminal 30 is, for example, information on the learning field input on the registration screen, the display time of the input screen on the learner terminal 30, or the display time of the output screen, the knowledge information presented on the input screen, or information on the difficulty level of the problems displayed on the output screen, the input history input by the learner on the output screen, and the like. The input history is, for example, information on the answers input by the learner to the problems displayed on the output screen, or the correction history of the answers, such as the change history of the options in a multiple-choice question.

[0032] The information regarding the screen to be next displayed on the learner terminal 30 is, for example, information indicating the nature of the next screen to be displayed, and more specifically, whether it is an input screen or an output screen. Further, the information regarding the screen may include the knowledge information displayed on the input screen or the problem information displayed on the output screen. According to such a configuration, based on the instruction generated by the AI control unit 13, the nature and content of the next screen to be displayed are output from the artificial intelligence unit 14.

[0033] The artificial intelligence unit 14 is an AI equipped with a language model such as a Transformer including BART (Bidirectional and Auto-regressive Transformer), BERT (Bidirectional Encoder Representations from Transformers), or GPT (Generative Pretrained Transformer, including GPT-1, GPT-2, GPT-3), especially a learning model such as a large language model (LLM). The learning model (also referred to as a machine learning model) refers to a learning model by a machine learning algorithm. Specific algorithms of machine learning include the nearest neighbor method, the naive Bayes method, decision trees, and support vector machines. In addition, deep learning (deep neural network learning) that uses a neural network to generate its own feature quantities and combined weight coefficients for learning is also included. The artificial intelligence unit 14 can appropriately apply the above algorithms.

[0034] The artificial intelligence unit 14 has a trained model obtained by machine learning of teaching materials. The teaching materials contain content related to a predetermined learning field and are so-called textbooks and reference books. The teaching materials are composed of, for example, any one or a combination of text data, image data, video data, and audio data. The teaching materials may be provided by an administrator or the like, or may include information collected from the Internet or the like. In addition, the trained model may be obtained by machine learning of a problem set presented to a learner. Instead of or in addition to this configuration, the artificial intelligence unit 14 may generate problems from the machine learning data of the teaching materials.

[0035] The learned model takes as input the learning status of the learner, more specifically, the information input into the learner terminal 30. For example, the learned model takes as input the display time of the input screen or the display time of the output screen. The learned model may further take as input the knowledge information presented on the input screen or the difficulty level information of the questions displayed on the output screen. The difficulty level of the knowledge information presented on the input screen may be, for example, an index input by the teaching material selector in association with the teaching material. Also, the difficulty level of the questions displayed on the output screen may be an index determined according to the correct answer rate of other learners. Furthermore, both the knowledge information and the difficulty level of the questions may be determined in consideration of information qualitatively input by the learner and / or other learners. The learned model may further take as input the answers input by the learner for the questions displayed on the output screen or the information on the input history of the answers. The learned model is trained as a deep reinforcement learning model with the correct answer rate, response time, and correctness of the answer as feature quantities, and the high correct answer rate of the learner as the reward. Also, the learned model may further use, as the reward, appropriate information indicating the learning level of the learner, such as test results or academic performance, separately input into the learning management system. In addition to obtaining appropriate learning data in advance, the learned model can also perform additional learning as appropriate.

[0036] The artificial intelligence unit 14 generates the knowledge information to be presented on the input screen or the questions to be presented on the output screen based on the instructions generated by the AI control unit 13, taking as input the information on the designated learning field. The artificial intelligence unit 14 may create sentences using the phrases stored in the question database DB1 and the teaching material information database DB2, or may select the information stored in the question database DB1 or the teaching material information database DB2.

[0037] The communication processing unit 20 is a processing unit that can perform data transmission and reception processing according to a predetermined protocol with the learner terminal 30 via a network NW such as the Internet. The communication processing unit 20 receives from the learner terminal 30 information on the learning field, answers to questions, operation information indicating the end of answering on the output screen, or operation information indicating the end of reading on the input screen. Further, the communication processing unit 20 transmits to the learner terminal 30 the input screen and the output screen selected by the artificial intelligence unit 14, and the knowledge information or questions generated by the artificial intelligence unit 14.

[0038] In human learning, the learning content will not be firmly fixed with only either output learning or input learning. It is obvious that the ability is improved by repeating output learning and input learning in an appropriate balance. In this regard, according to the server device 10 of the present invention, it is possible to repeatedly perform output learning and input learning for the learner in a well-balanced manner in consideration of the learning situation of the learner, and improve the ability of the learner.

[0039] Also, the optimal balance between the output time and the input time varies from learner to learner. Conventionally, the optimal study method for each individual had to be learned individually with private tutoring or a home tutor. According to this configuration, however, it is possible to individually propose a personalized optimal study method and let each learner adopt it.

[0040] ● Processing flow As shown in FIG. 2, first, the learner terminal 30 inputs the learning field of the problem (step S101) and transmits this to the server device 10. The AI control unit 13 gives an instruction to the artificial intelligence unit 14 to select or generate questions regarding the learning field received in step S101 (step S102). Next, the server device 10 transmits an output screen including the questions output from the artificial intelligence unit 14 to the learner terminal 30 (step S103). The learner terminal 30 displays these questions on the display unit 31 and accepts input from the learner (step S104). Also, the learner terminal 30 accepts changes to the input. A history including the input content and the change to the input is transmitted to the server device 10. Next, the learner terminal 30 selects on the screen information indicating the end of the answer, for example, a send button (step S105). The server device 10 records the information acquired via the learner terminal 30 (step S106). This information is, for example, information such as the options selected by the learner, the change history of the options, and the answer time.

[0041] Next, the server device 10 determines whether the learner has reached the target learning level (step S107). The learning level may be set in advance, or may be set by the learner, or by an appropriate instructor or guardian in a position to supervise or guide the learner's learning. The learning level is determined according to the correctness of the answers input by the learner. For example, when the correct answer rate of the answers is equal to or higher than a threshold value, it may be considered that the learning level has been reached. The threshold value may be constant, may vary according to the difficulty level of the questions, or may be determined by the artificial intelligence unit 14. In this case, the artificial intelligence unit 14 may have a learned model that takes the correct answer rate of the answers as input and outputs the threshold value.

[0042] When the learner's learning level has reached the target (Y in step S107), the process ends.

[0043] When the learner's learning level has not reached the target (N in step S107), the AI control unit 13 gives an instruction to the artificial intelligence unit 14 to determine whether to display the input screen or the output screen (step S108). When the artificial intelligence unit 14 determines that the output screen should be displayed (N in step S109), the process returns to step S102.

[0044] When the artificial intelligence unit 14 determines that the input screen should be displayed (Y in step S109), the artificial intelligence unit 14 determines the knowledge information to be presented (step S110). In step S110, the AI control unit 13 may send an instruction to the artificial intelligence unit 14 to select or generate the knowledge information to be presented on the input screen upon receiving the signal indicating that the input screen should be displayed. In response to this instruction, the artificial intelligence unit 14 determines and outputs the knowledge information to be presented on the input screen. Next, the server device 10 inserts the knowledge information into the input screen and transmits it to the learner terminal 30 (step S111).

[0045] The learner inputs an operation indicating that the browsing on the input screen has been completed, that is, indicating reading completion (step S112). Also, the learner may input the difficulty level felt for the content displayed on the input screen. The server device 10 stores the genre of the displayed knowledge information, the reading time measured according to the operation indicating reading completion, the difficulty level input by the learner, etc. The storage process is performed in response to the input of the operation indicating reading completion. Alternatively, for example, it may be appropriately acquired from the learner terminal 30 and stored while the input screen is being displayed on the learner terminal 30. Next, the process proceeds to step S107.

[0046] With such a configuration, the learner terminal 30 receives an appropriate input screen or output screen from the server device 10 and displays it. FIG. 3 is a conceptual diagram showing how the learning management system adjusts the time balance between the learner's input process and output process, that is, the learning balance. The time when the input screen is displayed is the time spent on the input process, that is, the input time. The time when the output screen is displayed is the time spent on the output process, that is, the output time. In the example of this figure, initially, as shown by the leftmost pie chart, it is biased towards the output time for solving problems, and the input time is insufficient. However, by presenting the input screen with the learning management system according to the present invention, the input time for accumulating knowledge is ensured as shown by the central pie chart. The learning management system adjusts the learning balance so that the learning level increases based on the learning balance and the learner's learning level. With this configuration, the learner can perform the input process and output process in learning with an appropriate balance for the learner, and can proceed with learning efficiently.

[0047] ● Display example FIG. 4 is an example of a screen displayed on the learner terminal 30. FIG. 4(a) is an example of the input screen G10, and the knowledge information that the learner should memorize is presented. The learner selects the difficulty input button displayed on the input screen G10 and inputs the difficulty felt for the content of the knowledge information. Also, when the learner finishes reading the knowledge information or wants to display another screen, the learner selects the read completion button displayed on the input screen G10. When the read completion button is selected, another input screen or output screen determined by artificial intelligence is displayed.

[0048] FIG. 4(b) is an example of the output screen G20, and a problem is displayed on the learner terminal 30. The problem to be displayed may be a problem related to the knowledge information presented on the immediately preceding input screen G10, or may be different. Regarding the unit and field of the problem to be displayed, it is also selected by artificial intelligence.

[0049] The learner inputs an answer on the output screen G20. Also, the learner selects the difficulty input button displayed on the output screen G20 and inputs the difficulty felt for the question. Further, if the learner wants to correct the answer, the learner may reselect the answer field and correct the answer. When the input of the answer is completed, the learner selects the answer completion button. When the answer completion button is selected, for example, the correct answer or the correct / incorrect result of the question is displayed. Also, another input screen or output screen determined by artificial intelligence is displayed.

[0050] As described above, according to the learning management system according to the present invention, it is possible to appropriately cause the learner to perform the output process and the input process in learning.

[0051] ● Learning management system (2) ● In the above example, the configuration for determining the learning balance by artificial intelligence has been described. However, a part or all of the configuration for determining the learning balance may be a configuration determined by a rule-based process. For example, the optimal balance of the output time and the input time may be fixed to a predetermined value, and it may be determined which of the input screen and the output screen is to be displayed so as to achieve the optimal balance. Also, the optimal balance may be determined according to the age and appropriate characteristics of the learner, or the learning field or its difficulty level. The knowledge information displayed on the input screen and the questions displayed on the output screen may be selected and displayed from pre-prepared data instead of being generated by artificial intelligence. The display order of the input screens and the display order of the output screens may be predetermined and may be displayed in order. With various configurations as described above, it is possible to adjust the learning balance of the learner and cause the learner to learn in a well-balanced manner.

Explanation of reference numerals

[0052] 10 Server device (learning management system) 11 Display control unit 12 Memory control unit 13 AI control unit 14 Artificial Intelligence Department 20 Communication Processing Department DB1 Problem Database DB2 Teaching Material Information Database 30 Learner Terminal

Claims

1. A learning management system, comprising a processor, wherein the processor in an input step, receives an input from a learner, in a discrimination step, based on the information obtained in the input step, determines whether to display an input screen for presenting knowledge information regarding predetermined learning content to the learner or an output screen for displaying a problem regarding the predetermined learning content and receiving an answer input from the learner, in a display step, based on the determination, displays either the input screen or the output screen, A learning management system.

2. The discrimination step is an instruction step of giving an instruction to an artificial intelligence to determine next whether to display the input screen or the output screen based on the information obtained in the input step, The artificial intelligence has a learned model that takes the information input in the input step as an input and outputs information indicating either the input screen or the output screen, The learning management system according to Claim 1.

3. The learned model takes, as inputs, the display time of the input screen and the display time of the output screen among the information obtained in the input step, The learning management system according to Claim 2.

4. The learned model further takes, as an input, the knowledge information presented on the input screen or the information on the difficulty level of the problem displayed on the output screen, The learning management system according to Claim 3.

5. The learned model further takes, as an input, the information of the answer input by the learner for the problem displayed on the output screen, The learning management system according to Claim 2.

6. The learned model further takes, as an input, the information of the input history input by the learner for the problem displayed on the output screen, The learning management system according to Claim 5.

7. The learned model is a model obtained by machine learning of one or more teaching materials, The artificial intelligence has a learned model that takes the information obtained in the input step as an input and outputs the knowledge information displayed on the input screen, In the discrimination step, when it is determined to display the input screen, an instruction is given to the artificial intelligence to select or generate the knowledge information to be presented on the input screen, The learning management system according to Claim 2.

8. The learned model is a model obtained by machine learning one or more teaching materials, The artificial intelligence has a learned model that takes the information acquired in the input step as input and outputs the problem displayed on the output screen, In the discrimination step, when it is determined to display the output screen, an instruction to select or generate a problem to be presented on the output screen is given to the artificial intelligence. The learning management system according to claim 2.

9. By a computer, An input reception step of receiving an input from a learner, Based on the information acquired in the input reception step, a discrimination step of determining whether to display an input screen for presenting knowledge information regarding predetermined learning content to the learner or an output screen for displaying a problem regarding the predetermined learning content and receiving an answer input from the learner, A display step of displaying either the input screen or the output screen based on the determination, To execute, Learning management method.

10. On a computer, An input reception step of receiving an input from a learner, Based on the information acquired in the input reception step, a discrimination step of determining whether to display an input screen for presenting knowledge information regarding predetermined learning content to the learner or an output screen for displaying a problem regarding the predetermined learning content and receiving an answer input from the learner, A display step of displaying either the input screen or the output screen based on the determination, To cause to execute, Learning management program.

Images