Information processing device and program

Abstract

To evaluate answer contents of a learner and provide suitable information to the learner and a learning supporter.SOLUTION: An information processing device is communicably connected to a terminal device used by an object person who is a specific learner. In addition, the information processing device has a learning history storage unit for storing answer information regarding an answer for a question by the learner. The information processing device first acquires information regarding a target question which is a question specified by the object person from the terminal device. Then, the information processing device extracts the answer information for the target question by the learner from the learning history storage unit, and classifies the answers for the target question by each learner into groups segmented for every pattern of the answer contents. The information processing device specifies a group to which the answers for the target question by the object person belong, based on the answer information of the target question by the object person.SELECTED DRAWING: Figure 15

Description

[Technical Field]

[0001] The present invention relates to a technique for outputting information related to learning. [Background technology]

[0002] Systems for evaluating learners' answers on a computer have been known for some time. Patent Document 1 discloses a learning device that stores the content entered by a learner in an answer area and performs a correct / incorrect evaluation to detect which part of the calculation process contains an error. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-87982 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the invention of Patent Document 1 merely evaluates the correctness of the answer based on the content of the answer given by the learner and detects parts that contain errors, and is not intended to evaluate whether the content of the answer is appropriate or provide information accordingly.

[0005] The present invention has been made to solve the above-mentioned problems, and aims to evaluate the content of a learner's answers and provide appropriate information to the learner and learning supporters. [Means for solving the problem]

[0006] In one aspect of the present invention, an information processing device is communicably connected to a terminal device used by a target person who is a specific learner among a plurality of learners, and includes a learning history storage unit that stores answer information regarding answers to questions by a plurality of learners including the target person, a target question acquisition unit that acquires information regarding a target question that is a question specified by the target person from the terminal device, and a learning history storage unit that acquires information regarding answers to questions by a plurality of learners including the target person from the learning history storage unit. The aforementioned The system comprises an extraction unit that extracts answer information for a target question, a group classification unit that classifies answers to the target question by each of a plurality of learners including the subject person into groups subdivided according to answer content patterns based on the answer information extracted by the extraction unit, and a group identification unit that identifies a group to which a target answer, which is the answer to the target question by the subject person, belongs based on target answer information, which is the answer information to the target question by the subject person.

[0007] In one aspect of the information processing device, the information processing device outputs group information regarding the group to which the target answer belongs. According to this aspect, the information processing device outputs information regarding the group to which the answer of the target question by the subject belongs, and can provide the information to the subject or a learning supporter.

[0008] In one aspect of the information processing device, the information processing device includes a correct answer rate calculation unit that calculates the correct answer rate of answers belonging to the same group as the target answer based on the answer information extracted by the extraction unit, and the group information output unit outputs the correct answer rate as the group information. According to this aspect, the information processing device outputs the correct answer rate of the group to which the subject's answer to the target question belongs, and provides it to the subject and a learning supporter. In other words, the information processing device can predict the correct answer rate of the subject's answer to the target question from the pattern of the answer content, and therefore can evaluate whether the answer content is appropriate.

[0009] In one aspect of the information processing device, an academic ability calculation unit is provided that calculates an evaluation value of the academic ability of learners who have worked on answers that belong to the same group as the target answer based on the answer information extracted by the extraction unit, and the group information output unit outputs the evaluation value of the academic ability as the group information. According to this aspect, the information processing device outputs an evaluation value of the academic ability of the group to which the subject's answer to the target question belongs, and provides it to the subject or a learning supporter. This allows the subject and a learning supporter to confirm the subject's academic ability level according to the content of the subject's answer.

[0010] In one aspect of the information processing device, other group information regarding other groups to which the target answer does not belong is output. According to this aspect, the information processing device outputs information regarding other groups and can provide the target person or a learning supporter with the information.

[0011] In one aspect of the information processing device, the information processing device further includes a correct answer rate calculation unit that calculates a correct answer rate of the answers belonging to the other group based on the answer information extracted by the extraction unit, and the other group information output unit outputs the correct answer rate as the other group information. According to this aspect, the subject and the learning supporter can compare the correct answer rate of the group to which the subject's answer to the target question belongs with the correct answer rates of the other groups.

[0012] In one aspect of the information processing device, the information processing device further includes an academic ability calculation unit that calculates an evaluation value of the academic ability of the learner who has worked on the answer belonging to the other group based on the answer information extracted by the extraction unit, and the other group information output unit outputs the evaluation value of the academic ability as other group information. According to this aspect, the subject and the learning supporter can compare the evaluation value of the academic ability of the group to which the subject's answer to the target question belongs with the evaluation value of the academic ability of the other group.

[0013] In one aspect of the information processing device, the other group information output unit outputs the answers belonging to the other groups as the other group information based on the answer information extracted by the extraction unit. According to this aspect, the subject and the learning supporter can study by comparing the subject's answers with the answers of the other groups.

[0014] In another aspect of the information processing device, the questions are linked to information on learning elements that indicate learning content, and a similar problem identification unit is provided that identifies questions with the same learning elements as similar questions, and the extraction unit further extracts, from the learning history storage unit, learners who have tackled the similar questions and answer information related to answers to the similar questions, and the group classification unit, based on the extracted answer information, Each of a plurality of learners including the target According to this aspect, when only a few learners have tackled the target problem, the information processing device can identify similar problems and categorize the answers to the target problem and similar problems by each learner into groups subdivided according to the patterns of the answer content.

[0015] In another aspect of the present invention, there is provided a program executed by an information processing device having a computer, the information processing device being communicably connected to a terminal device used by a target person who is a specific learner among a plurality of learners, the program including a learning history storage unit that stores answer information regarding answers to questions by a plurality of learners including the target person, a target question acquisition unit that acquires, from the terminal device, information regarding a target question that is a question specified by the target person, and a learning history storage unit that acquires, from the learning history storage unit, information regarding answers to questions by a plurality of learners including the target person. The aforementionedThe computer is made to function as an extraction unit that extracts answer information for a target question, a group classification unit that classifies answers to the target question by each of a plurality of learners including the subject into groups subdivided according to answer content patterns based on the answer information extracted by the extraction unit, and a group identification unit that identifies a group to which a target answer, which is the answer to the target question by the subject, belongs based on target answer information, which is the answer information to the target question by the subject. By installing this program on a computer and running it, an information processing device according to the present invention can be configured. [Effects of the Invention]

[0016] According to the information processing device of the present invention, it is possible to evaluate the content of the learner's answers and provide appropriate information to the learner and learning supporter. [Brief explanation of the drawings]

[0017] [Figure 1] 1 shows the configuration of a learning support system to which a server of the present invention is applied. [Figure 2] FIG. 2 is a block diagram showing the hardware configuration of a server. [Figure 3] 10 is an example of a data configuration of a learning element DB. [Figure 4] 10 is an example of the data structure of a question DB. [Figure 5] 10 is an example of a data configuration of a learning history DB. [Figure 6] FIG. 2 is a block diagram showing the functional configuration of a server in the first embodiment. [Figure 7] These are sample answers that each student wrote in the answer section of the target question. [Figure 8] 10A and 10B are diagrams illustrating a method for extracting sentences and formulas from answer information. [Figure 9] FIG. 10 is a diagram illustrating the criteria for determining one continuous calculation formula. [Figure 10] FIG. 10 is a diagram illustrating a method for extracting feature quantities of an answer based on a text. [Figure 11]FIG. 10 is a diagram illustrating a method for grouping answers to target questions by each student. [Figure 12] 1 is an example of a feature space in which answers to target questions by subjects are plotted. [Figure 13] 10 is an example of a prediction screen and a recommendation screen according to the first embodiment. [Figure 14] FIG. 2 is a block diagram showing the hardware configuration of a student terminal. [Figure 15] 4 is a flowchart of a screen creation process in the first embodiment. [Figure 16] FIG. 10 is a block diagram showing the functional configuration of a server in the second embodiment. [Figure 17] 10 is an example of the LRT evaluation value for each group. [Figure 18] 10 is an example of a prediction screen and a recommendation screen according to the second embodiment. [Figure 19] 10 is a flowchart of a screen creation process according to the second embodiment. [Figure 20] 13 shows an example of the configuration of a learning support system according to a fourth modified example. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. First Embodiment [Overall configuration] Figure 1 shows the configuration of a learning support system to which the server of the present invention is applied. The learning support system 100 is a system that predicts the correct answer rate based on the content of a learner's answers and recommends appropriate answer examples. The learning support system 100 is configured so that a server 10, multiple student terminals 20, and multiple teacher terminals 30 can communicate with each other via a network 5 such as the Internet.

[0019] The server 10 is an information processing device that processes, stores, and transmits / receives various types of information, and is, for example, a server device, a personal computer, or a general-purpose tablet PC (personal computer).

[0020] The student terminal 20 is used by learners such as students and their parents, and is an information processing device such as a smartphone, a mobile phone, a wearable device such as an Apple Watch (registered trademark), a tablet, a personal computer terminal, etc. Specifically, the student terminal 20 transmits answer information and target question information, receives and displays prediction screens and recommendation screens, etc.

[0021] The teacher terminal 30 is used by a learning supporter such as a teacher, and is, for example, an information processing device such as a tablet or a personal computer terminal. Specifically, the teacher terminal 30 transmits target question information, receives and displays prediction screens and recommendation screens, etc.

[0022] [Server Configuration] 2 is a block diagram showing the hardware configuration of server 10. Server 10 includes a communication unit 11, a control unit 12, a storage unit 13, a recording medium 14, a display unit 15, and an input unit 16. These components, a learning element database (hereinafter, "database" will be referred to as "DB") 41, a question DB 42, and a learning history DB 43 are interconnected via a bus 19.

[0023] The server 10 may be executed by a single computer, or may be executed by a plurality of computers in a distributed manner, or may be executed by a virtual machine in a distributed manner.

[0024] The communication unit 11 is a communication unit for communicating with the student terminal 20 and the teacher terminal 30 via the network 5. Specifically, the communication unit 11 receives target question information from the student terminal 20 and the teacher terminal 30, and transmits prediction screens and recommendation screens to the student terminal 20 and the teacher terminal 30.

[0025] The control unit 12 includes a central processing unit (CPU), a micro-processing unit (MPU), a graphics processing unit (GPU), and other arithmetic processing devices, and performs various information processing, control processing, and the like related to the server 10 by reading and executing programs stored in the storage unit 13. The programs can be deployed so that they are executed on a single computer, located at one site, or distributed across multiple sites and on multiple computers interconnected by a communications network. Although the control unit 12 is described in FIG. 2 as being a single processor, it may also be a multi-processor.

[0026] The storage unit 13 includes memory elements such as RAM (Random Access Memory) and ROM (Read Only Memory), and stores programs, data, etc. required for the control unit 12 to execute processing. The storage unit 13 also temporarily stores data, etc. required for the control unit 12 to execute arithmetic processing.

[0027] The recording medium 14 is a non-volatile, non-transitory recording medium such as a disk-shaped recording medium or a semiconductor memory, and is configured to be detachable from the server 10. The recording medium 14 records various programs to be executed by the control unit 12. When the server 10 executes a screen creation process described below, the programs recorded on the recording medium 14 are loaded into the memory unit 13 and executed by the control unit 12.

[0028] The display unit 15 is a liquid crystal display, an organic EL (electroluminescence) display, or the like, and displays various information in accordance with instructions from the control unit 12. The input unit 16 is an input device such as a mouse, keyboard, touch panel, or button, and outputs received operation information to the control unit 12.

[0029] The learning element DB 41 stores information about learning elements, which are subdivisions of learning content. Here, learning elements are learning content and textbook explanations that are sub-divided into optimal granularity based on learning groups such as units, for purposes such as linking textbooks and teaching materials, understanding learning progress, and creating teaching plans. Furthermore, a unit is a group of learning activities centered on a specific theme, and is set for each subject, such as "Mathematics" or "Japanese," and is composed of one or more learning elements.

[0030] FIG. 3 is an example of the data structure of the learning element DB 41. As shown in the figure, the learning element DB 41 stores information on subjects, fields, units, learning elements, and learning element IDs. The subject is the name of the subject, such as "Mathematics" or "Japanese." The field is the name of the field, set for each subject, such as "Differentiation." The unit is the name of the unit, set for each field, such as "Differentiation of a Composite Function." The learning element is information indicating the learning content that the learning element targets, set for each unit, such as "Logarithm" or "Negative Order." The learning element ID is identification information for the learning element.

[0031] It is assumed that the fields that make up a subject, the units that make up a field, and the learning elements that make up a unit are all set in advance. The data structure of the learning element DB 41 shown in Figure 3 is an example, and the present invention is not limited to this. The data structure of the learning element DB 41 can be set arbitrarily as long as it stores information related to the identification of learning elements.

[0032] The question DB 42 stores question information about the questions that students tackle, in association with a question ID that identifies the question. Figure 4 is an example of the data structure of the question DB 42. As shown in the figure, the question DB 42 stores information such as a question ID, question data, learning element ID, learning material name, page, and difficulty level. The question data is data about the question indicated by the question ID. The learning element ID is identification information for the learning element to which the problem indicated by the question ID belongs. The learning material name is the name of the learning material, such as a reference book or textbook, that contains the problem indicated by the question ID, and the page is the page and question number on which the problem is written in the learning material.

[0033] The difficulty level is a numerical value indicating the difficulty of the question indicated by the question ID. Here, the question difficulty level (hereinafter simply referred to as "difficulty level") is based on any concept, such as the difficulty level of the question or the correct answer rate, and may be set by evaluating each question using Item Response Theory (IRT) or the like, or may be set artificially in advance. Specifically, in this embodiment, the difficulty level is calculated as a numerical value based on the student's answer information, which will be described later. The lower the numerical value, the easier the difficulty level, and the higher the numerical value, the more difficult the difficulty level. Note that the difficulty level may be stored in the question DB 42 as shown in FIG. 4 and extracted when necessary, or it may not be stored in the question DB 42 but may be calculated based on the answer information each time it is needed.

[0034] It should be noted that which learning element a question belongs to is set in advance. Also, the data structure of the question DB 42 shown in Figure 4 is an example, and the present invention is not limited to this, and can be set arbitrarily.

[0035] The learning history DB43 stores answer information related to the answers to the problems that the students have worked on, in association with the student ID that identifies the student. Figure 5 is an example of the data structure of the learning history DB43. As shown in the figure, the learning history DB43 stores information on the student ID, question ID, answer column data, and correct / incorrect answers. The question ID is identification information for the problem that the student identified by the student ID worked on. The answer column data is data that the student entered in the answer column as the answer to the problem. The correct / incorrect answers are the correct / incorrect answers to the problem that the student identified by the student ID worked on.

[0036] In this embodiment, as an example, the student terminal 20 is linked in advance with the student ID of the student who uses it, and answer information regarding the answers to questions that the student has worked on using the student terminal 20 is automatically stored in the learning history DB 43 of the server 10. Furthermore, the correctness of the answers included in the answer information may be determined, for example, by the server 10 storing model answers for each question in advance and automatically scoring based on the answer column data, or by obtaining the teacher's scoring results. The data structure of the learning history DB 43 shown in FIG. 5 is an example, and the present invention is not limited to this, and the data structure can be set as desired.

[0037] The storage format of each DB described above is an example, and other storage formats may be used as long as the relationships between the data are maintained. Each DB is realized by a recording medium such as a hard disk drive (HDD) or a solid state drive (SSD). In this embodiment, the storage unit 13 and the various DBs may be configured as an integrated storage device, or may be separate storage devices. The various DBs may be external storage devices connected to the server 10, and the configuration thereof may be set arbitrarily.

[0038] 6 is a block diagram showing the functional configuration of the server 10. Functionally, the server 10 includes a learning element DB 41, a question DB 42, a learning history DB 43, a target question acquisition unit 51, an extraction unit 52, a group classification unit 53, a correct answer rate calculation unit 54, a group identification unit 55, and a screen creation unit 56.

[0039] The target question acquisition unit 51, extraction unit 52, group classification unit 53, correct answer rate calculation unit 54, group identification unit 55, and screen creation unit 56 are realized by the control unit 12 executing a program.

[0040] The target question acquisition unit 51 acquires target question information including a student ID and information related to the target question (hereinafter also referred to as the "target question") from the student terminal 20. When a student wants to check the accuracy rate of the answer they entered in the answer field or refer to other example answers, they use the student terminal 20 to specify a specific question as the target question and send the target question information to the server 10. The target question acquisition unit 51 acquires the target question information from the student terminal 20 and sets the student as the target.

[0041] The extraction unit 52 extracts students who have worked on the target problem and answer information related to the answers given to the target problem by each student from the learning history DB 43. Note that the students who have worked on the target problem extracted by the extraction unit 52 include the subject.

[0042] Based on the answer information extracted by the extraction unit 52, the group classification unit 53 classifies the answers to the target questions by each student into groups subdivided according to the patterns of the answer contents.

[0043] The classification method by the group classification unit 53 will be described in detail below. FIG. 7 shows example answers written by each student in the answer column of the target problem. FIG. 7(a) shows an example answer written by student A in answer column 61 of target problem 60. The pattern of student A's answer, as shown in the figure, is "includes intermediate steps, with supplementary explanation in Japanese." FIG. 7(b) shows an example answer written by student B in answer column 62 of target problem 60. The pattern of student B's answer, as shown in the figure, is "includes intermediate steps, without supplementary explanation in Japanese." FIG. 7(c) shows an example answer written by student C in answer column 63 of target problem 60. The pattern of student C's answer, as shown in the figure, is "without intermediate steps, without supplementary explanation in Japanese."

[0044] FIG. 8 is a diagram illustrating a method for extracting text and formulas from answer information. FIG. 9 is a diagram illustrating criteria for determining a single continuous formula (hereinafter also referred to as "one formula"). The group classification unit 53 first extracts text other than formulas from the answer column data included in the answer information. Specifically, the group classification unit 53 extracts text 71 other than formulas and text 72, as shown in FIG. 8(a). Next, the group classification unit 53 extracts formulas from the answer column data. Specifically, the group classification unit 53 detects "=" (equal) and treats all terms connected by "=" as one formula, and extracts formulas 73 to 77 as shown in FIG. 8(b). At this time, if there are a certain number of blank characters or more between terms, the group classification unit 53 treats them as separate formulas. For example, because there are a certain number of blank characters between formulas 73 and 74, the group classification unit 53 determines that they are separate formulas rather than a single formula, and extracts formulas 73 and 74 from the answer column data. Furthermore, as shown in FIG. 9, in the case of horizontally consecutive formulas that do not contain a certain number of blank characters, the group classification unit 53 determines that all terms connected by "=" are a single consecutive formula, and extracts, for example, formula 77 from the answer column data. Furthermore, as shown in FIG. 9, in the case of vertically consecutive formulas 75a and 75b, if there is no term to the left of "=" in formula 75b, the group classification unit 53 determines that it is a single formula that is a consecutive formula with formula 75a above, and extracts, for example, formula 75 from the answer column data.

[0045] 10 is a diagram illustrating a method for extracting feature quantities of an answer based on a text. The group classification unit 53 first combines the extracted sentences and formulas into one text. Specifically, as shown in FIG. 10(a), the group classification unit 53 combines the extracted sentences 71 and 72 with formulas 73 to 77 into one text 78.

[0046] The group classification unit 53 then extracts features of the text using a machine learning technique capable of processing time-series information and text, such as a Long Short Term Memory (LSTM) as shown in FIG. 10(b). Note that if the input and output of the LSTM are the same text and the LSTM is trained to perform "input," "extract features in the middle layer," and "output so as to restore the input," the features of the middle layer can be acquired in this process. Therefore, for example, the group classification unit 53 can acquire the features of the text 78 as numerical values ​​in the middle layer by training the LSTM to restore and output the exact same text 78 using text 78 as input. Here, the features of the text 78 are the answer column data corresponding to the text 78, i.e., the features of each student's answer to the target problem. In other words, the group classification unit 53 acquires the features of each student's answer to the target problem based on the answer information of the target problem that each student has worked on. Specifically, the features represent patterns of the answer content, such as whether or not there are intermediate steps and whether or not there are supplementary explanations in Japanese.

[0047] FIG. 11 is a diagram illustrating a method for grouping answers to the target questions by each student. As shown in FIG. 11(a), the group classification unit 53 plots the answers to the target questions by each student in a feature space based on the features. In other words, the plotted circles represent the answers to the target questions by the students. The group classification unit 53 then groups groups of answers that tend to have similar answer content patterns. Examples of grouping methods include a combination of RANSAC (Random Sample Consensus) and k-meams. Specifically, as shown in FIG. 11(b), the group classification unit 53 groups the circles representing the answers of each student plotted in the feature space into Group A, Group B, Group C, and Group D by clustering. In this way, the group classification unit 53 classifies the answers to the target questions by each student into groups subdivided according to the patterns of answer content, based on the answer information extracted by the extraction unit 52.

[0048] As shown in FIG. 11(c), the correct answer rate calculation unit 54 calculates the correct answer rate of the answers belonging to each group based on the answer information extracted by the extraction unit 52.

[0049] The group identification unit 55 identifies a group to which the answer to the target question by the subject (hereinafter also referred to as the "target answer") belongs, based on answer information regarding the answer to the target question by the subject (hereinafter also referred to as the "target answer information"). FIG. 12 is an example of a feature space in which the target answer is plotted. Specifically, first, the group classification unit 53 acquires the feature of the target answer as a numerical value based on the target answer information, and plots the target answer with a star mark in the feature space, as shown in FIG. 12. The group identification unit 55 identifies the group in the feature space in which the star mark is closest to the center of gravity of each group as the group to which the target answer belongs. In the case of the feature space shown in FIG. 12, the group identification unit 55 identifies the group to which the target answer belongs as group A.

[0050] When the screen creation unit 56 acquires the target question information, it references each DB and creates and outputs a prediction screen that allows the user to check the predicted correct answer rate (hereinafter also referred to as the "predicted correct answer rate") for the answer that the user has entered in the answer field.

[0051] Here, the prediction screen created by the screen creation unit 56 will be described. FIG. 13(a) is an example of the prediction screen. The prediction screen is a screen that displays the predicted correct answer rate of the target answer, and is transmitted to the student terminal 20 of the subject. The subject can check the predicted correct answer rate of the target answer by displaying the prediction screen received from the server 10 using the student terminal 20. As shown in FIG. 13(a), the prediction screen has a subject item 79, a learning element item 80, a question specification item 81, a target question 82, an answer column 83, a group correct answer rate item 84, and another group correct answer rate item 85.

[0052] The target student item 79 displays the student ID of the target student. The learning element item 80 displays the unit and learning element corresponding to the target question. The question specification item 81 has a question item 81a that displays the target question specified by the target student and its difficulty level, and question items 81b and 81c that display questions of the same learning element as the target question and their difficulty level. A check mark is displayed on the question item 81a that displays the target question.

[0053] The target person may use the student terminal 20 to perform a predetermined operation to display a prediction screen that includes only the target person item 79, learning element item 80, and question specification item 81. In this case, the target person can use the student terminal 20 to specify the target question by selecting a predetermined learning element and question in the learning element item 80 and question specification item 81, respectively, and transmit target question information including the student ID and information related to the target question to the server 10.

[0054] The target question 82 displays the target question specified by the subject. The answer column 83 displays the target answer. The group correct answer rate item 84 displays the correct answer rate of the group to which the target answer belongs. Since the group to which the target answer belongs has a similar answer content pattern, the correct answer rate of the target answer is predicted to be similar to the correct answer rate of the group. In other words, the predicted correct answer rate of the target answer is the correct answer rate of the group to which the target answer belongs. The other group correct answer rate item 85 displays the correct answer rates of other groups to which the target answer does not belong, and has a link that can be clicked to display answers belonging to other groups.

[0055] Specifically, if the answers given by students to a target question are classified into four groups, A to D, and the target answer belongs to group C, the group correct answer rate item 84 will display the correct answer rate of 85% for group C as "Correct answer rate for same answer pattern: 85%" as shown in FIG. 13(a). Similarly, the other group correct answer rate item 85 will display the correct answer rate of 93% for group A as "View an example answer with a 93% correct answer rate" along with a link to display answers belonging to group A, as shown in FIG. 13(a). Similarly, the other group correct answer rate item 85 will display the correct answer rates of group B and group D as "View an example answer with a 60% correct answer rate" and "View an example answer with a 30% correct answer rate" respectively, along with links to display answers belonging to each group.

[0056] When the screen creation unit 56 receives information from the student terminal 20 that a specific link has been clicked by the target person, it creates and outputs a recommendation screen including the answers of students who belong to the group corresponding to the link.

[0057] Here, the recommendation screen created by the screen creation unit 56 will be described. FIG. 13(b) is an example of a recommendation screen. The recommendation screen is a screen that displays answers belonging to other groups, and is transmitted to the student terminal 20 of the target student. By displaying the recommendation screen received from the server 10 using the student terminal 20, the target student can refer to answers from other groups whose answer content patterns are different from their own. As shown in FIG. 13(b), the recommendation screen has a target student item 79, a learning element item 80, a question specification item 81, a target question 82, an answer column 86, and a group correct answer rate item 87.

[0058] The target student items 79, learning element items 80, question specification items 81, and target questions 82 are the same as those on the prediction screen, and therefore will not be described for the sake of brevity. The answer field 86 displays answers belonging to other groups to which the target answer does not belong. Specifically, the screen creation unit 56 identifies the group corresponding to the link clicked by the target in the Other Group Correct Answer Rate field 85 on the prediction screen, and creates a recommendation screen to display answers belonging to that group. For example, if the target uses the student terminal 20 to click "View answer examples with a 93% correct answer rate" in the Other Group Correct Answer Rate field 85 on the prediction screen, as shown in FIG. 13(a), the screen creation unit 56 identifies group A, which has a correct answer rate of 93%, and creates a recommendation screen to display answers belonging to group A in the answer field 86. The answer field 86 can display multiple answers belonging to group A using scroll buttons, next buttons, etc. The display order of the multiple answers belonging to group A can be set arbitrarily, such as alphabetical order of the students' names, order of the students' academic ability levels, etc. The group correct answer rate item 87 displays the correct answer rate of the group to which the answer displayed in the answer column 86 belongs.

[0059] In this embodiment, the other group correct answer rate item 85 on the prediction screen displays the correct answer rates and links of all other groups to which the target answer does not belong, but the present invention is not limited to this, and it may also be possible to display the correct answer rates and links of only other groups with a higher correct answer rate than the correct answer rate of the group to which the target answer belongs or of only the other group with the highest correct answer rate. Specifically, the other group correct answer rate item 85 may display the correct answer rate and link of the other group with the highest correct answer rate along with the sentence recommended as learning support.

[0060] In addition, the screen creation unit 56 may not provide the other group correct answer rate item 85 on the prediction screen, but may display the correct answer rate and a link in the group correct answer rate item 84, and when the link is clicked, create a recommendation screen that allows reference to other answers that belong to the same group as the target answer.

[0061] In the above configuration, the learning history DB 43 of the server 10 is an example of a learning history storage unit of the present invention. Also, in the above configuration, the target question acquisition unit 51, extraction unit 52, group classification unit 53, correct answer rate calculation unit 54, and group identification unit 55 of the server 10 are examples of a target question acquisition unit, extraction unit, group classification unit, correct answer rate calculation unit, and group identification unit of the present invention, respectively. Also, in the above configuration, the screen creation unit 56 of the server 10 is an example of a group information output unit and other group information output unit of the present invention.

[0062] [Student Device Configuration] 14 is a block diagram showing the hardware configuration of student terminal 20. Student terminal 20 includes a communication unit 21, a control unit 22, a storage unit 23, a display unit 25, and an input unit 26. These components are interconnected via a bus 29.

[0063] The communication unit 21 is a communication unit for communicating with the server 10 via the network 5. Specifically, the communication unit 21 transmits answer information and target question information to the server 10, and receives prediction screens and recommendation screens from the server 10.

[0064] The control unit 22 includes an arithmetic processing unit such as a CPU, an MPU, a GPU, etc., and reads and executes programs stored in the storage unit 23 to perform various information processing, control processing, etc. related to the student terminal 20. Note that although the control unit 22 is described as a single processor in Fig. 14, it may be a multiprocessor.

[0065] The storage unit 23 includes memory elements such as RAM and ROM, and stores programs, data, etc. required for the control unit 22 to execute processing. The storage unit 23 also temporarily stores data, etc. required for the control unit 22 to execute arithmetic processing.

[0066] The display unit 25 is a liquid crystal display, an organic EL display, or the like, and displays various information in accordance with instructions from the control unit 22. The input unit 26 is an input device such as a mouse, keyboard, touch panel, or button, and outputs received operation information to the control unit 22.

[0067] [Screen creation process] Next, a screen creation process will be described, which creates and outputs a prediction screen on which the subject can check the predicted correct answer rate and a recommendation screen on which the subject can refer to the answers to the target question by other students, based on the target question information entered by the subject. Fig. 15 is a flowchart of the screen creation process by the server 10. This process is realized by the server 10 executing a program prepared in advance.

[0068] A student, who is a target, uses the student terminal 20 to transmit target question information including a student ID and information related to the target question to the server 10. The server 10 acquires the target question information from the student terminal 20 (step S101). The server 10 then extracts, from the learning history DB 43, the students who have tackled the target question and answer information related to each student's answer to the target question (step S102). The server 10 then classifies the answers to the target question by each student into a plurality of groups based on the extracted answer information (step S103).

[0069] The server 10 calculates the accuracy rate of the answers belonging to each group for each group based on the answer information extracted by the extraction unit 52 (step S104). Then, the server 10 identifies the group to which the target answer belongs based on the target answer information (step S105). Furthermore, the server 10 creates a prediction screen that displays the predicted accuracy rate of the target answer, and transmits it to the student terminal 20 used by the target person (step S106). By displaying the prediction screen received from the server 10 on the student terminal 20, the target person can check the predicted accuracy rate of their own answer or click a predetermined link to refer to answer examples belonging to other groups.

[0070] The server 10 determines whether or not it has received information from the student terminal 20 that a predetermined link has been clicked by the subject (step S107). If it has not received information that the link has been clicked (step S107; No), the server 10 waits. On the other hand, if it has received information that the link has been clicked (step S107; Yes), the server 10 first identifies other groups corresponding to the clicked link. Then, the server 10 creates a recommendation screen including answers belonging to the identified other groups, and transmits it to the student terminal 20 used by the subject (step S108). By the student terminal 20 displaying the recommendation screen received from the server 10, the subject can easily check answer examples that have answer patterns different from his or her own. This causes the server 10 to end the screen creation process.

[0071] Typically, when a problem requires calculation, if there are insufficient intermediate steps or supplementary explanations in Japanese, the content of the answer to the problem is inappropriate, which may result in a lower accuracy rate. The learning support system 100 of this embodiment can predict the accuracy rate of a target answer from the pattern of answer content, such as whether or not there are intermediate steps or supplementary explanations in Japanese, for problems requiring calculation. In other words, the accuracy rate can be predicted from the pattern of answer content, and whether or not the answer content is appropriate can be evaluated.

[0072] Furthermore, the learning assistance system 100 can provide the subject with answers that belong to other groups. Specifically, the learning assistance system 100 can support the subject's learning by recommending answers that belong to other groups with a high correct answer rate. This allows the subject to compare their own answers with the answers of other learners while referring to the correct answer rates of groups classified by answer content patterns, and can learn answering methods that are more likely to result in correct answers.

[0073] Second Embodiment In the first embodiment, the server 10 calculates the accuracy rate of answers belonging to each group classified by answer content and displays it on the prediction screen, but the present invention is not limited to this, and it may also be possible to calculate the academic ability level (hereinafter simply referred to as "academic ability") of students who worked on the answers belonging to each group and display it on the prediction screen.

[0074] First, the functional configuration of the server in the second embodiment will be described. Fig. 16 is a block diagram showing the functional configuration of the server in the second embodiment. For convenience of explanation, explanations similar to those in the first embodiment will be omitted, and therefore an LRT (Latent Rank Theory) evaluation value calculation unit 59 and a screen creation unit 56x will be described in detail.

[0075] The LRT evaluation value calculation unit 59 evaluates each student using the LRT based on the answer information extracted by the extraction unit 52, thereby calculating a numerical value representing the academic level of the students who worked on the answers belonging to each group. The LRT evaluation value can be, for example, the sum of the differences between the standard LRT and the LRTs higher than the standard LRT. Specifically, if the standard LRT is "2" and the LRTs within the group are "3," "5," and "1," the LRT evaluation value is calculated as "4" using the formula (3-2) + (5-2). Alternatively, the LRT evaluation value can be set arbitrarily, such as the average LRT within the group or the total number of learners within the group whose LRT exceeds a threshold. FIG. 17 shows the LRT evaluation value of each group calculated by the LRT evaluation value calculation unit 59. In this embodiment, the LRT evaluation value is the average LRT within the group. The LRT evaluation value can be used to visualize and provide information on which group the answers of students with high academic levels belong to.

[0076] When the screen creation unit 56x acquires the target question information, it references each DB, creates a prediction screen for checking the LRT evaluation value of the group to which the user's answer belongs, and outputs it.

[0077] Here, the prediction screen created by the screen creation unit 56x will be described. The prediction screen is a screen that displays the LRT evaluation value of the group to which the target answer belongs and the LRT evaluation values ​​of other groups, and is transmitted to the student terminal 20 of the target student. Fig. 18(a) is an example of the prediction screen in the second embodiment. For the sake of convenience, explanations similar to those in the first embodiment will be omitted, and the group evaluation value item 90 and the other group evaluation value item 91 will be described in detail.

[0078] The group evaluation value item 90 displays the LRT evaluation value of the group to which the target answer belongs. The other group evaluation value item 91 displays the LRT evaluation values ​​of other groups to which the target answer does not belong, and has links that can be clicked to display answers that belong to other groups. Specifically, if the answers given by students to a target question are classified into four groups, groups A to D, and the target answer belongs to group C, the group evaluation value item 90 displays the LRT evaluation value of group C, "2.3," as "LRT evaluation value of same answer pattern: 2.3," as shown in FIG. 18(a). Furthermore, the other group evaluation value item 91 displays the LRT evaluation value of group A, "4.0," as "View example answers with an LRT evaluation value of 4.0," along with a link to display answers that belong to group A, as shown in FIG. 18(a). The Other Group Evaluation Value item 91 also displays the LRT evaluation values ​​for Group B and Group D, along with links to display the answers belonging to each group, such as "View example answers with an LRT evaluation value of 1.3" and "View example answers with an LRT evaluation value of 1.5."

[0079] When the screen creation unit 56x receives information from the student terminal 20 that a predetermined link has been clicked by the subject, it creates and outputs a recommendation screen including answers that belong to the group corresponding to the link.

[0080] Here, the recommendation screen created by the screen creation unit 56x will be described. The recommendation screen is a screen that displays answers belonging to other groups to which the target answer does not belong, and is transmitted to the student terminal 20 of the target student. The target student can refer to answers from groups whose answer content patterns are different from their own by displaying the recommendation screen received from the server 10x using the student terminal 20. FIG. 18(b) is an example of the recommendation screen. For ease of explanation, explanations similar to those in the first embodiment will be omitted, and the answer field 86 and group evaluation value item 92 will be described in detail.

[0081] The answer column 86 displays answers belonging to other groups to which the target answer does not belong. Specifically, the screen creation unit 56x identifies a group corresponding to the link clicked by the target person in the other group evaluation value item 91 on the prediction screen and creates a recommendation screen to display answers belonging to that group. For example, when the target person uses the student terminal 20 to click "View example answers with an LRT evaluation value of 4.0" in the other group evaluation value item 91 on the prediction screen as shown in FIG. 18(a), the screen creation unit 56x identifies group A with an LRT evaluation value of "4.0" and creates a recommendation screen to display answers belonging to group A in the answer column 86. The answer column 86 can display multiple answers belonging to group A using scroll buttons, next buttons, etc. The display order of multiple answers belonging to group A can be set arbitrarily, such as alphabetical order of student names or order of student academic ability levels. The group evaluation value item 92 displays the LRT evaluation value of the group to which the answer displayed in the answer column 86 belongs.

[0082] In this embodiment, the other group evaluation value item 91 on the prediction screen displays the LRT evaluation values ​​and links of all other groups to which the target answer does not belong, but the present invention is not limited to this, and it is also possible to display the LRT evaluation values ​​and links of only other groups with LRT evaluation values ​​higher than the LRT evaluation value of the group to which the target answer belongs or only the other group with the highest LRT evaluation value. Specifically, the other group evaluation value item 91 may display the LRT evaluation value and link of the other group with the highest LRT evaluation value along with a sentence recommended as learning support.

[0083] In addition, the screen creation unit 56x may not provide the other group evaluation value item 91 on the prediction screen, but may display the LRT evaluation value and a link in the group evaluation value item 90, and when the link is clicked, create a recommendation screen that allows reference to other answers that belong to the same group as the target answer.

[0084] In the above configuration, the LRT evaluation value calculation unit 59 of the server 10x is an example of the academic ability calculation unit of the present invention. Also, the LRT evaluation value is an example of the academic ability evaluation value of the present invention.

[0085] [Screen creation process] Next, a screen creation process will be described, which creates and outputs a prediction screen on which the subject can check the LRT evaluation value of the group to which the subject's answer belongs and a recommendation screen on which the subject can refer to answers to the target question by other students, based on the target question information input by the subject. Fig. 19 is a flowchart of the screen creation process by the server 10x. This process is realized by the server 10x executing a program prepared in advance.

[0086] A target student uses the student terminal 20 to transmit target question information including a student ID and information related to the target question to the server 10x. The server 10x acquires the target question information from the student terminal 20 (step S201). The server 10x then extracts, from the learning history DB 43, the students who have tackled the target question and answer information related to each student's answer to the target question (step S202). The server 10x then classifies the answers to the target question by each student into a plurality of groups based on the extracted answer information (step S203).

[0087] The server 10x calculates, for each group, an LRT evaluation value of the students who worked on the answers belonging to each group, based on the answer information extracted by the extraction unit 52 (step S204). Then, the server 10x identifies the group to which the target answer belongs, based on the target answer information (step S205). Furthermore, the server 10x creates a prediction screen that displays the LRT evaluation value of the group to which the target answer belongs, and transmits it to the student terminal 20 used by the target person (step S206). By having the student terminal 20 display the prediction screen received from the server 10x, the target person can check the LRT evaluation value of the group to which his or her answer belongs, or click a predetermined link to refer to example answers belonging to other groups.

[0088] The server 10x determines whether or not it has received information from the student terminal 20 that a predetermined link has been clicked by the subject (step S207). If it has not received information that the link has been clicked (step S207; No), the server 10x waits. On the other hand, if it has received information that the link has been clicked (step S207; Yes), the server 10x first identifies other groups corresponding to the clicked link. Then, the server 10x creates a recommendation screen including answers belonging to the identified other groups, and transmits it to the student terminal 20 used by the subject (step S208). By the student terminal 20 displaying the recommendation screen received from the server 10x, the subject can easily check answer examples that have answer patterns different from his or her own. This causes the server 10x to end the screen creation process.

[0089] The learning support system 100x of this embodiment can identify the group to which a target answer belongs based on the pattern of answer content, such as whether or not intermediate steps are included and whether or not supplementary explanations are provided in Japanese, for problems requiring calculations, and can provide the academic level of students who have worked on answers belonging to the same group. This allows the target person to check the academic level according to the content of their own answer.

[0090] Furthermore, the learning assistance system 100x can provide the subject with answers belonging to other groups. Specifically, the learning assistance system 100x can support the subject's learning by recommending answers belonging to other groups with the highest LRT evaluation value. This allows the subject to compare their own answers with the answers of other learners while referring to the LRT evaluation values ​​of groups classified by answer content patterns, and can learn the answering methods of students with high academic ability levels.

[0091] <Modification> Next, a description will be given of modifications of the first and second embodiments. The following modifications can be applied to the first and second embodiments in appropriate combinations.

[0092] (First Modification) In the first and second embodiments described above, the extraction unit 52 extracts the students who have worked on the target questions and the answer information of each student to the target questions from the learning history DB 43. Furthermore, the group classification unit 53 classifies the answers to the target questions by each student into groups subdivided according to the patterns of the answer content, based on the answer information extracted by the extraction unit 52.

[0093] However, the present invention is not limited to this. If only a few students have worked on the target question, the extraction unit 52 may further extract from the learning history DB 43 students who have worked on similar questions similar to the target question, and the answer information for each student to the similar questions. In this case, the group classification unit 53 classifies the answers to the target question and similar questions by each student into groups subdivided by answer content patterns, based on the answer information extracted by the extraction unit 52. Also, in this case, the answer column 86 on the recommendation screen preferentially displays answers to target questions belonging to other groups. If there is no answer to the target question, the answer column 86 on the recommendation screen displays answers to similar questions belonging to other groups.

[0094] Specifically, the similar questions can be arbitrarily set to questions with the same learning elements as the target question, questions with the same learning elements and difficulty as the target question, etc. For example, if the similar questions are set to questions with the same learning elements as the target question, the extraction unit 52 refers to the question DB 42 and identifies questions with the same learning elements as the target question as the similar questions. Then, the extraction unit 52 extracts, from the learning history DB 43, information on the students who have tackled the target question and their answers to the target question, and information on the students who have tackled the similar questions and their answers to the similar questions.

[0095] In this modification, learning elements are used to identify similar questions, but the present invention is not limited to this. For example, any standard, such as the curriculum code established by the Ministry of Education, Culture, Sports, Science and Technology, may be used. The extraction unit 52 in this modification is an example of a similar question identification unit of the present invention. The question DB 42 is an example of a question storage unit of the present invention.

[0096] (Second Modification) In the first and second embodiments described above, the prediction screen and the recommendation screen are two screens displayed in separate windows, but the present invention is not limited to this, and the screen layout can be set arbitrarily, such as displaying the accuracy rate or LRT evaluation value of the group to which the target answer belongs and answers belonging to other groups on one screen. For example, the screen creation unit 56 may create a screen that displays the target answer and answers belonging to other groups side by side to make them easier to compare, or may create a screen that also displays the model answer.

[0097] (Third Modification) In the first and second embodiments, students use student terminals 20 to send target question information to server 10 and view predicted screens and recommendation screens acquired from server 10. However, the present invention is not limited to this. A teacher may use teacher terminal 30 to send target question information, including the student ID of the target student and information about the target question, to server 10 and view the predicted screens and recommendation screens acquired from server 10. In this case, server 10 creates a predicted screen based on the target question information received from teacher terminal 30 and transmits it to teacher terminal 30. Furthermore, when server 10 receives information from teacher terminal 30 that the teacher has clicked a specific link on the predicted screen, server 10 creates a recommendation screen including answers belonging to the group corresponding to the link and transmits it to teacher terminal 30.

[0098] (Fourth Modification) In the first and second embodiments described above, the teacher uses the teacher terminal 30, but the present invention is not limited to this, and the teacher may use a teacher terminal 95 having the functions of the server 10. In this case, the student terminals 20 are used by students, their parents, etc. The teacher terminal 95, like the server 10, is, for example, a personal computer or a general-purpose tablet PC (personal computer).

[0099] FIG. 20 shows an example configuration of a learning support system 200 in this case. As shown in the figure, the learning support system 200 is configured so that a teacher terminal 95 and multiple student terminals 20 can communicate with each other via a network 5. The teacher terminal 95 is connected to a learning element DB 96, a question DB 97, and a learning history DB 98. The teacher terminal 95 executes the screen creation process previously performed by the server 10, classifying students' answers to target questions by answer content pattern and creating each screen. Therefore, the teacher terminal 95 can transmit the created prediction screens and recommendation screens to the student terminals 20 and display them on its own display unit. In this case, the teacher terminal 95 is an example of the information processing device of the present invention. [Explanation of symbols]

[0100] 5. Network 10, 10x servers 20 Student devices 30, 95 Teacher's terminal 41, 96 Learning element DB 42, 97 Problem DB 43, 98 Learning history DB 51 Target Problem Acquisition Section 52 Extraction part 53 Group Classification Section 54 Correct answer rate calculation section 55 Group Specific Department 56, 56x Screen Creation Department 100, 100x, 200 Learning Support System

Claims

1. a terminal device used by a specific learner among the plurality of learners, a learning history storage unit that stores answer information regarding answers to questions by a plurality of learners including the target learner; a target question acquisition unit that acquires, from the terminal device, information about a target question that is a question designated by the subject; an extraction unit that extracts answer information of the target question by a plurality of learners including the target person from the learning history storage unit; a group classification unit that classifies the answers to the target questions by each of a plurality of learners including the target person into groups that are subdivided according to answer content patterns, based on the answer information extracted by the extraction unit; a group identification unit that identifies a group to which a target answer, which is an answer to the target question by the subject, belongs based on target answer information, which is answer information to the target question by the subject; An information processing device comprising:

2. The information processing device according to claim 1 , further comprising a group information output unit that outputs group information relating to a group to which the target answer belongs.

3. a correct answer rate calculation unit that calculates a correct answer rate of answers that belong to the same group as the target answer based on the answer information extracted by the extraction unit; The information processing device according to claim 2 , wherein the group information output unit outputs the correct answer rate as the group information.

4. an academic ability calculation unit that calculates an evaluation value of the academic ability of a learner who has worked on an answer that belongs to the same group as the target answer, based on the answer information extracted by the extraction unit; The information processing device according to claim 2 , wherein the group information output unit outputs the evaluation value of the academic ability as the group information.

5. The information processing device according to claim 1 or 2, further comprising an other group information output unit that outputs other group information relating to other groups to which the target answer does not belong.

6. a correct answer rate calculation unit that calculates the correct answer rate of the answers belonging to the other group based on the answer information extracted by the extraction unit; The information processing device according to claim 5 , wherein the other group information output unit outputs the correct answer rate as the other group information.

7. an academic ability calculation unit that calculates an evaluation value of the academic ability of the learners who have worked on the answers belonging to the other group based on the answer information extracted by the extraction unit; The information processing device according to claim 5 or 6, wherein the other group information output unit outputs the evaluation value of the academic ability as the other group information.

8. The information processing device according to claim 5 , wherein the other group information output unit outputs answers belonging to the other groups as the other group information based on the answer information extracted by the extraction unit.

9. The problem is linked to information of a learning element indicating a learning content, and a similar problem identification unit is provided that identifies a problem having the same learning element as a similar problem; the extraction unit further extracts, from the learning history storage unit, learners who have tackled the similar problems and answer information relating to answers to the similar problems; The information processing device described in claim 1, wherein the group classification unit classifies the answers to the target question and the similar questions by each of a plurality of learners including the subject into groups subdivided according to patterns of answer content based on the extracted answer information.

10. A program executed by an information processing device having a computer, the information processing device is communicably connected to a terminal device used by a target person who is a specific learner among a plurality of learners, a learning history storage unit that stores answer information regarding answers to questions by a plurality of learners including the target learner; a target question acquisition unit that acquires, from the terminal device, information about a target question that is a question designated by the subject; an extraction unit that extracts answer information of the target question by a plurality of learners including the target person from the learning history storage unit; a group classification unit that classifies the answers to the target questions by each of a plurality of learners including the target person into groups subdivided according to patterns of answer content based on the answer information extracted by the extraction unit; a group identification unit that identifies a group to which a target answer, which is an answer to the target question by the subject, belongs, based on target answer information, which is answer information to the target question by the subject; A program that causes the computer to function as a

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