Information processing device and program

Abstract

To make it possible to identify a student corresponding to an outlier in a graph created on the basis of a plurality of data related to learning.SOLUTION: An information processing device comprises a storage unit that stores identification information for a plurality of learners and grade information related to learning by the learners in association. The information processing device further creates a scatter chart, as a graph, in which the icons of the learners are plotted on the basis of grade information, with the vertical and horizontal axes as mutually different perspectives. Meanwhile, the information processing device classifies the learners into groups in the graph. Furthermore, the information processing device identifies a learner corresponding to an outlier from among the learners not belonging to a group in the graph. Then, the information processing device outputs the created graph, classified groups, and identified outlier.SELECTED DRAWING: Figure 18

Description

[Technical Field]

[0001] The present invention relates to a technique for searching for outliers in a graph. [Background technology]

[0002] Conventionally, systems have been known that store test results administered to students in a database and enable them to be checked on a computer. Patent Documents 1 and 2 disclose systems that calculate the fluctuations in grades based on the target test and tests administered before the target test, and highlight the grades of specific students. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-63900 [Patent Document 2] Patent Publication No. 2021-63901 Summary of the Invention [Problem to be solved by the invention]

[0004] Teachers and other learning support staff often need to centrally manage multiple data sets, including test results, and use graphs to grasp overall information for the entire class or grade. In such cases, learning support staff need to provide appropriate guidance by checking individual student information and grouping students with similar attributes.

[0005] The present invention has been made to solve the above-mentioned problems, for example, and its main objective is to provide an information processing device that identifies students who fall into outliers in a graph created based on multiple pieces of learning-related data. [Means for solving the problem]

[0006] In one aspect of the present invention, an information processing device includes a storage unit that stores identification information of a plurality of learners and performance information related to learning by the learners in association with each other, and a display unit that displays a vertical axis and a horizontal axis, each of which is different from the other. Test items and Corresponding to test items The system comprises a graph creation unit that creates a scatter diagram in which an icon of each learner is plotted based on performance information, a group classification unit that classifies the learners into groups in the graph, an outlier identification unit that identifies learners who are outliers from learners who do not belong to the groups in the graph, and an output unit that outputs the graph, the groups, and the outliers. The test items are the format of the test and an index showing the result, or a questionnaire that is an evaluation of the test and an index showing the result. According to this aspect, the information processing device can easily identify learners who are outliers in a graph on which the icons of each learner are plotted, thereby reducing the burden of identifying learners who are outliers in a graph.

[0007] In one aspect of the information processing device, the score information is item The outlier identification unit identifies, in the graph, learners who do not belong to the group and whose index included in the performance information is lower than a first threshold as outliers. According to this aspect, the information processing device can identify, for example, only learners with poor performance as outliers based on the first threshold.

[0008] One aspect of the information processing device includes a graph division unit that divides the graph into multiple regions of the same size, a learner proportion calculation unit that calculates a learner proportion, which is the proportion of the number of learners whose icons are plotted in each region, a detection unit that detects regions where the learner proportion is equal to or greater than a second threshold as detection regions, and an integration unit that integrates adjacent detection regions to form integrated regions, and the group classification unit classifies learners whose icons are plotted in the same integrated region into the same group. According to this aspect, the information processing device can classify learners into groups based on the learner proportion in each region into which the graph is divided. Therefore, learners who are grouped together on the graph can be classified into the same group.

[0009] In one aspect of the information processing device, if there is an area adjacent to the detection area where the learner proportion is less than the second threshold but not zero, the integration unit also integrates the area into an integrated area. According to this aspect, the information processing device can classify a learner whose icon is plotted in an area adjacent to the detection area into the same group as the learner whose icon is plotted in the detection area, even if the learner proportion does not meet the threshold. Thus, learners whose icons are plotted close to each other on the graph can be classified into the same group.

[0010] In one aspect of the information processing device, the information processing device includes a distance calculation unit that calculates the distance between the center of gravity of the integrated area and the icon of a learner that is not plotted on any integrated area, and the outlier identification unit identifies a learner whose icon is the distance equal to or greater than a third threshold as an outlier. According to this aspect, the information processing device can identify, from among learners who do not belong to any group, a learner who is farther from any group by the third threshold or more as an outlier.

[0011] In one aspect of the information processing device, the group classification unit classifies learners whose icons are closer to the integrated area than a threshold into the same group as learners whose icons are plotted in the integrated area. According to this aspect, the information processing device can classify learners who do not belong to any group into groups whose distances on the graph are close.

[0012] In one aspect of the information processing device, the vertical and horizontal axes of the graph are Different test items The information processing device includes an input information acquisition unit that acquires as input information the above-mentioned test scores, and the graph creation unit creates the scatter diagram as a graph based on the input information and the test score information. According to this aspect, the information processing device creates a scatter diagram as a graph in which the test-related indicators and test evaluations included in the input information are on the vertical axis and the horizontal axis, respectively, and can visualize the test score information of each learner using the graph.

[0013] In one aspect of the information processing device, the output unit outputs, based on the grade information, either a test-related index or a test evaluation for the test that is the target of the vertical axis, and either a test-related index or a test evaluation for the test that is the target of the horizontal axis, for the learner who falls under the outlier. According to this aspect, the information processing device can output the test-related index and the test evaluation for the learner who falls under the outlier, along with the graph, the group on the graph, and the outlier. Therefore, a learning supporter or the like can check information about the learner who falls under the outlier, along with the graph.

[0014] In another aspect of the present invention, a program executed by an information processing device having a computer includes a storage unit that stores identification information of a plurality of learners and performance information related to learning by the learners in association with each other, and a vertical axis and a horizontal axis that are respectively different from each other. Test items and Corresponding to test itemsThe computer is caused to function as a graph creation unit that creates a scatter diagram in which each learner's icon is plotted based on performance information, a group classification unit that classifies the learners into groups in the graph, an outlier identification unit that identifies learners who are outliers from learners who do not belong to the groups in the graph, and an output unit that outputs the graph, the groups, and the outliers. The test items are the format of the test and an index showing the result, or a questionnaire that is an evaluation of the test and an index showing the result. By installing and executing this program on a computer, an information processing device according to the present invention can be configured. [Effects of the Invention]

[0015] According to the information processing device of the present invention, it is possible to identify students who fall into outliers in a graph created based on a plurality of data related to learning. [Brief explanation of the drawings]

[0016] [Figure 1] 1 shows a configuration of an information display system according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing the internal configuration of a management server. [Figure 3] 10 is an example of a data configuration of a test DB. [Figure 4] 10 is an example of a data configuration of an evaluation DB. [Figure 5] This is an example of an evaluation index. [Figure 6] 10 is an example of a data configuration of a questionnaire DB. [Figure 7] 10 is an example of the data structure of a grade DB. [Figure 8] This is an example of a scatter plot with test scores on the vertical axis and rubric evaluation scores on the horizontal axis. [Figure 9] FIG. 10 is a diagram illustrating a first outlier pattern in a graph. [Figure 10] FIG. 10 is a diagram illustrating a second outlier pattern in a graph. [Figure 11] FIG. 10 is a diagram illustrating a third outlier pattern in a graph. [Figure 12] FIG. 10 is a diagram illustrating a fourth outlier pattern in the graph. [Figure 13] FIG. 10 is a diagram illustrating calculation of the student ratio for each area obtained by dividing a graph. [Figure 14] FIG. 10 is a diagram illustrating detection of an area in a graph. [Figure 15] FIG. 10 is a diagram illustrating group classification of students in a graph. [Figure 16] FIG. 10 is a diagram illustrating the identification of students who fall under outliers. [Figure 17] 10 is an example of a graph screen. [Figure 18] 10 is a flowchart of a graph creation process. [Figure 19] 10 shows a configuration of an information display system according to a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. <Embodiment> [Overall configuration] 1 shows the configuration of an information display system to which an information processing device of the present invention is applied. The information display system 100 is configured so that a plurality of terminal devices 10 and a management server 20 can communicate with each other via a network 5. In this embodiment, as an example, the learners are students and the learning supporters are teachers.

[0018] The terminal device 10 is used by a teacher and is, for example, a desktop PC, laptop PC, tablet PC, etc. The terminal device 10 is equipped with a display unit such as a liquid crystal display, and is used to output information on grades based on test results, etc.

[0019] The management server 20 is connected to a student database (hereinafter, "database" will be referred to as "DB") 31, a test DB 32, an assessment DB 33, a questionnaire DB 34, and a grade DB 35. The management server 20 creates a two-axis scatter diagram (hereinafter, simply referred to as a "scatter diagram") that visualizes the learning status of all students and the learning status of each student, with the vertical and horizontal axes set as different perspectives, and refers to the grade DB 35. The management server 20 then classifies the students into multiple groups on the graph and identifies outliers from among the students who do not belong to any group. The management server 20 also creates a graph screen that displays information about the created graph, the classified groups, and the identified outliers, and transmits the information on the graph screen to the terminal device 10 used by the teacher. This allows the terminal device 10 to display the graph screen on its display unit.

[0020] The communication between the management server 20 and the terminal device 10 via the network 5 may be wired or wireless.

[0021] [Administration Server Configuration] 2 is a block diagram showing the internal configuration of management server 20. Management server 20 includes a communication unit 21, an input information acquisition unit 22, a grade information acquisition unit 23, a graph creation unit 24, a group classification unit 25, an outlier identification unit 26, and a graph screen output unit 27. These components, a student DB 31, a test DB 32, an evaluation DB 33, a questionnaire DB 34, and a grade DB 35 are interconnected via a bus 29.

[0022] The communication unit 21 is a communication unit for communicating with the terminal device 10 through the network 5. Specifically, the communication unit 21 receives input information from the terminal device 10 used by the teacher and transmits information on the created graph screen to the terminal device 10.

[0023] The student DB 31 stores student information about students. Specifically, the student DB 31 stores student information such as a student ID, which is student identification information, the student's name, grade, class, and the like.

[0024] The test DB 32 stores test information related to test-style exams in which students enter answers. FIG. 3 shows an example of the data structure of the test DB 32. As shown in the figure, the test DB 32 stores information such as the test type, test items, subject, and test ID. The test type is the type of exam, such as "first semester midterm" or "first semester final." The test items indicate the format of the exam and an indicator of the results, such as "test score" for a test-style exam and "rubric evaluation score" for an assessment-style exam described below. The test items also indicate a questionnaire that evaluates the exam and an indicator of the results, such as "questionnaire response" for a questionnaire. The subject is the subject of the test, such as "mathematics," "Japanese," or "English." The test ID is identification information for the exam.

[0025] The test DB 32 only needs to be able to check test information related to test-style exams, and the data structure can be set arbitrarily. For example, in this embodiment, the test type is set to a regular exam including the semester in which the exam was conducted, but the present invention is not limited to this and can be set arbitrarily. Similarly, the test items and subjects can also be set arbitrarily.

[0026] The evaluation DB 33 stores evaluation information related to evaluation-style tests that evaluate student performance. In this embodiment, rubric evaluation is also considered to be one type of test and an evaluation-style test. Rubric evaluation is a method of evaluating a student's learning achievement using predetermined evaluation criteria, and can evaluate a student's thinking ability and expressive ability based on performance in, for example, presentations and discussions, rather than test scores. FIG. 4 shows an example of the data structure of the evaluation DB 33. As shown in the figure, the evaluation DB 33 stores information such as test type, test items, evaluation items, subjects, test IDs, evaluation scores, and evaluation criteria. The test type, test items, subjects, and test IDs are the same as those in the test DB 32 described above.

[0027] The evaluation items are the viewpoints of evaluation by rubric evaluation, such as "thinking, judgment, and expression." In this embodiment, the viewpoint of evaluation by rubric evaluation is only one type, "thinking, judgment, and expression." The evaluation score is a score that evaluates a student's performance, etc., and in this embodiment, it ranges from "0" to "10" in increments of one point, with lower scores indicating lower evaluations and higher scores indicating higher evaluations. The evaluation criteria are the standards for assigning evaluation scores to student performance, and are linked to each evaluation score.

[0028] The evaluation DB 33 may have any data structure as long as it can confirm evaluation information related to the evaluation-style test. For example, in this embodiment, the evaluation items are based on one type of perspective, "thinking, judgment, and expression," but the evaluation items may be based on multiple types of perspectives, such as "thinking," "judgment," and "expression." The evaluation DB 33 may also include evaluation indices that associate evaluation items, evaluation scores, and evaluation criteria, and may use the evaluation indices to determine the evaluation scores of students' performance.

[0029] Here, we will explain how to calculate evaluation scores using evaluation indices. Figure 5(a) shows an example of an evaluation index when evaluation items are based on multiple perspectives. In this case, as shown in Figure 5(a), the vertical axis of the evaluation index represents three types of evaluation items: "thinking," "judgment," and "expression," and the horizontal axis represents the evaluation scores for each evaluation item, ranging from "1" to "3." The squares where the evaluation items on the vertical axis intersect with the evaluation scores on the horizontal axis represent the evaluation criteria for each evaluation item corresponding to each evaluation score. On the other hand, Figure 5(b) shows an example of an evaluation index when thinking, judgment, and expression are combined into one type. In this case, as shown in Figure 5(b), the vertical axis of the evaluation index represents one type of evaluation item: "thinking, judgment, and expression," and the horizontal axis represents the evaluation score for the evaluation item "thinking, judgment, and expression." Note that any evaluation criteria can be set.

[0030] Specifically, teachers refer to the evaluation index shown in Figure 5(a) and compare students' performance with the evaluation criteria for each evaluation item to determine the evaluation score for each of the evaluation items: "Thinking," "Judgment," and "Expression." Furthermore, if there is only one evaluation item, the evaluation results for each evaluation item are comprehensively reviewed to determine the evaluation score for the evaluation item "Thinking, Judgment, and Expression." Specifically, teachers refer to the evaluation index shown in Figure 5(b) and determine the evaluation score for the evaluation item "Thinking, Judgment, and Expression" based on the total score for each evaluation item.

[0031] In this embodiment, the horizontal axis of the evaluation index is the evaluation score, but the present invention is not limited to this and may be the evaluation stage. Furthermore, the number of evaluation points and the number of stages of the evaluation stage can be set arbitrarily, and may be set to letters instead of numbers.

[0032] Furthermore, the method of determining the evaluation score is not limited to the method of conducting a step-by-step evaluation for each of the evaluation items "thinking," "judgment," and "expression," and then determining the evaluation score for the evaluation item "thinking, judgment, and expression" by comprehensively examining the evaluation results. It is also possible for a teacher or other person to create an exam and evaluation index that combines the evaluation items "thinking, judgment, and expression," and then determine the evaluation score directly from the exam.

[0033] Furthermore, the evaluation DB 33 stores the evaluation scores and evaluation criteria as evaluation information, but the management server 20 may store and manage the evaluation scores and evaluation criteria in a separate DB.

[0034] The questionnaire DB 34 stores questionnaire information related to questionnaires in which students enter answer numbers. The questionnaires include evaluations and impressions of exams. FIG. 6 shows an example of the data structure of the questionnaire DB 34. As shown in the figure, the questionnaire DB 34 stores information such as exam type, exam items, subjects, exam ID, survey content, answer numbers, and answer content. The exam type, exam items, subjects, and exam ID are the same as those in the test DB 32 described above.

[0035] The survey contents are questions posed to students, such as, "How much of the material on this test did you understand?", which asks about the student's level of understanding. Answer numbers and answer contents are linked, and the survey answer numbers and their contents are, for example, "I didn't understand it at all" corresponding to answer number "1" and "I understood it perfectly" corresponding to answer number "5." In this embodiment, a survey asking about the student's level of understanding of each subject is conducted at the time of regular exams, such as "midterm of first semester" and "end of first semester," as indicated by the exam type. In this case, the smaller the answer number, the lower the student's level of understanding, and the larger the answer number, the higher the student's level of understanding.

[0036] The questionnaire DB 34 only needs to be able to check questionnaire information related to the questionnaire, and the data structure thereof can be set arbitrarily. The questionnaire DB 34 stores the questionnaire content, answer number, and answer content as questionnaire information, but the management server 20 may store and manage the questionnaire content, answer number, and answer content in a separate DB.

[0037] The grade DB 35 stores grade information related to the results of exams and surveys of students, in association with the student ID. FIG. 7 shows an example of the data structure of the grade DB 35. As shown in the figure, the grade DB 35 stores information such as the student ID, exam type, exam items, exam ID, and exam results. Specifically, the exam results are scores if the exam is in test format, grades if in assessment format, and answer numbers if in survey format. The grade DB 35 is updated when the marking, evaluation, and collection of answers for the exams conducted by students are completed and confirmed.

[0038] The performance DB 35 only needs to continuously accumulate the results of tests, rubric evaluations, and other exams and questionnaires for each student, and the data structure can be set as desired. In this embodiment, the management server 20 connects and manages the various DBs described above, but the present invention is not limited to this. Information from multiple DBs may be consolidated into one, or information from one DB may be divided into multiple databases for management.

[0039] The input information acquisition unit 22 acquires information about the test that is the subject of the graph. Specifically, the teacher uses the terminal device 10 to specify, on a predetermined screen, the test type, subject, test items to be used as the vertical axis of the graph, and test items to be used as the horizontal axis of the graph, and transmits the information to the management server 20 as input information.

[0040] In this embodiment, the students who are the subject of the graph are students who belong to a specific grade. The affiliation of the students who are the subject of the graph is not limited to grade level, but can be set arbitrarily, such as by class level. The affiliation of the students who are the subject of the graph may be specified by a teacher and transmitted to the management server 20 as input information.

[0041] Based on the input information, the grade information acquisition unit 23 acquires grade information of students belonging to a specified grade from the grade DB 35. Specifically, the grade information acquisition unit 23 first refers to the student DB 31 and identifies the student IDs of students belonging to a specified grade. Furthermore, based on the test type, subject, test items on the vertical axis, and test items on the horizontal axis included in the input information, the grade information acquisition unit 23 refers to the test DB 32, the evaluation DB 33, and the questionnaire DB 34 and identifies the test IDs of the tests that will be the vertical axis and the horizontal axis of the graph. Then, the grade information acquisition unit 23 acquires grade information from the grade DB 35 based on the identified student IDs and test IDs.

[0042] Based on the grade information, the graph creation unit 24 creates a scatter diagram in which icons representing each student (hereinafter, "icons representing students" are also referred to as "student icons") are plotted at the intersection of the test results on the horizontal axis and the test results on the vertical axis. Here, an icon is a simple symbol or graphic representation, such as a black circle or a human figure. Figure 8 is an example of a scatter diagram with test scores on the vertical axis and rubric evaluation scores on the horizontal axis. Note that graph 40 plots icons for 100 students per grade, but because some icons overlap, for convenience, only icons for approximately 30 students are depicted in Figure 8.

[0043] Specifically, as shown in FIG. 8 , the vertical axis of graph 40 represents test scores, which are the results of a test indicated by the test type "End of First Semester," the subject "Japanese," and the test item "Test Score." The horizontal axis of graph 40 represents evaluation scores, which are the results of a test indicated by the test type "End of First Semester," the subject "Japanese," and the test item "Rubric Evaluation." Graph 40 plots a human-shaped icon representing each student at the intersection of the test score on the vertical axis and the test evaluation score on the horizontal axis. In this embodiment, the rubric evaluation only uses one evaluation item, "Thinking, Judgment, and Expression," so the horizontal axis of graph 40 displays "End of First Semester Thinking, Judgment, and Evaluation."

[0044] The group classification unit 25 classifies students into groups based on the positions of the icons plotted on the graph. In this embodiment, a group is a group of students with similar grades. Furthermore, the outlier identification unit 26 identifies students who are outliers from among students who do not belong to any group, based on the icons plotted on the graph. In this embodiment, outliers are students who have poor grades or low survey ratings and who do not belong to any group on the graph.

[0045] FIG. 9 is a diagram illustrating a first outlier pattern in a graph. In graph 40, as shown in FIG. 9, the group classification unit 25 classifies students into three groups 42, 43, and 44 based on the positions of their icons. In this case, the outlier identification unit 26 identifies students 45, 46, and 47, whose icons are plotted at positions away from any of groups 42, 43, and 44 and whose scores on the vertical axis and / or evaluation points on the horizontal axis are lower than the thresholds, as outliers. Here, the threshold for the scores on the vertical axis is set to 15 points, and the threshold for the evaluation points on the horizontal axis is set to 3 points. The score threshold and the evaluation point threshold are examples of the first threshold.

[0046] 10 is a diagram illustrating a second outlier pattern in a graph. In graph 48, as shown in FIG. 10, the group classification unit 25 classifies students into groups 49, 50, and 51 based on the positions of their icons. In this case, the outlier identification unit 26 identifies student 53, whose icon is plotted at a position away from all of groups 49, 50, and 51 and whose score on the vertical axis and / or evaluation score on the horizontal axis are lower than the respective thresholds, as an outlier. Student 52, whose icon is plotted at a position away from all of groups 49, 50, and 51, does not qualify as an outlier because both the score on the vertical axis and the evaluation score on the horizontal axis are higher than the respective thresholds and the student has good grades.

[0047] FIG. 11 is a diagram illustrating a third outlier pattern in a graph. In graph 54, the group classification unit 25 classifies students into groups 55, 56, and 57 based on the positions of their icons, as shown in FIG. 11. In this case, the outlier identification unit 26 identifies students 59 and 60, whose icons are plotted away from groups 55, 56, and 57 and whose scores on the vertical axis and / or evaluation scores on the horizontal axis are lower than the respective thresholds, as outliers. Student 58, whose icon is plotted outside the encircling lines representing each group and whose scores on the vertical axis are lower than the threshold, is not an outlier because its icon is close to that of group 55. In other words, student 58 is considered to belong to group 55.

[0048] Figure 12 is a diagram illustrating the fourth outlier pattern on a graph. In graph 70, the student icons are uniformly distributed, and no groups are formed. Therefore, there are no outliers. If a graph looks like this, it is possible that there is a problem with the test or evaluation itself.

[0049] Here, a method for classifying students into groups on a graph will be described in detail. FIG. 13 is a diagram illustrating the calculation of the student ratio for each region obtained by dividing a graph. The group classification unit 25 divides the graph 40 into multiple regions of the same width and height. Specifically, as shown in FIG. 13, the group classification unit 25 divides the graph 40 into 100 regions of the same size. The group classification unit 25 then calculates the student ratio, which is the ratio of the number of students whose icons are plotted in each region. For example, if 100 student icons are plotted on the graph 40, the region 61 indicated by the thick line has one student icon plotted, so the student ratio for region 61 is "0.01." Furthermore, the region 62 indicated by the thick line has 10 student icons plotted, so the student ratio for region 62 is "0.1." Furthermore, the regions 63 and 64 indicated by the thick line have four student icons plotted each, so the student ratios for both regions 63 and 64 are "0.04." The student ratio for regions without student icons plotted is "0." In the example of FIG. 13, the graph 40 is divided into regions each having the same width and height, but the present invention is not limited to this and any rectangular region may be used.

[0050] FIG. 14 is a diagram illustrating the detection of regions in a graph. FIG. 14(a) is a diagram in which the color and pattern of each region are changed according to the calculated student ratio of each region. In FIG. 14(a), for convenience, regions with a student ratio of "0" are displayed in white, regions with a student ratio of "0.1 or more" are displayed in black, and regions with a student ratio of "greater than 0 and less than 0.1" are displayed in different patterns. The group classification unit 25 detects regions with a student ratio equal to or greater than a predetermined student ratio threshold as detection regions. Here, the student ratio threshold is "0.05." Note that the student ratio threshold is an example of a second threshold. At this time, the group classification unit 25 also detects regions adjacent to the detection region where the student ratio is less than the threshold "0.05" but "greater than 0" as adjacent regions. The group classification unit 25 may also detect regions adjacent to adjacent regions as adjacent regions.

[0051] For convenience, in Figure 14(b), the detected area is shown in black and the adjacent areas are shown in a diagonal line pattern. Specifically, area 61, which is not adjacent to the detected area and has a student ratio of "0.01," is shown in white. Area 62, which has a student ratio of "0.1," is shown in black as a detected area, along with adjacent areas with a student ratio equal to or greater than the threshold value of "0.05." Furthermore, area 63, which has a student ratio of "0.04" adjacent to detected area 65, and area 64, which has a student ratio of "0.04" adjacent to area 63, are shown in a diagonal line pattern as adjacent areas.

[0052] The group classification unit 25 combines adjacent detection areas to form a combined area. Specifically, as shown in FIG. 14(b), the group classification unit 25 combines adjacent detection areas in the upper right of the graph 40 to form a combined area 66 surrounded by a dotted line. Furthermore, the group classification unit 25 combines adjacent detection areas in the center of the graph 40 to form a combined area 67 surrounded by a dotted line. Furthermore, the group classification unit 25 combines adjacent detection areas in the lower left of the graph 40 with adjacent areas 63 and 64 to form a combined area 68 surrounded by a dotted line.

[0053] 15 is a diagram illustrating how students are classified into groups on a graph. The group classification unit 25 classifies students whose icons are plotted in the same integrated area into the same group. Specifically, as shown in FIG. 15, on graph 40, the group classification unit 25 classifies students whose icons are plotted in integrated area 66 into group 42, students whose icons are plotted in integrated area 67 into group 43, and students whose icons are plotted in integrated area 68 into group 44.

[0054] Next, the identification of students who fall under outliers will be described. FIG. 16 is a diagram illustrating the identification of students who fall under outliers. The group classification unit 25 calculates the distance between the center of gravity of the integrated area corresponding to each group in the graph and the icon of a student who does not belong to any group in a brute-force manner. If the calculated distance is equal to or greater than a predetermined icon distance threshold, the outlier identification unit 26 identifies the student as an outlier. On the other hand, if the calculated distance is less than the icon distance threshold, the group classification unit 25 classifies the student into the same group as the student whose icon is plotted on the closest integrated area. The icon distance threshold is an example of a third threshold.

[0055] For example, as shown in FIG. 16(a), the group classification unit 25 calculates the distance between the center of gravity of the integrated region 67 corresponding to group 43 and each of students 45, 46, 47, 71, 72, and 73 who do not belong to any group. Specifically, the group classification unit 25 calculates the distance between the center of gravity of the integrated region 67 and the position where the icon of each student who does not belong to any group is plotted. Similarly, although arrows are omitted in FIG. 16(a) for convenience, the group classification unit 25 calculates the distance between the center of gravity of the integrated region 66 corresponding to group 42 and each of students 45, 46, 47, 71, 72, and 73. Furthermore, the group classification unit 25 calculates the distance between the center of gravity of the integrated region 68 corresponding to group 44 and each of students 45, 46, 47, 71, 72, and 73.

[0056] In this case, the group classification unit 25 determines that the integrated area 67 is the closest integrated area, and that the distance to the students 72 and 73 is less than the icon distance threshold. The group classification unit 25 then classifies the students 72 and 73 into the same group 43 as the students whose icons are plotted on the integrated area 67. In other words, the students 72 and 73 are considered to belong to group 43.

[0057] Furthermore, the outlier identification unit 26 identifies as outliers students who do not belong to any group on the graph those whose test indices corresponding to the vertical and horizontal axes exceed predetermined thresholds. Specifically, when the threshold for the score corresponding to the vertical axis is 15 points and the threshold for the evaluation score corresponding to the horizontal axis is 3 points, and students who fall below one or more of these are identified as outliers, as shown in FIG. 16(b), the outlier identification unit 26 identifies students 45, 46, and 47 as outliers on the graph 40. Note that student 71 does not qualify as an outlier because neither the test score corresponding to the vertical axis nor the rubric evaluation score corresponding to the horizontal axis is below the respective thresholds.

[0058] The graph screen output unit 27 creates a graph screen including the graph created by the graph creation unit 24, the groups classified by the group classification unit 25, and information about students who fall under the outliers identified by the outlier identification unit 26, and transmits screen information about the graph screen to the terminal device 10. By displaying the graph screen based on the screen information received by the terminal device 10, the teacher can check the graph that is a scatter plot corresponding to the input information, the groups into which students with similar grades are classified in the graph, and information about students who fall under the outliers.

[0059] The graph screen output unit 27 may create a screen for the teacher to input input information, and transmit screen information related to the screen to the terminal device 10. In this embodiment, the graph screen output unit 27 creates a screen for inputting input information and outputting graphs and the like, and transmits information related to the screen to the terminal device 10 in response to login and various requests, as will be described in detail later.

[0060] In the above configuration, the grade DB 35 of the management server 20 is an example of a storage unit of the present invention. Also, in the above configuration, the input information acquisition unit 22, outlier identification unit 26, and graph screen output unit 27 of the management server 20 are examples of an input information acquisition unit, an outlier identification unit, and an output unit of the present invention, respectively. Also, in the above configuration, the grade information acquisition unit 23 and the graph creation unit 24 are examples of a graph creation unit of the present invention. Also, in the above configuration, the group classification unit 25 is an example of a group classification unit, a graph division unit, a learner proportion calculation unit, a detection unit, an integration unit, and a distance calculation unit of the present invention.

[0061] [screen] Next, we will explain the screens created and output by the graph screen output unit 27. The teacher displays the screen by logging in to a specific site using the terminal device 10 and inputs information about the test that is the subject of the graph. In addition, when the teacher makes a request by pressing a specific button on the screen, a graph or the like corresponding to the input information is displayed on the screen.

[0062] 17 is an example of a graph screen. As shown in FIG. 17, the graph screen has a test type selection area 81, a subject selection area 82, a vertical axis item selection area 83, a horizontal axis item selection area 84, a scatter plot creation button 85, a graph display area 86, and a detailed display area 87.

[0063] The test type selection area 81 is an area where the teacher selects any test type that they want to visualize as a graph. The subject selection area 82 is an area where the teacher selects any subject that they want to visualize as a graph. The vertical axis item selection area 83 is an area where the teacher selects vertical axis items, which are test items that they want to use as the vertical axis of the graph. The horizontal axis item selection area 84 is an area where the teacher selects horizontal axis items, which are test items that they want to use as the horizontal axis of the graph. The information selected in the test type selection area 81, subject selection area 82, vertical axis item selection area 83, and horizontal axis item selection area 84 is obtained by the management server 20 as input information.

[0064] After the teacher has finished selecting the test type, subject, vertical axis items, and horizontal axis items using the terminal device 10, he or she presses the scatter plot creation button 85 to send a graph output request to the management server 20. Upon receiving the graph output request, the management server 20 retrieves grade information from the grade DB 35 based on the input information, creates a graph, classifies students into groups, and identifies outliers. The management server 20 then creates a graph screen containing a scatter plot graph, groups into which students with similar grades are classified in the graph, and information about students who fall into the outlier category, and sends screen information about the graph screen to the terminal device 10. In this way, when the teacher presses the scatter plot creation button 85, the graph screen displays the graph, the groups into which the students are classified, and information about students who fall into the outlier category.

[0065] The graph display area 86 is an area for displaying a graph of the created scatter plot. Specifically, as shown in FIG. 17, the graph display area 86 displays the graph 40, highlighting the solid lines surrounding each group and the dotted lines surrounding the outliers. The detail display area 87 displays detailed information about the outliers, such as the name of the outlier, the test-related indicators on the vertical axis, and the test-related indicators on the horizontal axis. Specifically, as shown in FIG. 17, the detail display area 87 displays detailed information about the outliers, such as the name of the outlier, the test type "End of First Semester" and test score for the subject "Japanese" on the vertical axis, and the evaluation score for the test type "End of First Semester" and the rubric evaluation item "Thinking, Judgment, and Evaluation" for the subject "Japanese."

[0066] The graph screen allows teachers to easily send input information to the management server 20. Furthermore, the graph screen allows teachers to easily recognize the scatter plot graph corresponding to the test type, subject, vertical axis item, and horizontal axis item selected by the teacher, the groups into which students are classified on the graph, and information about students who are outliers on the graph.

[0067] Note that when logging in to a specific site, only the range indicated by the input at the top of the screen shown in Figure 17 is displayed, and by pressing the scatter plot creation button 85, the range indicated by the output at the bottom of the screen is displayed. The screen shown in Figure 17 is merely an example, and the present invention is not limited to this, and the screen configuration can be set as desired. For example, the test type, subject, vertical axis items, and horizontal axis items may be input instead of selected using radio buttons, and subjects may be selected for the vertical and horizontal axes, respectively.

[0068] [Graph creation process] 17 is a flowchart of the graph creation process according to this embodiment. This process creates a graph with the teacher's vertical and horizontal axes specified based on the input information and grade information, and identifies students who fall into outliers on the graph. Specifically, this process is realized by the computer constituting the management server 20 executing a program prepared in advance.

[0069] The teacher displays the graph screen by logging in to a specific site using the terminal device 10 and selects the test type, subject, vertical axis items, and horizontal axis items. After completing the selection, the teacher presses a specific button on the screen to send the input information to the management server 20 and request the output of the graph.

[0070] The management server 20 acquires input information from the terminal device 10 (step S11). Based on the acquired input information, the management server 20 acquires grade information from the grade DB 35 (step S12). Then, the management server 20 creates a scatter graph by plotting the test results of the corresponding students based on the grade information, with the test-related indicators designated by the teacher based on the input information as the vertical and horizontal axes (step S13).

[0071] Next, the management server 20 classifies the students into groups based on the student icons plotted on the created graph (step S14). Then, the management server 20 identifies, from among the students who do not belong to any group in the graph, students with poor grades whose test-related indicators on at least one of the vertical and horizontal axes are below a threshold, as outliers (step S15). The management server 20 then creates a graph screen including the created graph, the classified groups, and information about the students who fall into the identified outliers, and transmits information about the graph screen to the terminal device 10 (step S16). This completes the graph creation process. The teacher can display the graph screen on the terminal device 10 and check the graph, groups, and students who fall into the outliers.

[0072] In this embodiment, the test-style exam and the evaluation-style exam are in different formats, but the evaluation-style exam may be included in the test-style exam, and the evaluation points may be included in the score.

[0073] Furthermore, in this embodiment, the test items "Test Score" and "Rubric Evaluation" are specified as the vertical and horizontal axes of the scatter diagram, but the present invention is not limited to this. The vertical and horizontal axes may be selected to represent any of the test items "Test Score," "Rubric Evaluation," and "Survey Response." As described above, in this embodiment, the survey is a question asking about the student's level of understanding, and the smaller the answer number, the lower the student's level of understanding, and the larger the answer number, the higher the student's level of understanding. Therefore, in the survey as well, students whose answer numbers are below a predetermined threshold for survey responses can be considered to be students with poor grades, so the management server 20 can apply a graph creation process.

[0074] In addition, in this embodiment, the indicators are the scores, evaluation points, and answer numbers, which are indicators of grades, but the present invention is not limited to these and can be set arbitrarily as long as it is related to learning. Furthermore, various thresholds can be set arbitrarily.

[0075] In this embodiment, students whose distance from the center of gravity of the integrated area is less than a threshold are classified into the same group as students whose icons are plotted in the closest integrated area. However, the present invention is not limited to this, and the management server 20 may classify into the same group only students whose icons are plotted in the same integrated area. In this case, the management server 20 identifies as outliers students whose distance from the center of gravity of the integrated area is equal to or greater than the icon distance threshold and whose indicators corresponding to the vertical and horizontal axes are equal to or less than the corresponding thresholds.

[0076] Identifying learners who fall under outliers on a graph requires a high workload because it requires consideration of not only the individual learner's performance information but also the overall situation of the learners. For example, while conventional k-means clustering methods can group learners plotted on a graph, identifying outliers is difficult. However, the information display system 100 of the present invention can automatically identify outlier learners in scatter plots that visualize regular test scores, rubric assessment scores, learner survey results, and the like. This reduces the workload of learning support staff, facilitates the proposal of individually optimized instruction for outlier learners, and improves learning efficiency.

[0077] <Modification>

[0078] In the above embodiment, the teacher uses the terminal device 10, but the present invention is not limited to this, and the teacher may use a teacher terminal having the functions of the management server 20. The teacher terminal, like the terminal device 10, may be, for example, a desktop PC, a laptop PC, or a tablet PC.

[0079] Fig. 19 shows an example of the configuration of an information display system 200 in this case. As shown in the figure, a student DB 91, a test DB 92, an evaluation DB 93, a questionnaire DB 94, and a grade DB 95 are connected to a teacher terminal 90 in the information display system 200, and the teacher terminal 90 can execute the processing (corresponding to steps S11 to S16 in Fig. 18) that was previously performed by the management server 20 to create and display information on a graph screen. The teacher terminal 90 can also print the graphs, groups, and information on students corresponding to outliers displayed on the graph screen. In this case, the teacher terminal 90 is an example of the information processing device of the present invention. [Explanation of symbols]

[0080] 5. Network 10 Terminal Equipment 20 Management Server 21 Communications Department 22 Input information acquisition unit 23. Grade Information Acquisition Department 24 Graph Creation Section 25 Group Classification Section 26 Outlier Identification Unit 27 Graph screen output section 31, 91 Student DB 32, 92 Test DB 33, 93 Evaluation DB 34, 94 Survey DB 35, 95 Grade DB 90 Teacher's terminal 100, 200 Information Display System

Claims

1. a storage unit that stores identification information of a plurality of learners and performance information related to the learning of the learners in association with each other; a graph creation unit that creates a scatter diagram as a graph, in which the vertical axis and the horizontal axis represent different test items and the icons of each learner are plotted based on the performance information corresponding to the test items; a group classification unit that classifies the learners into groups in the graph; an outlier identifying unit that identifies an outlier learner from among learners who do not belong to the group in the graph; an output unit that outputs the graph, the group, and the outlier; Equipped with The test items are an index showing the format of a test and its results, or a questionnaire that is an evaluation of a test and its results.

2. the performance information includes indicators relating to a plurality of test items; The information processing device according to claim 1 , wherein the outlier identifying unit identifies, in the graph, a learner who does not belong to the group and whose index included in the performance information is lower than a first threshold, as an outlier.

3. a graph division unit for dividing the graph into a plurality of regions of equal size; a learner ratio calculation unit that calculates a learner ratio, which is the ratio of the number of learners whose icons are plotted in each area; a detection unit that detects an area where the learner ratio is equal to or greater than a second threshold as a detection area; an integration unit that integrates adjacent detection areas into an integrated area, The information processing device according to claim 2 , wherein the group classification unit classifies learners whose icons are plotted in the same integrated area into the same group.

4. The information processing device according to claim 3 , wherein when there is an area adjacent to the detection area and the learner ratio is less than the second threshold but is not 0, the integration unit also integrates the area to create an integrated area.

5. a distance calculation unit that calculates the distance between the center of gravity of the integrated area and an icon of a learner that is not plotted on any integrated area; The information processing apparatus according to claim 3 , wherein the outlier identifying unit identifies a learner whose icon has the distance equal to or greater than a third threshold as an outlier.

6. The information processing device according to claim 5 , wherein the group classification unit classifies a learner whose icon is located at a distance from the integrated region that is less than a threshold into the same group as a learner whose icon is plotted in the integrated region.

7. an input information acquisition unit that acquires different test items as input information for the vertical and horizontal axes of the graph; The information processing apparatus according to claim 2 , wherein the graph creation unit creates the scatter diagram as a graph based on the input information and the performance information.

8. 8. The information processing device according to claim 7, wherein the output unit outputs, based on the performance information, either an index related to the test item that is the target of the vertical axis or an evaluation of the test item, and either an index related to the test item that is the target of the horizontal axis or an evaluation of the test item, for the learner who falls under the outlier.

9. A program executed by an information processing device having a computer, a storage unit that stores identification information of a plurality of learners and performance information related to the learning of the learners in association with each other; a graph creation unit that creates a scatter diagram as a graph, in which the vertical axis and the horizontal axis are different test items and icons of each learner are plotted based on the performance information corresponding to the test items; a group classification unit that classifies the learners into groups in the graph; an outlier identifying unit that identifies, in the graph, a learner who is an outlier from among learners who do not belong to the group; an output unit that outputs the graph, the group, and the outlier; causing the computer to function as The test items are a program that includes a test format and an index showing the results, or a questionnaire that is an evaluation of the test and an index showing the results.

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