Academic ability analysis system, academic ability analysis method, and academic ability analysis program

Abstract

To provide a teaching suitable for each student without increasing the burden of teaching of a teacher.SOLUTION: An academic ability analysis system 100 includes: an acquisition unit for acquiring academic ability data of each student; and a classification unit for classifying the academic ability data of each student. The classification unit determines whether the academic ability data is similar to one another on each of predetermined units and includes the academic ability data including similar academic abilities in one group on a unit-by-unit basis.SELECTED DRAWING: Figure 1

Description

Technical Field

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[0003]

[0001] The present disclosure relates to a technique for analyzing the academic ability of students, and more particularly, to a technique for grouping similar achievement data.

Background Art

[0002] When providing learning guidance to students, guidance suitable for individual students such as guidance to overcome weak subjects or guidance to expand strong subjects is desired. For this purpose, teachers need to understand the characteristics of each student's grades and provide guidance according to the grades. For example, a teacher can understand the characteristics (weak subjects and strong subjects) of each student by grasping the grades of each student in units of arbitrary units. Also, the more the units of grades are subdivided, the more detailed the teacher can understand the characteristics of each student and provide guidance suitable for each student.

[0003] However, it is a great burden for teachers to understand the characteristics of a large number of students in detail and provide guidance suitable for each student. Therefore, there is a need for a technique for educational support that enables teachers to provide guidance suitable for each student while reducing the burden on teachers.

[0004] Regarding technologies for educational support, for example, Japanese Patent Publication No. 2020-098253 (Patent Document 1) discloses an educational support system. This educational support system "includes an action history database that stores action history data based on a learner's learning behavior with respect to a digital textbook; an answer data acquisition unit that acquires answer data based on the learner's answers to questions on a test administered to the learner; a scoring processing unit that performs scoring processing on the questions in the acquired answer data to generate answer data for return; and a scoring result output unit that outputs the answer data for return. The scoring processing unit includes a scoring result data acquisition unit that acquires scoring result data for the test to which scoring attributes are associated in the answer data; and a teaching information assignment unit that assigns teaching information to the questions to which scoring attributes are associated in the scoring result data, based on the learner's action history data stored in the action history database" (see [Abstract]). [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-098253 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] According to the technology disclosed in Patent Document 1, providing instruction tailored to each student increases the burden on teachers. Therefore, there is a need for technology that can provide instruction tailored to each student without increasing the burden on teachers.

[0007] This disclosure is made in light of the above-mentioned background, and in one aspect, its purpose is to provide technology that enables teachers to provide instruction tailored to each student without increasing their teaching burden. [Means for solving the problem]

[0008] According to one embodiment, an academic performance analysis system is provided. The academic performance analysis system comprises an acquisition unit for acquiring performance data for each of several students, and a classification unit for grouping the performance data for each of the several students. The classification unit determines whether each performance data is similar to each other in each of a predetermined set of units, and includes performance data containing similar performances in the same group for each unit.

[0009] In a given scenario, the academic performance analysis system further includes a material selection unit for selecting materials to provide to each of several students. The material selection unit determines which group a student belongs to by comparing the student's academic performance with the group performance data for each group, and then selects materials and / or advice corresponding to the group to which the student belongs.

[0010] In a given scenario, the material selection unit calculates group performance data for each group based on the unit-specific performance of all students in that group, and then selects the materials and / or advice to be provided to the students in that group based on the unit-specific performance included in the group performance data.

[0011] In a given scenario, the material selection unit is configured to determine which group a new student belongs to based on the registration of a new student in the academic performance analysis system, to refer to the learning history and performance trends of one or more past students belonging to the same group, and to select materials and / or advice to provide to the new student based on the learning history and performance trends.

[0012] In a given scenario, performance data may include each student's assessment of their living situation, their learning progress, and / or their mental state.

[0013] In a given scenario, the academic performance analysis system further comprises an analysis unit that selects students whose performance has declined based on the progress of the individual performance of one or more students belonging to a group, and a warning unit that outputs a warning to students whose performance has declined.

[0014] In another embodiment, a method for analyzing academic performance performed by a computer is provided. The method includes the steps of obtaining individual performance data for multiple students and grouping the performance data of the multiple students. The grouping step includes determining whether the performance data are similar to each other in each of a predetermined set of units and including performance data containing similar performance for each unit in the same group.

[0015] In a given scenario, the academic performance analysis method further includes the steps of determining which group a student receiving the materials belongs to by comparing the student's performance with the group performance data of each group, and selecting and providing materials and / or advice corresponding to the group to which the student receiving the materials belongs.

[0016] In a given scenario, the academic performance analysis method further includes, before students receiving the materials select and provide materials and / or advice corresponding to their respective groups, the steps of calculating group performance data for each group based on the unit-specific performance of all students in that group, and selecting materials and / or advice to provide to students in that group based on the unit-specific performance included in the group performance data.

[0017] In a given scenario, the academic performance analysis method further includes the steps of determining the group to which a new student belongs based on the registration of the new student, referring to the learning history and performance trends of one or more past students who belong to the group to which the new student belongs, and selecting learning materials and / or advice to provide to the new student based on the learning history and performance trends.

[0018] In a certain situation, the achievement data includes the living situation evaluation value, learning progress evaluation value, and / or mental evaluation value of each student.

[0019] In a certain situation, the academic ability analysis method further includes the step of selecting students with declining grades based on the trend of the achievements of each of one or more students belonging to a group, and the step of outputting a warning to the students with declining grades.

[0020] According to still another embodiment, a program is provided for causing a computer to execute the above method.

Advantages of the Invention

[0021] According to an embodiment, it is possible to provide guidance suitable for each student without increasing the burden on the teacher's guidance.

[0022] The above and other objects, features, aspects and advantages of this disclosure will become apparent from the following detailed description of the disclosure understood in connection with the accompanying drawings.

Brief Description of the Drawings

[0023] [Figure 1] It is a diagram showing an example of an academic ability analysis system according to this embodiment. [Figure 2] It is a diagram showing an example of the hardware configuration of a device 200 for realizing the academic ability analysis system 100. [Figure 3] It is a diagram showing an example of the functional blocks of the academic ability analysis system 100. [Figure 4] It is a diagram showing an example of achievement data acquisition by the academic ability analysis system 100. [Figure 5] It is a diagram showing an example of the display of group achievement data for each group. [Figure 6] It is a diagram showing an example of the display of students belonging to each group. [Figure 7]It is a diagram showing an example of the display of teaching materials corresponding to each group. [Figure 8] It is a diagram showing an example of the display of advice corresponding to each group. [Figure 9] It is a diagram showing an example of a display for presenting learning items to be noted based on the grade data of students (senior students) classified into groups created previously. [Figure 10] It is a diagram showing an example of the display of the transition of the grades of students belonging to a group. [Figure 11] It is a diagram showing an example of the display regarding the grades of students for each unit. [Figure 12] It is a diagram showing an example of the flow of the process of grouping students. [Figure 13] It is a diagram showing an example of the flow of the process in which the academic ability analysis system 100 provides various services to a user (such as a teacher). [Figure 14] It is a diagram showing a first example of the flow of the process based on the group determination result. [Figure 15] It is a diagram showing a second example of the flow of the process based on the group determination result. [Figure 16] It is a diagram showing a third example of the flow of the process based on the group determination result.

Embodiments for Carrying Out the Invention

[0024] Hereinafter, embodiments of the technical idea according to the present disclosure will be described while referring to the drawings. In the following description, the same parts are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0025] <A. Outline of the Academic Ability Analysis System> FIG. 1 is a diagram showing an example of the academic ability analysis system according to the present embodiment. The academic ability analysis system 100 is a learning support system for enabling the provision of guidance suitable for each student without increasing the burden of teacher guidance. Referring to FIG. 1, the outline of the functions and operations of the academic ability analysis system 100 will be described.

[0026] The learning environment 10 shown in Figure 1 comprises an academic performance analysis system 100 and one or more terminals 110. The academic performance analysis system 100 and the one or more terminals 110 are interconnected via a wired network and / or a wireless network. In one aspect, the academic performance analysis system 100 may consist of one or more personal computers, servers, or other information processing devices. In another aspect, the academic performance analysis system 100 may consist of servers, virtual machines, instances, containers, or a combination thereof built on a cloud environment.

[0027] Terminal 110 transmits student performance data to the academic performance analysis system 100. Terminal 110 can also utilize various learning support functions provided by the academic performance analysis system 100. Terminal 110 may be used by school teachers, cram school teachers, test creators, individuals responsible for entering student performance data, companies that handle the entry of student performance data, or the students themselves. In some cases, terminal 110 includes personal computers, tablets, smartphones, smart glasses, and other information processing terminals.

[0028] The academic performance analysis system 100 according to this embodiment analyzes the academic performance data of multiple students belonging to a school or cram school (for example, by determining whether they are similar or not based on the curriculum guidelines code) and groups the academic performance data. The academic performance analysis system 100 also calculates group performance data based on the multiple performance data classified into the same group. Furthermore, the academic performance analysis system 100 uses the group performance data to provide appropriate teaching materials and / or advice to each student. More specifically, the academic performance analysis system 100 may present the group performance data on a unit basis and provide appropriate teaching materials and / or advice to each student on a unit basis. The academic performance analysis system 100 provides the teaching materials and / or advice to the learning instructor (school teacher, cram school teacher, parent, etc.) or the student themselves.

[0029] In this specification, "performance data" means a set of performance scores (such as test scores) per unit of the curriculum guidelines code. In other words, performance data is a combination of performance scores per unit of the curriculum guidelines code. In some cases, performance data may include performance scores in any unit other than the curriculum guidelines code. In other cases, performance data may include scores for any other aspects of life, such as lifestyle habits, study habits, and mental health.

[0030] In this specification, "grouping performance data" means combining similar combinations of performance grades based on curriculum guideline codes or other arbitrary units into a single group. The operation of the academic performance analysis system 100 will be explained using the example of classifying the performance data of five students (students A to E) based on 100 types of curriculum guideline codes (1 to 100). For example, suppose the trends of each performance combination for curriculum guideline codes 1 to 100 in student A are similar to the trends of each performance combination for curriculum guideline codes 1 to 100 in student B. Also, suppose the trends of each performance combination for curriculum guideline codes 1 to 100 in students C, D, and E are similar. In this case, the academic performance analysis system 100 classifies "each performance combination for curriculum guideline codes 1 to 100 in student A" and "each performance combination for curriculum guideline codes 1 to 100 in student B" as the first group. Similarly, the academic performance analysis system 100 classifies the following as a second group: "the combination of grades for each curriculum guideline code 1 to 100 for student C," "the combination of grades for each curriculum guideline code 1 to 100 for student D," and "the combination of grades for each curriculum guideline code 1 to 100 for student E." In other words, the academic performance analysis system 100 includes grade data with similar characteristics (combinations of grades for each unit) in the same group. From this point forward, the similarity between one set of grade data and another set of grade data may be referred to as "similar grade characteristics."

[0031] In this specification, "group performance data" is calculated based on each performance data classified into the same group. In some cases, group performance data may be the mean or median of each performance data classified into the group. In other cases, group performance data may be a representative value selected from each performance data classified into the group. In yet other cases, group performance data may be a value calculated by any method using each performance data classified into the same group. For example, the group performance data for the first group described above is calculated based on the performance data of student A and student B. The group performance data for the second group is calculated based on the performance data of student C, student D, and student E. Table 120 shows an example of group performance data.

[0032] In this specification, "unit" refers to a collection of multiple curriculum guideline codes. For example, the academic ability analysis system 100 may group each of the curriculum guideline codes 1-10, 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, 81-90, and 91-100 into units 1-10. A unit may also be a collection of related curriculum guideline codes, such as Unit 1 for the first range of Mathematics I (subject) (curriculum guideline codes 1-10) and Unit 2 for the second range of Mathematics I (curriculum guideline codes 11-20).

[0033] In this specification, "user" includes any user such as school teachers, cram school teachers, parents, students, instructors at any facility, or employee training personnel at a company. The academic performance analysis system 100 assists these users in selecting teaching materials and / or advice according to the student's academic performance.

[0034] More specifically, the academic performance analysis system 100 first determines which group a student receiving the learning materials belongs to. The academic performance analysis system 100 can make this determination by comparing the performance data of the student receiving the learning materials with the group performance data of each group. The group to which a student receiving the learning materials belongs is a group whose performance characteristics are similar to those of the student receiving the learning materials. In some cases, the academic performance analysis system 100 may be provided as a cloud service or a web application. In this case, the academic performance analysis system 100 can display information on the relevant group for one or more students (students receiving learning materials) selected by the client software or browser on the user's terminal 110.

[0035] Next, the academic performance analysis system 100 selects the learning materials and / or advice (content for each group) that correspond to the group to which the students being provided belong. Then, the academic performance analysis system 100 presents the selected learning materials and / or advice to the user. In some cases, the learning materials and / or advice corresponding to a group may be learning materials and / or advice that are appropriate for the academic performance of the students belonging to that group. In other cases, the learning materials and / or advice corresponding to a group may be learning materials and / or advice that are pre-associated with the group.

[0036] For example, suppose the student who is provided with the learning materials is Student 1, and Student 1 belongs to Group X. In this case, the academic ability analysis system 100 proposes to the user the learning materials and / or advice corresponding to Group X as the learning materials and / or advice to be provided to Student 1.

[0037] As described above, the academic ability analysis system 100 groups a plurality of academic achievement data according to the characteristics of the academic achievements. Then, the academic ability analysis system 100 proposes teaching materials and / or advice to the students belonging to each group in group units. For example, assume that the academic ability analysis system 100 has pre-classified the academic achievement data of 1000 students into 10 groups. In this case, by using the academic ability analysis system 100, the user (teacher) does not need to consider 1000 sets of teaching materials and / or advice, and can teach 1000 students using only 10 sets of teaching materials and / or advice. As a result, the burden on the user (teacher) is greatly reduced. Also, each group is a set of students with similar academic achievement characteristics. Therefore, even when the same teaching materials and / or advice are provided to a large number of students belonging to the same group, a high learning effect can be expected.

[0038] <B. Configuration of Academic Ability Analysis System> FIG. 2 is a diagram showing an example of the hardware configuration of the device 200 for realizing the academic ability analysis system 100. In one aspect, the academic ability analysis system 100 may be realized using one or more devices 200. In another aspect, the academic ability analysis system 100 may be constructed on a cloud environment. In this case, the cloud environment may be realized using one or more devices 200. Alternatively, each device for constructing the cloud environment may not include a part of the hardware of the device 200 according to its use. Also, in another aspect, the terminal 110 may have the same hardware configuration as the device 200.

[0039] The device 200 includes a CPU (Central Processing Unit) 1, a primary storage device 2, a secondary storage device 3, an external device interface 4, an input interface 5, an output interface 6, and a communication interface 7.

[0040] CPU1 can execute programs to implement various functions of the device 200. CPU1 is composed of, for example, at least one integrated circuit. The integrated circuit may consist of, for example, at least one CPU (Central Processing Unit), at least one GPU (Graphics Processing Unit), at least one FPGA (Field Programmable Gate Array), at least one ASIC (Application Specific Integrated Circuit), or a combination thereof.

[0041] The primary storage device 2 stores the program executed by the CPU 1 and the data referenced by the CPU 1. In some cases, the primary storage device 2 may be implemented by DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory), etc.

[0042] The secondary storage device 3 is a non-volatile memory and may store programs executed by the CPU 1 and data referenced by the CPU 1. In this case, the CPU 1 executes programs read from the secondary storage device 3 to the primary storage device 2 and references data read from the secondary storage device 3 to the primary storage device 2. In some cases, the secondary storage device 3 may be implemented by an HDD (Hard Disk Drive), SSD (Solid State Drive), EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), or flash memory, etc.

[0043] The external device interface 4 can be connected to any external device such as a printer, scanner, and external HDD. In some cases, the external device interface 4 may be implemented by a USB (Universal Serial Bus) terminal or the like.

[0044] The input interface 5 can be connected to any input device such as a keyboard, mouse, touchpad, or gamepad. In some cases, the input interface 5 may be implemented by a USB terminal, PS / 2 terminal, and Bluetooth® module, etc.

[0045] The output interface 6 can be connected to any output device such as a cathode ray tube display, a liquid crystal display, or an organic EL (Electro-Luminescence) display. In some cases, the output interface 6 may be implemented by a USB terminal, a D-sub terminal, a DVI (Digital Visual Interface) terminal, an HDMI® (High-Definition Multimedia Interface) terminal, or a DisplayPort terminal.

[0046] The communication interface 7 is connected to other devices via a wired or wireless network. In some cases, the communication interface 7 may be implemented by a wired LAN (Local Area Network) port and a Wi-Fi® (Wireless Fidelity) module, etc. In other cases, the communication interface 7 may send and receive data using communication protocols such as TCP / IP (Transmission Control Protocol / Internet Protocol) and UDP (User Datagram Protocol).

[0047] Figure 3 shows an example of a functional block of the academic ability analysis system 100. In some cases, each functional block shown in Figure 3 may be implemented as a program. In this case, each functional block shown in Figure 3 can be implemented by the hardware shown in Figure 2 working together to execute the program. In other cases, some of the functional blocks shown in Figure 3 may be implemented as hardware. In this case, the academic ability analysis system 100 further includes hardware configurations such as FPGAs and ASICs to implement some of the functional blocks.

[0048] The academic performance analysis system 100 comprises, as functional blocks, an acquisition unit 301, a classification unit 302, an analysis unit 303, a teaching material selection unit 304, an advice selection unit 305, a warning unit 306, an output unit 307, and a storage unit 310. The storage unit 310 includes a performance data DB (Database) 311, a classification data DB 312, a teaching material data DB 313, and an advice data DB 314.

[0049] The acquisition unit 301 collects performance data from the terminal 110. In addition to test results, the performance data may also include evaluation values ​​of each student's living situation, learning progress, and / or mental state. These evaluation values ​​can be obtained, for example, from questionnaire results. In some cases, the performance data may include information such as correct / incorrect answers on tests, scores, and curriculum code. In other cases, the performance data may include information such as questionnaire responses, scores on certain items in the questionnaire, and units. The acquisition unit 301 stores the acquired performance data in the performance data DB 311. The acquisition unit 301 may also output the acquired performance data to the classification unit 302 and / or the analysis unit 303.

[0050] The classification unit 302 groups the performance data based on the performance data entered from the acquisition unit 301 or read from the performance data DB 311. More specifically, the classification unit 302 first reads each student's performance data and calculates the performance for each curriculum code from multiple test results, etc. For example, the classification unit 302 calculates the performance for each student for each curriculum code based on the correct / incorrect answers, scores, and curriculum code information of the test answers included in each student's performance data.

[0051] Next, the classification unit 302 groups together grade data with similar grades for each curriculum code. For example, the classification unit 302 may group the grade data of students 1-10 into group X, the grade data of students 11-20 into group Y, and the grade data of students 21-30 into group Z. The classification unit 302 stores the classification results in the classification data DB 312.

[0052] The analysis unit 303 performs various analyses based on the group performance data. The analysis unit 303 refers to the performance data DB 311, classification data DB 312, teaching material data DB 313, and advice data DB 314 as needed. The analysis unit 303 stores the analysis results in the storage unit 310, or outputs them to the warning unit 306 and / or output unit 307. The group performance data for each group can be represented in a radar chart or the like, as shown in Figure 4. The academic ability analysis system 100 can consolidate multiple curriculum code standards into a single unit. In the example radar chart shown in Figure 4, the curriculum code standards are consolidated into 10 items (units).

[0053] In certain situations, the analysis unit 303 may associate teaching materials and / or advice with each group. The teaching material data DB 313 stores teaching material information by level for each unit. The analysis unit 303 may pre-select teaching materials appropriate for the students belonging to a group from the teaching material data DB 313 according to the group performance data, and associate the selected teaching materials with the group. The advice data DB 314 stores advice by level for each unit. The analysis unit 303 may pre-select advice appropriate for the students belonging to a group from the advice data DB 314 according to the group performance data, and associate the selected advice with the group. The analysis unit 303 may store the information to be associated with groups in the classification data DB 312.

[0054] In other scenarios, the analysis unit 303 may monitor the progress (and learning history) of each student belonging to the group and detect students whose grades are declining. In this case, the warning unit 306 or output unit 307 may output information about the students whose grades are declining to the user.

[0055] In other situations, the analysis unit 303 may receive a request from the classification unit 302 to analyze the performance data for each of a predetermined set of units and to calculate the performance for each unit for multiple students. In this case, the classification unit 302 outputs the calculated performance for each unit for multiple students to the analysis unit 303.

[0056] Furthermore, in other scenarios, the analysis unit 303 may be responsible for some of the processing of the classification unit 302, the teaching material selection unit 304, and the advice selection unit 305. In addition, in other scenarios, the analysis unit 303 may function as the highest-level functional block of the academic ability analysis system 100. In this case, the analysis unit 303 calls other functional blocks to realize all the functions provided by the academic ability analysis system 100.

[0057] The material selection unit 304 selects materials from the material data DB 313 to be provided to students belonging to each group. More specifically, the material selection unit 304 calculates group performance data for each group. Then, based on the group performance data, the material selection unit 304 selects materials from the material data DB 313 to be provided to students belonging to each group. For example, the material selection unit 304 may select materials that are expected to improve performance in each unit based on the performance in each unit included in the group performance data. For example, the material selection unit 304 may suggest introductory materials for unit A to students with low performance in unit A, and suggest advanced materials for unit A to students with high performance in unit A. The material selection unit 304 outputs information of the selected materials to the output unit 307. As an example, the material selection unit 304 may select materials corresponding to the group to which a student belongs based on information obtained from a user interface (not shown) provided to the terminal 110.

[0058] In some situations, the material selection unit 304 may select materials to provide to students belonging to each group from the material data DB 313 based on group performance data. In other situations, if materials have been pre-associated with each group by the analysis unit 303, the material selection unit 304 may select the materials that have been pre-associated with each group.

[0059] In other situations, the material selection unit 304 may select materials to provide to a newly registered student in the academic ability analysis system 100 based on the performance trends and learning history of past students (senior students) belonging to groups created in the past. More specifically, based on the registration of a new student in the academic ability analysis system 100, the material selection unit 304 compares the performance data of each of the one or more groups created in the past with the performance data of the new student. Next, the material selection unit 304 selects a group that has group performance data similar to the new student's performance data as the group to which the new student belongs. Next, the material selection unit 304 selects materials and / or advice to provide to the new student (junior student) based on the past learning history of one or more students belonging to the selected group. The material selection unit 304 may also perform some analysis processing in cooperation with the analysis unit 303.

[0060] The advice selection unit 305 selects advice to be provided to students belonging to each group from the advice data DB 314. More specifically, the advice selection unit 305 calculates group performance data for each group. In some cases, the advice selection unit 305 may reuse the group performance data calculated by the analysis unit 303 or the teaching material selection unit 304. Then, based on the group performance data, the advice selection unit 305 selects advice to be provided to students belonging to each group from the advice data DB 314. For example, the advice selection unit 305 may select advice that is expected to improve performance in each unit based on the performance in each unit included in the group performance data. The advice selection unit 305 outputs the information of the selected advice to the output unit 307. As an example, the advice selection unit 305 may select advice corresponding to the group to which a student belongs based on information obtained from the user interface provided to the terminal 110.

[0061] In some situations, the advice selection unit 305 may select advice to provide to students belonging to each group from the advice data DB 314 based on group performance data. In other situations, if advice has been pre-associated with each group by the analysis unit 303, the advice selection unit 305 may select the advice that has been pre-associated with each group.

[0062] Furthermore, teaching materials and advice may be closely related. Therefore, in certain situations, the teaching material selection unit 304 may include the advice selection unit 305.

[0063] The warning unit 306 outputs warnings regarding students. For example, based on information obtained from the analysis unit 303 (such as student performance trend data), the warning unit 306 may output information to the user about students who have problems with their grades or who are at risk of their grades declining. In some situations, the warning unit 306 may send warning information to the user's terminal 110 via email. In other situations, the warning unit 306 may send warning information to the user's terminal 110 for display on the screen of a browser or client software.

[0064] The output unit 307 outputs various information. In some situations, the output unit 307 may send various information to the user's terminal 110 for display on the screen of a browser or client software. The output unit 307 may also send a user interface to the terminal 110 for the user to use the various functions of the academic ability analysis system 100. The user can send various requests to the academic ability analysis system 100 and refer to the information output by the academic ability analysis system 100 via the user interface. The academic ability analysis system 100 can perform various processes based on the requests received via the user interface. In some situations, the acquisition unit 301 may acquire the requests received via the user interface. In other situations, another input unit (not shown) may acquire the requests received via the user interface.

[0065] The memory unit 310 stores various types of information used by the academic performance analysis system 100. In some cases, the memory unit 310 may be a database management system (DBMS). In this case, the grade data DB311, classification data DB312, teaching material data DB313, and advice data DB314 are implemented as relational databases. In other cases, the memory unit 310 may be a NoSQL type database system in any format, or it may simply be a storage area.

[0066] The grade data DB311 stores each student's grade data. For example, a record in the grade data DB311 may include arbitrary information such as student ID (Identifier), student name, population ID, population name, test ID, test name, question ID, question name, unit ID, unit name, correct / incorrect answer information for the questions, and question score information.

[0067] The classification data DB312 stores data for each group. For example, records in the classification data DB312 may include arbitrary information such as group ID, group name, group performance data, information on teaching materials associated with the group, and information on advice associated with the group.

[0068] The curriculum data database DB313 stores curriculum data categorized by unit and level. For example, records in curriculum data DB313 may include arbitrary information such as curriculum ID, curriculum name, link to the curriculum, unit ID, curriculum level, and target student performance range information.

[0069] The advice data DB314 stores advice data categorized by unit level. For example, a record in the advice data DB314 may include arbitrary information such as advice ID, advice, unit ID, material ID of the related material, advice level, and the range of the target student's grades. The storage unit 310 may also include a student DB (not shown) for storing student information.

[0070] <C. Specific Examples of Academic Ability Analysis and Learning Support> Next, referring to FIGS. 4 to 11, specific examples of learning support of the academic ability analysis system 100 will be described.

[0071] FIG. 4 is a diagram showing an example of acquisition of score data by the academic ability analysis system 100. Referring to FIG. 4, the score data acquired by the academic ability analysis system 100 will be described.

[0072] The academic ability analysis system 100 collects score data from the population 400. The population 400 may include any group. For example, the population 400 may be a group of any unit such as a school class, a school grade, a school, a local government, a country, etc. The user (such as a teacher, a cram school instructor, an employee of a test preparation company, etc.) inputs score data such as test results received by the population 400 into the terminal 110. The terminal 110 transmits the input score data to the academic ability analysis system 100.

[0073] Table 420 shows an example of the configuration of the input score data. The terminal 110 may display a user interface for inputting each item of Table 420 on the screen. Note that the academic ability analysis system 100 may transmit the user interface to the terminal 110.

[0074] [[ID=,19]]Table 420 includes a test taker (student) ID and test scores. The test scores are input to the terminal 110, for example, in association with a learning guideline code or the like.

[0075] [[ID=,22]] For example, each test question is associated with a learning guideline code or the like. The user may input the test taker (student) ID and the score for each learning guideline code into the terminal 110. In a certain situation, when a test using the terminal 110 is conducted, the terminal 110 may automatically aggregate the scores for each learning guideline code for each test taker (student) and transmit the score data to the academic ability analysis system 100.

[0076] Figure 5 shows an example of how group performance data is displayed for each group. The academic performance analysis system 100 groups each student's performance data based on the performance data received from the terminal 110. The academic performance analysis system 100 then calculates group performance data for each group. Furthermore, the academic performance analysis system 100 can generate a graph, such as display 500, from the group performance data for each group.

[0077] Display 500 shows an example of a radar chart of group performance data for five groups, 1 to 5. In the example in Figure 5, the academic performance analysis system 100 groups (classifies) each student's performance data into five groups and generates a radar chart of the group performance data for each group. Items 1 to 10 each represent a different unit. The group performance data is a unit that includes multiple curriculum guideline codes. The academic performance analysis system 100 calculates the performance for each unit based on the performance of each curriculum guideline code. In some cases, the academic performance analysis system 100 may use the average or median of the performance of each curriculum guideline code included in a unit as the performance for that unit. In other cases, the academic performance analysis system 100 may use a representative value of the performance of each curriculum guideline code included in a unit as the performance for that unit. Note that the radar chart is just an example, and the academic performance analysis system 100 may generate any graph such as a bar graph or line graph.

[0078] The academic performance analysis system 100 (output unit 307) is configured to output generated graphs (e.g., display 500) and / or tables to the user (teacher, etc.). By referring to the graphs and / or tables, the user can understand how the population 400 is classified into groups and what performance trends each group has.

[0079] Figure 6 shows an example of how students belonging to each group are displayed. The academic performance analysis system 100 can output information on students belonging to each group, as shown in display 600.

[0080] Display 600 shows a list of students belonging to each of the five groups 1 to 5. In certain situations, the academic performance analysis system 100 may output a pie chart or the like to show the percentage of students belonging to each group.

[0081] The academic performance analysis system 100 (output unit 307) is configured to output information (for example, display 600) of students belonging to each generated group to the user (teacher, etc.). By referring to the information of students belonging to each group, the user can understand which students belong to each group.

[0082] Figure 7 shows an example of how teaching materials are displayed for each group. The academic ability analysis system 100 can output information on teaching materials corresponding to each group, as shown in display 700.

[0083] The academic ability analysis system 100 (output unit 307) is configured to output information on teaching materials corresponding to each generated group (for example, display 700) to the user (teacher, etc.). By referring to the information on teaching materials corresponding to each group, the user can understand which teaching materials should be provided to the students belonging to each group.

[0084] As described above, the academic performance analysis system 100 (material selection unit 304) provides materials suitable for the group performance data of each group to all students belonging to that group. In other words, the academic performance analysis system 100 selects materials suitable for a hypothetical student who has group performance data for a certain group, and provides materials suitable for that hypothetical student to all students belonging to that group.

[0085] Figure 8 shows an example of how advice corresponding to each group is displayed. The academic performance analysis system 100 can output information on advice corresponding to each group, as shown in display 800.

[0086] The academic performance analysis system 100 (output unit 307) is configured to output advice information (e.g., display 800) corresponding to each generated group to the user (teacher, etc.). By referring to the advice information corresponding to each group, the user can understand how to advise the students belonging to each group.

[0087] As described above, the academic performance analysis system 100 (advice selection unit 305) provides advice appropriate to the group performance data of each group to all students belonging to that group. In other words, the academic performance analysis system 100 selects advice appropriate to a hypothetical student who has group performance data for a certain group, and provides that advice appropriate to that hypothetical student to all students belonging to that group.

[0088] Figure 9 shows an example of a display for highlighting noteworthy learning items based on the performance data of students (senior students) who have been classified into previously created groups. The academic performance analysis system 100 can output information on the learning history and performance trends of students (senior students) who have been classified into previously created groups, as shown in display 900.

[0089] Display 900 shows a comparison of the lowest-performing student (senior student) and the highest-performing student (senior student) for each unit (items 1-10) within a given group. According to Display 900, there are differences within the group (among senior students) in items 3 and 8. This suggests that items 3 and 8 are units where differences in performance are likely to occur among students classified into that particular group.

[0090] As mentioned above, the students belonging to each group tend to have similar academic performance. For example, suppose student A, who belongs to Group 1, significantly improved their performance in Unit X. In this case, it is suggested that other students in Group 1 also have the potential to significantly improve their performance in Unit X. Similarly, suppose student A, who belongs to Group 1, significantly decreased their performance in Unit Y. In this case, it is suggested that other students in Group 1 also have the potential to significantly decrease their performance in Unit Y.

[0091] Using the example of Display 900, it can be seen that in items (units) 3 and 8, students belonging to a certain group have both the potential to improve and the potential to decline their grades. The academic performance analysis system 100 presents items with a large difference between low-performing and high-performing students as learning items that require attention, as shown in Display 900. In certain situations, the academic performance analysis system 100 may highlight items with a large difference between low-performing and high-performing students in any way it chooses. By referring to Display 900, users can understand which subjects students should focus on in particular.

[0092] Furthermore, in certain situations, the academic ability analysis system 100 may select teaching materials and / or advice along with the display results such as the display 900. For example, if the academic ability analysis system 100 (analysis unit 303 or teaching material selection unit 304) finds that there is a tendency for differences in performance in items 3 and 8 among students classified into a certain group, it may select teaching materials and / or advice for new students (junior students) classified into that group to focus on studying items 3 and 8.

[0093] Furthermore, the academic performance analysis system 100 (output unit 307) is configured to output information on teaching materials and / or advice for new students (junior students) selected using the academic performance trends and learning history of past students (senior students), along with the display 900 (performance trends (and learning history) of senior students). By referring to the information on teaching materials and / or advice selected using the learning history of past students (senior students), users can provide new students (junior students) with an effective learning plan.

[0094] In this way, the academic ability analysis system 100 analyzes the progress of past unit (items 1 to 10) performance of multiple students (senior students) classified into a certain group. This analysis may include arbitrary analyses such as units where differences in performance are likely to occur within the group, units where performance is likely to improve within the group, and units where performance is unlikely to improve within the group. Based on the analysis results, the academic ability analysis system 100 can then propose and output a learning plan for students (junior students) newly classified into the group (it may also output selected learning materials and / or advice).

[0095] Figure 10 shows an example of a display showing the trends in the academic performance of students belonging to a group. The academic performance analysis system 100 can output the trends in group performance data for each group, as shown in Display 1000. The line graph in Display 1000 can show, for example, the total of the group performance data, or the trends in the average performance for each unit included in the group performance data. The time-series group performance data included in Display 1000 is calculated based on the time-series performance data of students belonging to each group. By referring to Display 1000, the user can identify group 1001, which shows particularly large fluctuations. In addition, if there is a large change in the group performance data of any group, the academic performance analysis system 100 may display a warning 1002. The user can, for example, select warning 1002 to transition the screen to Display 1010.

[0096] Based on the selection of warning 1002, the academic performance analysis system 100 may output the progress of students' performance in each group, as shown in display 1010. Here, progress in performance refers to the average score of each student for each unit. In certain situations, the academic performance analysis system 100 may output the progress of students' performance in each group for each unit.

[0097] The academic performance analysis system 100 (warning unit 306, output unit 307) may output warnings to the user (teacher, etc.) along with the display 1010 regarding students who have problems with their grades or who may have problems with their grades in the future. By referring to the display 1010 and the warning information, the user can take early and appropriate action regarding students who have problems with their grades and / or who may have problems with their grades in the future. In the example of the display 1010, the academic performance of students A17 and A33 has declined. In this case, the user can understand this fact based on the display 1010 and the warnings, etc., and take care of students A17 and A33.

[0098] Figure 11 shows an example of a display of a student's performance per unit. The academic performance analysis system 100 can output the performance of individual students per unit, as shown in display 1101. The academic performance analysis system 100 can also output the trends of evaluation values ​​other than student performance (lifestyle, learning status, mental evaluation value), as shown in display 1102. In addition, the academic performance analysis system 100 can display any analysis results of a student's performance. Users (teachers, etc.) can refer to displays 1101, 1102, or any other analysis results of a student's performance to provide detailed support to individual students.

[0099] In certain situations, the academic performance analysis system 100 (warning unit 306, output unit 307) may display various displays, as shown in Figures 5 to 11, to the student and / or the student's family. In this case, the academic performance analysis system 100 may remove information about other students from the information it displays to a particular student and / or their family in order to protect the privacy of other students. For example, when the academic performance analysis system 100 displays display 600 to a particular student, it may only display the name of the group to which that student belongs, and conceal information about which group other students belong to.

[0100] In other situations, the academic performance analysis system 100 (warning unit 306, output unit 307) may distribute to the terminal 110 a user interface configured to allow switching between various displays as shown in Figures 5 to 11.

[0101] <D.フローチャート> Next, the internal processing of the academic ability analysis system 100 will be described with reference to Figures 12 to 16. In one phase, the CPU 1 reads a program for performing the processing shown in Figures 12 to 16 from the secondary storage device 3 to the primary storage device 2 and executes the program. In other phases, part or all of the processing can also be realized as a combination of circuit elements configured to perform the processing.

[0102] Figure 12 shows an example of the process flow for grouping students. In step S1210, the academic performance analysis system 100 accepts the input of grade data. More specifically, the user inputs grade data into terminal 110. Terminal 110 transmits the grade data to the academic performance analysis system 100. The academic performance analysis system 100 stores the grade data received from each terminal 110 in the grade data DB 311.

[0103] In step S1220, the academic performance analysis system 100 performs a grouping process. More specifically, the academic performance analysis system 100 uses a clustering algorithm or the like to group each student's performance data based on the similarity of their performance for any unit, such as the curriculum code. In this step, the academic performance analysis system 100 may also calculate performance for each group based on a unit that combines multiple curriculum codes.

[0104] In step S1230, the academic ability analysis system 100 performs a group registration process. More specifically, the academic ability analysis system 100 links the information of one or more generated groups with the information of one or more students classified (corresponding) to each group and registers them in the classification data DB312.

[0105] In step S1240, the academic performance analysis system 100 performs evaluation registration for each group. More specifically, the academic performance analysis system 100 calculates group performance data as shown in Figure 5. Furthermore, in this step, the academic performance analysis system 100 may select teaching materials to provide to each group, as shown in Figure 7, and select advice to provide to each group, as shown in Figure 8, based on the group performance data for each group.

[0106] Figure 13 shows an example of the processing flow in which the academic performance analysis system 100 provides various services to users (teachers, etc.).

[0107] In step S1310, the academic performance analysis system 100 accepts input of the academic performance data of the students to be analyzed. More specifically, the academic performance analysis system 100 receives the academic performance data of the students to be analyzed from the user's terminal 110.

[0108] In certain situations, the academic performance analysis system 100 may accept only the student's ID or name if the students to be analyzed have already been grouped. In other situations, the academic performance analysis system 100 may accept the performance data of multiple students, or the IDs or names of multiple students.

[0109] In step S1320, the academic ability analysis system 100 determines which group the student being analyzed belongs to (is classified into). If the academic ability analysis system 100 has received the student's performance data, it compares the student's performance with the group performance data for each group. The academic ability analysis system 100 then determines that the group with similar group performance data to the student's performance data is the group to which the student being analyzed belongs (is classified into). If the academic ability analysis system 100 has received the student's ID, it refers to the classification data DB312 and determines that the group associated with the student's ID is the group to which the student being analyzed belongs (is classified into).

[0110] In step S1330, the academic ability analysis system 100 executes processing based on the group determination results. The academic ability analysis system 100 may execute one or more of the processes shown in Figures 14 to 16 as subroutines for processing based on the group determination results. In a given situation, the academic ability analysis system 100 may select the process to execute based on the user's operation in the user interface provided to the terminal 110. The process to execute here means one or any combination of the processes shown in Figures 14 to 16.

[0111] Figure 14 shows a first example of the processing flow based on the group determination results. The process shown in Figure 14 is the process of displaying a list of students belonging to each group. The academic ability analysis system 100 executes the process shown in Figure 14 when it receives a request from terminal 110 to display a list of students belonging to each group, etc.

[0112] In step S1410, the academic performance analysis system 100 aggregates the number of students belonging to each group. More specifically, the academic performance analysis system 100 can refer to the classification data DB312 to count the number of students belonging to each group.

[0113] In step S1420, the academic performance analysis system 100 displays the number of students belonging to each group and a list of students, as shown in display 600. Display 600 is displayed on the user interface of terminal 110. Terminal 110 receives various information necessary for display 600 from the academic performance analysis system 100. This information may include information about the groups, HTML (Hyper Text Markup Language), CSS (Cascading Style Sheets), Javascript (registered trademark), etc.

[0114] Figure 15 shows a second example of the processing flow based on the group determination results. The academic ability analysis system 100 executes the processing shown in Figure 15 when it receives a request from terminal 110 to display teaching methods for students belonging to each group, etc. Alternatively, the academic ability analysis system 100 may execute the processing shown in Figure 15 after receiving user input or automatically after the processing in step S1420.

[0115] In step S1510, the academic ability analysis system 100 extracts teaching policies associated with the group of students being evaluated from the teaching material data DB313 and the advice data DB314. More specifically, the academic ability analysis system 100 obtains teaching materials extracted from the teaching material data DB313 by the teaching material selection unit 304 and advice extracted from the advice data DB314 by the advice selection unit 305 as teaching policies.

[0116] In step S1520, the academic performance analysis system 100 displays the teaching policy (teaching materials and advice) for each group, as shown in displays 700 and 800. These displays 700 and 800 are shown on the user interface of terminal 110. Terminal 110 receives various information necessary for displays 700 and 800 from the academic performance analysis system 100. This information may include information related to the teaching policy, HTML, CSS, Javascript, etc.

[0117] Figure 16 shows a third example of the processing flow based on the group determination results. The academic ability analysis system 100 executes the processing shown in Figure 16 when it receives a request from terminal 110 to display learning guidance based on the analysis results of past student learning history (learning history of senior students).

[0118] In step S1610, the academic performance analysis system 100 retrieves the initial grades of students who were previously in the same group as the student being analyzed (senior students). More specifically, the academic performance analysis system 100 retrieves the initial grades (at the beginning of the school year, etc.) of senior students.

[0119] In step S1620, the academic performance analysis system 100 obtains the grades of senior students from the beginning to a predetermined period of time. More specifically, the academic performance analysis system 100 obtains the grades of senior students classified in the same group as the student being analyzed, from the beginning to a predetermined period of time (e.g., the end of the school year).

[0120] In step S1630, the academic performance analysis system 100 detects the specific characteristics (units, etc.) that have improved in senior students whose grades have improved. To explain using Figure 9 as an example, the processing in this step corresponds to detecting items 3 and 8 from the display 900 as specific characteristics (units) that have improved.

[0121] In step S1640, the academic performance analysis system 100 presents specific characteristics as comments for the student being evaluated. As illustrated in Figure 9, the academic performance analysis system 100 generates advice to focus on learning items 3 and 8. In some situations, the academic performance analysis system 100 may generate comments based on any analysis results other than the specific characteristics that have improved (the units in which the student has grown). In other situations, the academic performance analysis system 100 may present the user with learning materials based on the analysis results, in addition to advice.

[0122] As described above, the academic performance analysis system 100 according to this embodiment groups multiple students based on their performance in each unit. The academic performance analysis system 100 then selects and outputs teaching materials and / or advice for each group. In this way, the academic performance analysis system 100 can reduce the number of curricula, etc., from the number of students to the number of groups, thereby reducing the burden on teachers.

[0123] Furthermore, the academic performance analysis system 100 provides common learning materials and / or advice that are expected to improve the performance of multiple students with similar academic performance (students belonging to the same group). This allows the academic performance analysis system 100 to provide each student with a learning plan that is expected to have a learning effect similar to individualized instruction.

[0124] The embodiments disclosed herein should be considered in all respects as illustrative and not restrictive. The scope of this disclosure is indicated by the claims rather than the foregoing description, and all modifications are intended to be equivalent to the claims. Furthermore, the disclosures described in the embodiments and each variation are intended to be implemented, as far as possible, individually or in combination. [Explanation of symbols]

[0125] 1 CPU, 2 Primary memory, 3 Secondary memory, 4 External device interface, 5 Input interface, 6 Output interface, 7 Communication interface, 10 Learning environment, 100 Academic ability analysis system, 110 Terminal, 120 Table, 200 Device, 301 Acquisition unit, 302 Classification unit, 303 Analysis unit, 304 Material selection unit, 305 Advice selection unit, 306 Warning unit, 307 Output unit, 310 Memory unit, 311 Grade data DB, 312 Classification data DB, 313 Material data DB, 314 Advice data DB, 400 Population, 500, 600, 700, 800, 900, 1000, 1010, 1101, 1102 Display.

Claims

1. A data acquisition unit for obtaining the individual academic performance data of multiple students, A classification unit for grouping the performance data of each of the aforementioned multiple students, The system includes a material selection unit for selecting materials to be provided to each of the aforementioned multiple students, The aforementioned classification unit is It is determined whether each of the aforementioned performance data is similar to the others in each of the predetermined units. The performance data containing similar performance for each unit are included in the same group. The aforementioned material selection unit is, For each group, group performance data is calculated based on the performance of all students belonging to that group for each unit. By comparing the academic performance of students who are provided with the aforementioned group performance data for each group, it is determined which group each student who is provided with the materials belongs to. For each of the aforementioned groups, based on the performance of each of the aforementioned units included in the group performance data, select the teaching materials and / or advice to be provided to the students belonging to the aforementioned group. The materials are configured such that the students to whom the materials are provided select the materials and / or advice corresponding to the group in question. The aforementioned material selection unit further, based on the registration of a new student in the academic performance analysis system, The group to which the new student belongs is determined, The learning history and academic progress of one or more past students who belong to the group to which the new student belongs, A learning ability analysis system configured to enable the selection of teaching materials and / or advice to be provided to the new student based on the learning history and the progress of academic performance.

2. The academic performance analysis system according to claim 1, wherein the performance data includes an evaluation value for each student's living situation, an evaluation value for their learning progress, and / or a mental evaluation value.

3. Based on the trends in the performance of one or more students belonging to the aforementioned group, an analysis unit selects students whose performance has declined, The academic performance analysis system according to claim 1 or 2, further comprising a warning unit that outputs a warning to students whose grades are declining.

4. A method of analyzing academic ability performed by a computer, Steps to obtain individual academic performance data for multiple students, The step includes grouping the performance data of the aforementioned multiple students, The grouping step described above is, The steps include determining whether the aforementioned performance data are similar to each other in each of a predetermined set of units, The steps include including the step of including the performance data, which includes similar performance for each unit, in the same group, For each group, the steps include: calculating group performance data based on the performance of all students in each unit who belong to that group; The steps include determining which group a student receiving the learning materials belongs to by comparing the student's academic performance with the group performance data for each group, For each of the aforementioned groups, the step of selecting the teaching materials and / or advice to be provided to the students belonging to the group, based on the performance of each unit included in the group performance data, The steps include: a student being provided with the aforementioned materials selects and provides the materials and / or advice corresponding to the group to which they belong; Based on the registration of a new student, The steps include determining which group the new student belongs to, The steps include referring to the learning history and academic progress of one or more past students who belong to the group to which the new student belongs, A method for analyzing academic ability, further comprising the step of selecting the teaching materials and / or advice to provide to the new student based on the learning history and the progress of the academic performance.

5. The academic performance analysis method according to claim 4, wherein the performance data includes an evaluation value for each student's living situation, an evaluation value for their learning progress, and / or a mental evaluation value.

6. Based on the progress of the grades of one or more students belonging to the aforementioned group, a step is to select students whose grades have declined, The academic performance analysis method according to claim 4, further comprising the step of outputting a warning to a student whose grades have declined.

7. A program for causing a computer to perform the procedure described in any one of claims 4 to 6.

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