Evaluation program and equipment

Abstract

To provide an evaluation program capable of performing objective learning evaluation involving not only academic ability but also other learning-related aspects.SOLUTION: An evaluation program provided herein makes a computer perform steps of acquiring academic ability information on a subject under evaluation (S83), acquiring learning status information on the subject under evaluation (S84), and evaluating the subject under evaluation using a combination of the academic ability information and the learning status information (S85-S88).SELECTED DRAWING: Figure 26

Description

Technical Field

[0006] , ,

[0005] , ,

[0001] This disclosure relates to an evaluation program and apparatus.

Background Art

[0002] Japanese Unexamined Patent Application Publication No. 2021-56364 (Patent Document 1) discloses an information processing apparatus having a generation means for generating a viewing history of learning content on a terminal used by a student, and a display means for displaying the grades of the student for a test in association with the viewing history.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, it has been desired to utilize the evaluation results of learning not only for grasping the academic ability of learners but also for purposes such as improving learning, evaluating teacher guidance, and improving guidance. In order to utilize the evaluation results of learning for such purposes, it is preferable to conduct a learning evaluation including not only the academic ability indicated by the test scores but also other aspects related to learning. However, conventionally, learning evaluations have been conducted based on subjective impressions, experiences, and intuitions of teachers. Therefore, an objective learning evaluation is desired.

[0005] In the information processing apparatus described in Patent Document 1, it is not possible to objectively conduct a learning evaluation including not only academic ability but also other aspects related to learning.

[0006] This disclosure has been made to solve the above-described problems, and an object thereof is to provide an evaluation program and apparatus capable of objectively conducting a learning evaluation including not only academic ability but also other aspects related to learning. [Means for solving the problem]

[0007] In a certain scenario, the evaluation program causes the computer to perform the following steps: acquire academic ability information of the subject to be evaluated; acquire learning status information of the subject to be evaluated; and evaluate the subject by combining the academic ability information and learning status information.

[0008] Preferably, the evaluation step includes: retrieving correlation information showing the relationship between a combination of academic ability level and learning status level and evaluation information showing an overall evaluation of learning; determining the academic ability level based on academic ability information; determining the learning status level based on learning status information; and using the correlation information to obtain evaluation information corresponding to the determined combination of academic ability level and learning status level.

[0009] Preferably, the calling step includes the step of selecting the correlation information to be called from among multiple correlation information.

[0010] Preferably, the learning scope to be evaluated is subdivided into multiple items. The evaluation program further causes the computer to perform a step of associating academic ability information with learning status information for each item. The evaluation step includes a step of evaluating the subject to be evaluated for each item using the associated academic ability information and learning status information.

[0011] Preferably, multiple items are defined by the curriculum guidelines. The step of acquiring academic ability information includes, for each item, generating academic ability information based on the answers to one or more questions in the academic ability test that correspond to that item. The step of acquiring learning status information includes, for each item, generating learning status information from the status of implementation of one or more learning materials that correspond to that item. The linking step includes linking the academic ability information and learning status information generated for each item.

[0012] Preferably, the learning scope to be evaluated is subdivided into multiple sections. Each of these sections contains at least one item from a set of items. The evaluation program further causes the computer to output the evaluation results for each item contained within that section.

[0013] Preferably, the multiple divisions are obtained by classifying the learning scope using at least one of the perspectives defined by the curriculum guidelines, the content defined by the curriculum guidelines, the domains defined by the curriculum guidelines, and the units defined by the textbooks.

[0014] Preferably, the evaluation program causes the computer to further perform the steps of receiving the designation of one of several categories, and outputting the evaluation results of the items included in the designated category.

[0015] Preferably, the evaluation target includes one or more learners. The evaluation program further causes the computer to perform the steps of receiving the selection of a target learner from among one or more learners, and outputting the evaluation results for the target learner.

[0016] Preferably, the subjects of evaluation include multiple learners. The evaluation program further causes the computer to perform the steps of accepting the selection of a target class from one or more classes, and outputting the evaluation results for one or more learners belonging to the target class from among the multiple learners.

[0017] Preferably, the step of acquiring academic ability information includes the step of acquiring academic ability information for each period. The step of acquiring learning status information includes the step of acquiring learning status information for each period. The evaluation step includes the step of evaluating the subject to be evaluated by combining the academic ability information and the learning status information for each period. The evaluation program further causes the computer to output output information showing the change in the evaluation results of the subject to be evaluated over time.

[0018] Preferably, the step of acquiring academic ability information includes the step of acquiring academic ability information for each period. The step of acquiring learning status information includes the step of acquiring learning status information for each period. The evaluation program further causes the computer to perform the step of generating an academic ability prediction model by machine learning using the academic ability information and learning status information for the first period and the academic ability information for the second period, which is a certain period after the first period has elapsed, as training data.

[0019] Preferably, the step of acquiring academic ability information includes the step of acquiring academic ability information for each period. The step of acquiring learning status information includes the step of acquiring learning status information for each period. The evaluation program further causes the computer to perform the steps of predicting academic ability information after a certain period has elapsed from the target period by inputting the academic ability information and learning status information for the target period into an academic ability prediction model generated by machine learning using the academic ability information and learning status information for the first period and the academic ability information for the second period after a certain period has elapsed from the first period as training data, and outputting the predicted academic ability information.

[0020] Preferably, the evaluation program further causes the computer to output first plotting information for drawing a graph showing academic ability and learning status. The first plotting information shows first evaluation values ​​generated based on academic ability information as coordinate values ​​on the first axis of the graph, and second evaluation values ​​generated based on learning status information as coordinate values ​​on the second axis of the graph.

[0021] Preferably, the evaluation program further causes the computer to perform the following steps: output second drawing information for drawing a first screen showing evaluation results for each category including at least one of a plurality of items; receive an instruction to select an evaluation result for the first screen; and output third drawing information for drawing a second screen showing at least one of academic ability information and learning status information for the category corresponding to the selected evaluation result.

[0022] According to another aspect, the apparatus includes a first acquisition unit that acquires academic ability information of an evaluation target, a second acquisition unit that acquires learning situation information of the evaluation target, and an evaluation unit that evaluates the evaluation target by combining the academic ability information and the learning situation information.

Advantages of the Invention

[0023] According to the present disclosure, it is possible to objectively perform learning evaluation including not only academic ability but also other aspects related to learning.

Brief Description of the Drawings

[0024] [Figure 1] It is a diagram showing a schematic configuration of an evaluation system according to an embodiment of the present disclosure. [Figure 2] It is a diagram showing a learning guideline code. [Figure 3] It is a diagram schematically showing an example of the hardware configuration of a server device according to the present embodiment. [Figure 4] It is a diagram schematically showing an example of the functional configuration of a server device according to the present embodiment. [Figure 5] It is a diagram showing an example of an academic ability table. [Figure 6] It is a diagram showing an example of a learning situation table. [Figure 7] It is a diagram showing an example of a learner master. [Figure 8] It is a diagram showing an example of a teacher master. [Figure 9] It is a diagram showing an example of a class performance table. [Figure 10] It is a diagram showing an example of an aggregation period during which academic ability information and learning situation information are acquired. [Figure 11] It is a diagram showing an example of an evaluation result table. [Figure 12] It is a diagram showing an example of correlation information. [Figure 13] It is a diagram showing another example of correlation information. [Figure 14] It is a diagram showing yet another example of correlation information. [Figure 15]This is a flowchart showing the flow of the material provision process in the material provision device. [Figure 16] This flowchart shows the process for updating the learning status table on the server device. [Figure 17] This flowchart shows the process for updating the lesson record table on the server device. [Figure 18] This flowchart shows the process of updating the academic performance prediction model in the server device. [Figure 19] This is a flowchart showing the main processing flow in a server device. [Figure 20] This figure shows an example of a selection screen provided to the teacher's terminal. [Figure 21] This figure shows an example of a selection screen provided on a learner's device. [Figure 22] This figure shows an example of a selection screen provided to the administrator terminal. [Figure 23] This is a flowchart showing the process for adding to the academic ability table. [Figure 24] This is a flowchart showing the process for updating the learner master data. [Figure 25] This is a diagram showing the flow of the teacher master update process. [Figure 26] This flowchart shows the process for updating the evaluation results table. [Figure 27] This flowchart shows the process for providing evaluations based on different perspectives. [Figure 28] This figure shows an example of the screen provided in step S95 of Figure 27. [Figure 29] This flowchart shows the first half of the process for providing individualized learner assessments. [Figure 30] This flowchart shows the latter half of the process for providing individualized learner assessments. [Figure 31] This figure shows an example of the screen provided in step S107 of Figure 29. [Figure 32]This figure shows an example of the screen provided in step S111 of Figure 30. [Figure 33] This figure shows an example of the screen provided in step S114 of Figure 30. [Figure 34] This figure shows an example of the screen provided in step S116 of Figure 30. [Figure 35] This flowchart shows the process for providing class-based evaluations. [Figure 36] This figure shows an example of the screen provided in step S126 of Figure 35. [Figure 37] This figure shows an example of the screen provided in step S128 of Figure 35. [Figure 38] This flowchart shows part of the process for providing teacher performance data. [Figure 39] This flowchart shows the remaining steps in the process of providing teacher performance data. [Figure 40] This figure shows an example of the screen provided in step S146 of Figure 38. [Figure 41] This figure shows an example of the screen provided in step S150 of Figure 39. [Modes for carrying out the invention]

[0025] Hereinafter, an evaluation system to which an evaluation program according to an embodiment of this disclosure is applied will be described with reference to the drawings. In the following description, the same parts and components are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions of them will not be repeated. The embodiments and modifications described below may be combined selectively as appropriate.

[0026] <Overall Evaluation System> Figure 1 is a diagram showing the schematic configuration of an evaluation system according to an embodiment of the present disclosure. As shown in Figure 1, the evaluation system 1 comprises a server device 100, a teaching material provision device 200, an academic ability test database 300, a school administration database 400, an administrator terminal 500, a teacher terminal 600, and a learner terminal 700.

[0027] The server device 100, the teaching material provision device 200, the academic ability test database 300, the school administration database 400, the administrator terminal 500, the teacher terminal 600, and the student terminal 700 can communicate with each other via network N. Network N may be a public network such as the Internet, a public network, or a public wireless LAN (Local Area Network), or it may be a private network such as a LAN or a VPN (Virtual Private Network).

[0028] The administrator terminal 500 is a terminal used by, for example, an administrator who manages school operations. The teacher terminal 600 is a terminal used by teachers. The student terminal 700 is a terminal used by students. Each of the administrator terminal 500, teacher terminal 600, and student terminal 700 consists of a general-purpose computer device such as a desktop computer, notebook computer, smartphone, or tablet. The administrator terminal 500 is provided, for example, for each school. The evaluation system 1 is equipped with multiple teacher terminals 600 corresponding to the number of teachers and multiple student terminals 700 corresponding to the number of students.

[0029] The material provision device 200 is a device that provides cloud services. Specifically, the material provision device 200 receives instructions from the learner terminal 700 and provides the designated material to the learner terminal 700. The material includes, for example, calculation drills and kanji drills.

[0030] When the learning material provider 200 receives an access request from the learner terminal 700, it provides the learner terminal 700 with a login screen. The learner enters their learner ID and password to identify themselves on the login screen. The learning material provider 200 provides the learner terminal 700 with a learning material selection screen, depending on whether the learner ID and password entered on the login screen match the information registered in advance. The learning material provider 200 then provides the learning material to the learner terminal 700 according to the input on the selection screen.

[0031] For example, if the learning material is a math drill, the learner enters their answer into the learner terminal 700. The learning material provider 200 scores the answer entered into the learner terminal 700 by comparing it with the correct answer stored in advance, and transmits the result to the learner terminal 700.

[0032] Each time a teaching material is provided, the teaching material provider 200 stores teaching material provision data that includes the following information. The teaching material provider 200 also stores the time from the start time to the end time of teaching material provision as learning time. • Learner ID to identify the learner who provided the learning materials. • Date and time when the teaching materials were provided, ·Type of teaching material, • Curriculum code corresponding to the teaching materials, • Study time using the provided materials, etc.

[0033] The National Curriculum Standards are the educational curriculum standards established by the Ministry of Education, Culture, Sports, Science and Technology. Each school implements education in accordance with these standards. The standards are established for each type of school and specify the topics to be learned in each subject for each grade level. Specifically, the standards define the "objectives," "content," and "handling of content" for each subject. The "content" includes one or more "domains." Furthermore, the standards define which of several "perspectives," which are important elements in school education, each topic to be learned falls under. These "perspectives" include "knowledge and skills" and "thinking, judgment, and expression."

[0034] A curriculum code is assigned to every item in the curriculum guidelines for all types of schools. Therefore, the curriculum code can be used to identify the type of school, grade level, subject, content, area of ​​study, how the content is handled, and perspectives.

[0035] Figure 2 shows the curriculum guidelines code. As shown in Figure 2, the curriculum guidelines code represents a 16-digit number. The first digit indicates the date the curriculum guidelines were announced. The second digit indicates the type of school (kindergarten, elementary school, junior high school, high school, and special needs school). The third digit indicates the subject. The fourth digit indicates the field of study for elementary and junior high schools, or the subject for high school. The fifth digit indicates the objectives and content (major classification) of each subject. The sixth digit indicates the grade and stage. The seventh digit indicates the objectives and content (minor classification) set for each field or subject. The eighth to fifteenth digits indicate the details of the curriculum guidelines. The sixteenth digit indicates the status of any partial revisions made at the time of announcement indicated by the first digit.

[0036] The teaching material provision device 200 has pre-stored the corresponding curriculum guideline code for each teaching material.

[0037] The academic achievement test database 300 and the school administration database 400 are established, for example, for each school.

[0038] The Academic Ability Test Database 300 stores performance data for each academic ability test, including the following information. Academic ability tests are administered regularly, such as at the end of a semester, to measure the academic ability of learners. • Test type (subject, content, etc.) • Test date, • Learner ID to identify learners who took the test. • Question number to identify each question, • The correctness of each learner's answer to each question. • The curriculum guideline code corresponding to each question, etc.

[0039] The accumulation of performance data in the academic achievement test database 300 is performed upon input from the administrator terminal 500 or the teacher terminal 600. The curriculum code corresponding to each question may be provided by the creator of the academic achievement test.

[0040] The school administration database 400 stores data on the performance of each lesson conducted at the school. The lesson performance data includes the following: ·School name, Grade level, ·class, Subjects, • Class dates, • Textbook unit, • The curriculum code corresponding to the unit. • Identifying information to identify the teacher in charge (teacher name, teacher ID, etc.) • Number of students attending and absent from classes, • Class attitude indicators, • Progress of lessons, etc.

[0041] The accumulation of lesson performance data in the school administration database 400 is performed in response to input from the administrator terminal 500 or the teacher terminal 600. The class attitude index value is an index value of the attitude of all learners belonging to the class. The "class attitude index value" and "progress of the lesson" are given by subjective evaluation by the teacher who conducted the lesson. A "unit" is a learning unit in the textbook. Teachers only need to input the units they taught in class. The curriculum guideline code corresponding to the unit may be provided by the textbook creator.

[0042] The server device 100 is a computer that collects information about each student's learning from the teaching material provision device 200, the academic ability test database 300, the school administration database 400, and the administrator terminal 500, and uses the collected information to evaluate each student's learning. The server device 100 is, for example, a cloud server. The server device 100 transmits the evaluation results to the teacher terminal 600 or the student terminal 700 in response to a request from the teacher terminal 600 or the student terminal 700.

[0043] <Server hardware configuration> Figure 3 is a schematic diagram showing an example of the hardware configuration of a server device according to this embodiment. As shown in Figure 3, the server device 100 includes a hardware processor, a CPU (Central Processing Unit) 101, RAM (Random Access Memory) 102, ROM (Read Only Memory) 103, an HDD (Hard Disk Drive) 104, a memory interface 105, and a network controller 106.

[0044] The CPU 101 loads programs stored in a storage device such as the HDD 104 into the RAM 102 and executes the loaded programs. The RAM 102 includes an area for storing various information and a working area for when the CPU 101 executes programs. The ROM 103 stores programs and data executed by the CPU 101. The HDD 104 stores the system program 110, which includes the OS, and the evaluation program 112 that is executed under the system program 110.

[0045] The memory interface 105 includes a driver circuit to which a storage medium 107 is detachably attached, and to which data or programs are read from or written to the attached storage medium 107. The storage medium 107 is a medium that stores information such as programs recorded therein through electrical, magnetic, optical, mechanical, or chemical means, so that the CPU 101, other devices, machines, etc., can read the information such as programs recorded therein.

[0046] The network controller 106 includes circuits such as a NIC for communicating with external devices via the network N.

[0047] <Server device functional configuration> Figure 4 is a schematic diagram showing an example of the functional configuration of a server device according to this embodiment. As shown in Figure 4, the server device 100 includes a storage unit 10, an update unit 11, a first acquisition unit 12, a second acquisition unit 13, an evaluation unit 14, a model generation unit 15, a prediction unit 16, and an output unit 17. The storage unit 10 is realized by the RAM 102, ROM 103, and HDD 104 shown in Figure 3. The update unit 11, the first acquisition unit 12, the second acquisition unit 13, the evaluation unit 14, the model generation unit 15, the prediction unit 16, and the output unit 17 are realized by the CPU 101 shown in Figure 3 executing the evaluation program 112.

[0048] (Storage part) The memory unit 10 stores one or more academic ability tables 120, a learning status table 130, a learner master 140, a teacher master 150, a lesson performance table 160, one or more evaluation result tables 170, one or more academic ability prediction models 180, and one or more correlation information 190.

[0049] (Update section) The update unit 11 retrieves performance data from the academic ability test database 300 and creates an academic ability table 120 based on the retrieved performance data. The update unit 11 stores the created academic ability table 120 in the storage unit 10. The update unit 11 creates an academic ability table 120 for each academic ability test. The academic ability table 120 associates the learner ID with the correctness of the answers to each question for each learner.

[0050] Figure 5 shows an example of an academic ability table. The academic ability table 120 is associated with date information 121 indicating the date the academic ability test was administered. As shown in Figure 5, the academic ability table 120 contains records 122 for each learner who took the academic ability test. Each record 122 has a field 123 indicating the learner ID and a field 124 indicating whether the answer to each question was correct or incorrect. Furthermore, for each question, the academic ability table 120 associates a question number 125 that identifies the question with a curriculum guideline code 126 that corresponds to the question.

[0051] As shown in Figure 5, the question number is associated with the curriculum guideline code. Therefore, by checking the curriculum guideline code for each question, the item in the curriculum guideline corresponding to that question can be identified.

[0052] Furthermore, the update unit 11 updates the learning status table 130, learner master 140, teacher master 150, and lesson performance table 160 stored in the storage unit 10 based on data acquired from an external device.

[0053] Specifically, the update unit 11 acquires material provision data from the material provision device 200 and updates the learning status table 130 based on the acquired material provision data. The learning status table 130 associates the learner ID of the learner who received material from the material provision device 200, the date and time of provision, the type of material, the curriculum code corresponding to the material, and the learning time.

[0054] Figure 6 shows an example of a learning status table. As shown in Figure 6, the learning status table 130 includes a record 131 corresponding to one provision of learning materials from the learning material provision device 200 to the learner terminal 700. Each record 131 has a field 132 indicating a learner ID that identifies the learner who received the learning materials, a field 133 indicating the date and time the learning materials were provided, a field 134 indicating the type of learning material, a field 135 indicating the curriculum guideline code corresponding to the provided learning materials, and a field 136 indicating the learning time.

[0055] The update unit 11 updates the learner master 140 and teacher master 150 according to the input to the administrator terminal 500.

[0056] Figure 7 shows an example of a learner master table. As shown in Figure 7, the learner master table 140 is a table that associates learners with their school locations for each academic year, and contains one or more records 141. Each record 141 has a field 142 indicating the learner ID, a field 143 indicating the learner's name, a field 144 indicating the academic year, a field 145 indicating the name of the school attended in that academic year, a field 146 indicating the grade level in that academic year, and a field 147 indicating the class attended in that academic year.

[0057] Figure 8 shows an example of a teacher master table. As shown in Figure 8, the teacher master table 150 is a table that associates teachers with their school locations for each academic year, and contains one or more records 151. Each record 151 has a field 152 indicating the teacher ID, a field 153 indicating the teacher's name, a field 154 indicating the academic year, a field 155 indicating the name of the school where the teacher was enrolled for that academic year, a field 156 indicating the grade level taught for that academic year, a field 157 indicating the class taught for that academic year, and a field 158 indicating the subject taught for that academic year.

[0058] The update unit 11 retrieves lesson performance data from the school administration database 400 and updates the lesson performance table 160 based on the retrieved lesson performance data. The lesson performance table 160 associates the location, date, content, teacher in charge, and lesson status for each lesson.

[0059] Figure 9 shows an example of a lesson performance table. As shown in Figure 9, the lesson performance table 160 contains records 161 corresponding to each lesson. Each record 161 has fields 162a to 162c indicating the location where the lesson was conducted, a field 163 indicating the date of the lesson, fields 164a to 164c indicating the content of the lesson, a field 165 indicating the teacher ID that identifies the teacher who conducted the lesson, and fields 166a to 166d indicating the status of the lesson. Fields 162a to 162c indicate the school name, grade, and class, respectively. Fields 164a to 164c indicate the subject, unit, and curriculum code, respectively. Fields 166a to 166d indicate the number of attendees, the number of absentees, the class attitude index value, and the progress of the lesson, respectively.

[0060] (First acquisition section and second acquisition section) The first acquisition unit 12 acquires academic ability information of the students to be evaluated. The students to be evaluated include one or more students registered in the student master 140. The first acquisition unit 12 generates academic ability information of the students to be evaluated using one or more academic ability tables 120 stored in the memory unit 10. The academic ability information indicates the academic ability of the students to be evaluated. The first acquisition unit 12 may generate academic ability information not only using the results of academic ability tests conducted regularly, but also using the results of quizzes conducted for each unit.

[0061] The second acquisition unit 13 acquires learning status information of the subject to evaluation. The second acquisition unit 13 generates learning status information of the subject to evaluation using the learning status table 130 stored in the storage unit 10. The learning status information indicates the learning status of the subject to evaluation.

[0062] It is preferable that the first acquisition unit 12 and the second acquisition unit 13 acquire academic ability information and learning status information, respectively, for each aggregation period.

[0063] Figure 10 shows an example of an aggregation period for which academic ability information and learning status information are acquired. As shown in Figure 10, each academic year generally includes five aggregation periods: the first term from the start of the academic year until before the summer break, the summer break, the second term from after the summer break until before the winter break, the winter break, and the third term from after the winter break until the closing ceremony (or graduation ceremony).

[0064] Academic achievement tests are generally administered at the end of each of the three semesters; therefore, academic achievement data is collected for each of these three semester periods.

[0065] The provision of learning materials from the material provisioning device 200 to the learner terminal 700 can occur throughout the year. Therefore, learning progress information is acquired for each aggregation period of the first semester, summer vacation, second semester, winter vacation, and third semester.

[0066] The start and end dates for each term—the first term, summer vacation, second term, winter vacation, and third term—will be determined in advance.

[0067] Academic performance information includes, for example, the correct answer rate, the incorrect answer selection rate, and the IRT (Item Response Theory) score. The correct answer rate shows the ratio of correct answers to the total number of questions. The incorrect answer selection rate shows the ratio of test-takers who selected the correct answer option for a question they answered incorrectly, relative to the total number of test-takers. The incorrect answer selection rate is used as an indicator to determine whether or not a question is a common incorrect answer.

[0068] The IRT score is calculated using a known item response theory and indicates the level of mastery, taking into account the difficulty and discriminability of the questions. Specifically, the IRT score is calculated using an item characteristic curve determined based on the difficulty and discriminability of the item response theory. Alternatively, the first acquisition unit 12 may acquire the level of mastery generated using the overall correct answer rate and an index showing the correlation between correct / incorrect answers and the overall correct answer rate instead of the IRT score.

[0069] Learning status information includes, for example, learning density, number of repetitions, continuous learning time, and learning progress. Learning density indicates the number of learning activities using the materials performed per unit of time and represents the level of concentration on learning. For example, learning density indicates the number of pages of calculation drills completed per unit of time. Number of repetitions indicates the number of times the same materials were used for learning and represents the persistence of learning. For example, number of repetitions indicates the number of times mistakes in a calculation drill were corrected or corrected multiple times. Continuous learning time indicates the amount of time spent continuously learning using the materials and represents the learning method. For example, continuous learning time indicates the time from receiving the calculation drill to its completion.

[0070] Learning progress indicates the degree of progress toward a goal set by the teacher or individual learner, or the time taken to achieve that goal, and represents the planning of learning and the attitude toward learning. The goal, for example, indicates the range of calculation drills to be worked on in a predetermined period (e.g., one semester). The second acquisition unit 13 may acquire the goal from the teacher terminal 600. Alternatively, the material provision device 200 may provide the learner terminal 700 with a goal setting screen and set the goal according to the input to the learner terminal 700. Information indicating the set goal is stored in the material provision device 200. The second acquisition unit 13 should acquire information indicating the goal from the material provision device 200 at the timing of generating the learning status information or at a timing prior to that.

[0071] The second data acquisition unit 13 may acquire learning status information using information other than the learning status table 130. For example, if a short test is conducted to check comprehension for each unit and the results of the short tests are stored on the teacher terminal 600, the second data acquisition unit 13 may acquire these results from the teacher terminal 600 and acquire the comprehension level for each unit as part of the learning status information. Alternatively, the second data acquisition unit 13 may perform behavioral analysis using video recordings of what is happening in class and calculate index values ​​representing each learner's level of concentration and engagement in class as part of the learning status information.

[0072] As mentioned above, the scope of learning for learners is subdivided into multiple items in accordance with the curriculum guidelines. It is preferable that learning assessment be conducted for each of these items.

[0073] Therefore, it is preferable that the first acquisition unit 12 generates academic ability information for each item of the curriculum guidelines based on the answers to one or more questions in the academic ability test that correspond to that item. Specifically, the first acquisition unit 12 reads from the storage unit 10 an academic ability table 120 associated with date information 121 indicating the implementation date belonging to the target aggregation period. The first acquisition unit 12 generates learner-specific academic ability information for the item of the curriculum guideline code 126 in the read academic ability table 120 (see Figure 5) based on the correctness of the answers indicated by the field 123 corresponding to the same curriculum guideline code 126.

[0074] Similarly, the second acquisition unit 13 preferably generates learning status information from the implementation status of one or more teaching materials corresponding to each item of the curriculum guidelines. Specifically, the second acquisition unit 13 reads records 131 from the learning status table 130 that include a field 133 (see Figure 6) indicating the date and time of provision belonging to the target aggregation period. Furthermore, the second acquisition unit 13 extracts records 131 from the read records 131 that include a field 135 indicating the same curriculum guideline code, and generates learning status information for each learner regarding the item of the curriculum guideline code based on the information indicated by the extracted records 131.

[0075] The first acquisition unit 12 and the second acquisition unit 13 write the acquired academic ability information and learning status information into the evaluation result table 170. At this time, the first acquisition unit 12 and the second acquisition unit 13 associate the academic ability information with the learning status information for each item.

[0076] Figure 11 shows an example of an evaluation results table. Evaluation results table 170 is created annually.

[0077] As shown in Figure 11, the evaluation results table 170 includes a record 171 showing academic ability information and a record 172 showing learning status information. The evaluation results table 170 also includes a column 173 showing the learner ID, a column 174 showing the type of information, a column 175 showing the aggregation period and evaluation period, and one or more columns 176 provided for each curriculum guideline code.

[0078] Furthermore, the evaluation results table 170 associates the curriculum code with the domain, content, and perspective in each of the 176 columns. The "domain," "content," and "perspective" are identified from the curriculum code, as described above.

[0079] The first acquisition unit 12 acquires academic ability information for each item of the curriculum guidelines for the target learner during the target aggregation period, adds a new record 171 to the evaluation result table 170, and writes the acquired academic ability information to each field of the new record 171.

[0080] Similarly, when the second acquisition unit 13 acquires learning status information for each item of the curriculum guidelines for the target learner during the target aggregation period, it adds a new record 172 to the evaluation result table 170 and writes the acquired learning status information to each field of the new record 172.

[0081] As described above, the evaluation results table 170 includes one or more columns 176, each corresponding to a curriculum guideline code. Therefore, academic ability information and learning status information are associated for each curriculum guideline code.

[0082] (Evaluation Department) The evaluation unit 14 evaluates the subject of evaluation by combining academic ability information and learning status information. The evaluation unit 14 evaluates the subject of evaluation for each evaluation period. The evaluation period includes, for example, the first evaluation period consisting of the first semester, the second evaluation period consisting of the summer vacation and the second semester, the third evaluation period consisting of the winter vacation and the third semester, and the entire period from the first to the third semester.

[0083] The evaluation unit 14 retrieves correlation information 190 from the memory unit 10, which shows the relationship between the combination of academic ability level and learning status level and evaluation information indicating the overall evaluation of learning. The correlation information 190 is created in advance. The memory unit 10 may store multiple correlation information 190. In this case, the evaluation unit 14 only needs to select the correlation information 190 to be retrieved from among the multiple correlation information 190 in response to instructions from the teacher terminal 600.

[0084] Academic ability level and learning status level are represented, for example, by numerical values. A higher numerical value for academic ability level indicates higher academic ability. A higher numerical value for learning status level indicates better learning status. Evaluation information shows a numerical value (hereinafter referred to as "evaluation value") that represents the level of learning evaluation. Correlation information 190 may be in table format or function format.

[0085] Figure 12 shows an example of correlation information. Figure 13 shows another example of correlation information. Figure 14 shows yet another example of correlation information. As shown in Figures 12 to 14, the correlation information 190 shows the evaluation information (evaluation value) for each of multiple areas on a coordinate plane with academic ability level on the horizontal axis and learning status level on the vertical axis. In the examples shown in Figures 12 to 14, a larger evaluation value indicates a higher evaluation.

[0086] Furthermore, when evaluation values ​​are provided to teachers or learners, they may be converted into letters representing the level of evaluation. For example, the evaluation values ​​"4", "3", "2", and "1" shown in Figure 12 are converted to "A", "B", "C", and "D" respectively. The evaluation values ​​"5", "4", "3", "2", and "1" shown in Figure 13 are converted to "A", "B+", "B-", "C", and "D" respectively. The evaluation values ​​"5", "4", "3", "2", and "1" shown in Figure 14 are converted to "A", "B", "C", "D", and "E" respectively.

[0087] The evaluation unit 14 determines the academic ability level based on the academic ability information written in the evaluation result table 170. For example, for each evaluation period, the evaluation unit 14 calculates the academic ability level by substituting the correct answer rate, incorrect answer selection rate, and IRT score indicated by the academic ability information into X, Y, and Z in the following formula (1). If the evaluation period includes multiple aggregation periods, representative values ​​(e.g., average values) of the academic ability information for those multiple aggregation periods should be substituted into X, Y, and Z. Academic ability level = a × X + b × Y + c × Z ··Equation (1) In equation (1), a, b, and c are constants. Equation (1) may be modified according to instructions from the administrator terminal 500 or the teacher terminal 600. Also, any of the constants a, b, and c may be 0. For example, if you want to calculate the academic level using only the IRT score, a and b will be set to 0.

[0088] The evaluation unit 14 determines the learning status level based on the learning status information written in the evaluation result table 170. For example, for each evaluation period, the evaluation unit 14 calculates the learning status level by substituting the learning density, number of repetitions, continuous learning time, and learning progress indicated by the learning status information into P, Q, R, and S in the following equation (2). If the evaluation period includes multiple aggregation periods, representative values ​​(e.g., average values) of the learning status information for those multiple aggregation periods should be substituted into P, Q, R, and S. Learning status level = d × P + e × Q + f × R + g × S ··Equation (2) In equation (2), d, e, f, and g are constants. Equation (2) may be modified according to instructions from the administrator terminal 500 or the teacher terminal 600. Also, any of the constants d, e, f, and g may be 0.

[0089] The evaluation unit 14 uses the correlation information 190 retrieved from the memory unit 10 to obtain evaluation information (evaluation value) corresponding to the determined combination of academic ability level and learning status level. The evaluation unit 14 determines the obtained evaluation information as the evaluation result.

[0090] As described above, academic ability information and learning status information are generated for each aggregation period. The evaluation unit 14 evaluates the subject of evaluation for each evaluation period by combining the academic ability information and learning status information from the aggregation period included in that evaluation period.

[0091] Furthermore, academic ability information and learning status information are generated for each item of the curriculum guidelines. Therefore, the evaluation unit 14 evaluates the target of evaluation by combining the academic ability information and learning status information for each item of the curriculum guidelines.

[0092] The evaluation unit 14 writes the determined academic ability level, learning status level, and evaluation result to the evaluation result table 170. As shown in Figure 11, the evaluation result table 170 further includes a record 177 indicating the academic ability level, a record 178 indicating the learning status level, and a record 179 indicating the evaluation result (evaluation value).

[0093] When the evaluation unit 14 determines the academic ability level of the target learner during the target evaluation period, it adds a new record 177 to the evaluation result table 170 and writes the determined academic ability level to each field of the new record 177.

[0094] When the evaluation unit 14 determines the learning status level of the target learner during the target evaluation period, it adds a new record 178 to the evaluation result table 170 and writes the determined learning status level to each field of the new record 178.

[0095] When the evaluation unit 14 determines the evaluation results for the target learner during the target evaluation period, it adds a new record 179 to the evaluation results table 170 and writes the determined evaluation results to each field of the new record 179.

[0096] (Model generation unit) The model generation unit 15 generates an academic ability prediction model 180 by machine learning using the academic ability information and learning status information for each of the multiple learners in the evaluation result table 170 during a certain period (hereinafter referred to as the "input period") and the academic ability information for a period after a certain time has elapsed from the input period (hereinafter referred to as the "output period") as training data. The academic ability information and learning status information during the input period correspond to explanatory variables, and the academic ability information during the output period corresponds to the target variable.

[0097] The academic ability prediction model 180 is configured to predict academic ability information after a certain period has elapsed since the target period, given academic ability information and learning status information for the target period. The academic ability prediction model 180 can use known learning models such as neural networks. The model generation unit 15 stores the generated academic ability prediction model 180 in the memory unit 10.

[0098] The model generation unit 15 may create multiple academic ability prediction models 180 in which the input period and output period differ from each other.

[0099] For example, the model generation unit 15 generates an academic ability prediction model 180 by using machine learning with the academic ability information and learning status information of multiple learners who are currently in the second year of junior high school or higher, from the period from the fourth to sixth grade of elementary school (input period), and the academic ability information from the first year of junior high school (output period) as training data. When this academic ability prediction model 180 is given academic ability information and learning status information from the fourth to sixth grade of elementary school, it outputs academic ability information for the first year of junior high school.

[0100] Furthermore, the model generation unit 15 generates an academic ability prediction model 180 by using machine learning with the academic ability information and learning status information of multiple learners in the 6th grade and above for the period from the 2nd to 4th grade of elementary school (input period), and the academic ability information for the 5th grade of elementary school (output period) as training data. When this academic ability prediction model 180 is given academic ability information and learning status information from the 2nd to 4th grade of elementary school, it outputs academic ability information for the 5th grade of elementary school.

[0101] (Prediction section) The prediction unit 16 uses the academic ability prediction model 180 to predict each learner's future academic ability. For example, for a learner who has just graduated from elementary school, the prediction unit 16 inputs academic ability information and learning status information from the 4th to 6th grades of elementary school into the academic ability prediction model 180 to predict the learner's academic ability in the 1st year of junior high school.

[0102] (Output section) The output unit 17 provides a screen showing the evaluation results in response to instructions from the teacher terminal 600 or the learner terminal 700. Specifically, the output unit 17 transmits drawing information to the terminal for drawing the screen showing the evaluation results.

[0103] <Processing flow of the educational material provision device> Figure 15 is a flowchart showing the flow of the material provision process in the material provision device. First, the material provision device 200 determines whether or not login authentication was successful (step S1). Specifically, the material provision device 200 determines that login authentication was successful if the learner ID and password received from the learner terminal 700 match the data that has been registered in advance. If login authentication fails (NO in step S1), the material provision process returns to step S1.

[0104] If login authentication is successful (YES in step S1), the material provider 200 provides the learner terminal 700 with a material selection screen and receives instructions to select materials from the learner terminal 700 (step S2). Next, the material provider 200 delivers the selected materials to the learner terminal 700 (step S3). The material provider 200 starts measuring the learning time (step S4).

[0105] Next, the material provision device 200 determines whether or not it has received a learning completion instruction (step S5). If it has not received a completion instruction (NO in step S5), the material provision process returns to step S5. As a result, learning using the materials continues.

[0106] If a termination instruction is received (YES in step S5), the material provision device 200 terminates the measurement of learning time (step S6). The material provision device 200 creates and records material provision data (step S7). After step S6, the material provision process ends.

[0107] <Process flow that is automatically started on the server device> The server device 100 periodically updates the learning status table 130, the lesson performance table 160, and the academic ability prediction model 180. The following describes the update process for the learning status table 130, the lesson performance table 160, and the academic ability prediction model 180.

[0108] (Updating the learning status table) Figure 16 is a flowchart showing the flow of the learning status table update process in the server device. The evaluation program 112 causes the CPU 101 to execute steps S11 and S12 shown in Figure 16, for example, once a day at a fixed time.

[0109] The CPU 101 of the server device 100 retrieves unacquired material provision data from the material provision device 200 (step S11). Based on the acquired material provision data, the CPU 101 adds a new record 131 to the learning status table 130 shown in Figure 6 (step S12). After step S12, the update process of the learning status table 130 is completed.

[0110] (Updating the lesson record table) Figure 17 is a flowchart showing the flow of the update process for the lesson performance table in the server device. The evaluation program 112 causes the CPU 101 to execute steps S21 and S22 shown in Figure 17, for example, once a week at a fixed time.

[0111] The CPU 101 of the server device 100 retrieves unacquired lesson performance data from the school administration database 400 (step S21). Based on the acquired lesson performance data, the CPU 101 adds a new record 161 to the lesson performance table 160 shown in Figure 9 (step S22). After step S22, the update process for the lesson performance table 160 is completed.

[0112] (Updating the academic ability prediction model) Figure 18 is a flowchart showing the flow of the update process for the academic ability prediction model in the server device. The evaluation program 112 causes the CPU 101 to execute steps S31 and S32 shown in Figure 18 when the collection of data necessary for updating the academic ability prediction model 180 is completed, for example, at the end of the academic year.

[0113] The CPU 101 of the server device 100 acquires training data from the evaluation result table 170 (step S31). Specifically, the CPU 101 acquires, for each of the multiple learners, academic ability information and learning status information for the first period, and academic ability information for the second period, which is a certain period after the first period, as training data.

[0114] Next, the CPU 101 generates an academic ability prediction model 180 by performing machine learning using the training data, and updates the academic ability prediction model 180 stored in the HDD 104 with the newly generated academic ability prediction model 180 (step S32). After step S32, the update process of the academic ability prediction model 180 is completed.

[0115] <Main processing flow in server device> Figure 19 is a flowchart showing the main processing flow in the server device. The evaluation program 112 causes the CPU 101 of the server device 100 to execute steps S41 to S45 shown in Figure 19.

[0116] First, the CPU 101 determines whether or not it has received an access request from an external terminal via network N (step S41). If it has not received an access request (NO in step S41), the main process returns to step S41.

[0117] If an access request is received (YES in step S41), the CPU 101 provides a login screen to the external terminal and obtains the login ID and password from the external terminal (step S42). Next, the CPU 101 determines whether or not login authentication was successful based on the obtained login ID and password (step S43). If login authentication fails (NO in step S43), the main process returns to step S41.

[0118] If login authentication is successful (YES in step S43), the CPU 101 provides the external terminal with a processing menu selection screen (step S44). The CPU 101 changes the selection screen according to the logged-in user.

[0119] Figure 20 shows an example of a selection screen provided to the teacher terminal. The CPU 101 provides the selection screen 20 shown in Figure 20 when the logged-in user is a teacher. The selection screen 20 includes a menu 20a that instructs updating the evaluation results table, a menu 20b that instructs providing evaluation by viewpoint, a menu 20c that instructs providing evaluation by learner, a menu 20d that requests providing evaluation by class, and a menu 20e that instructs providing teacher performance data.

[0120] Figure 21 shows an example of a selection screen provided to a learner's terminal. When the logged-in user is a learner, the CPU 101 provides the selection screen 21 shown in Figure 21. The selection screen 21 includes a menu 21a that instructs the provision of learner-specific evaluations.

[0121] Figure 22 shows an example of a selection screen provided to the administrator terminal. The CPU 101 provides the selection screen 22 shown in Figure 22 when the logged-in user is an administrator. The selection screen 22 includes a menu 22a that instructs the addition of an academic ability table, a menu 22b that instructs the updating of the learner master, and a menu 22c that instructs the updating of the teacher master.

[0122] As shown in Figure 19, in the next step S45 following step S44, the CPU 101 executes the processing of the selected menu. After step S45, the main processing ends. The subroutine for step S45 is described below.

[0123] (Adding to the academic ability table) Figure 23 is a flowchart showing the flow of the process for adding to the academic ability table. The evaluation program 112 causes the CPU 101 to execute steps S51 and S52 shown in Figure 23, in response to the selection of menu 22a on the selection screen 22 shown in Figure 22. After the academic ability test is conducted and the performance data corresponding to that test is registered in the academic ability test database 300, the administrator should operate the administrator terminal 500 and select menu 22a.

[0124] The CPU 101 retrieves performance data for newly administered academic achievement tests from the academic achievement test database 300 (step S51). Based on the retrieved performance data, the CPU 101 creates an academic achievement table 120, for example, as shown in Figure 5, and records the created academic achievement table 120 on the hard disk 104 (step S52). At this time, the CPU 101 creates date information 121 indicating the date the test was administered, associates the academic achievement table 120 with the date information 121, and records it on the HDD 104. After step S52, the process of adding the academic achievement table 120 is completed.

[0125] (Learner master data update process) Figure 24 is a flowchart showing the flow of the learner master update process. The evaluation program 112 causes the CPU 101 to execute steps S61 and S62 shown in Figure 24, depending on whether menu 22b on the selection screen 22 shown in Figure 22 is selected. The administrator simply needs to operate the administrator terminal 500 and select menu 22b when a change occurs in the learner's grade level, enrollment, or transfer to another school.

[0126] The CPU 101 obtains student transfer data from the administrator terminal 500, which includes the name of the school the student is transferred to, their grade, and their class (step S61). Specifically, the CPU 101 provides the administrator terminal 500 with a screen for inputting the name of the school the student is transferred to, their grade, and their class, and obtains the student transfer data in response to the input on that screen.

[0127] Next, the CPU 101 updates the learner master 140, for example, as shown in Figure 7, based on the acquired learner transfer data (step S62). Specifically, if a learner is promoted to the next grade or enrolls in a new school, the CPU 101 adds a new record 141 to the learner master 140 based on the learner transfer data. After step S62, the update process for the learner master 140 is completed.

[0128] (Teacher master update process) Figure 25 is a flowchart showing the flow of the teacher master update process. The evaluation program 112 causes the CPU 101 to execute steps S71 and S72 shown in Figure 25, depending on whether menu 22c is selected on the selection screen 22 shown in Figure 22. The administrator can, for example, operate the administrator terminal 500 and select menu 22c at the start of the new academic year.

[0129] CPU 101 obtains teacher transfer data from administrator terminal 500, which includes the name of the school to which the teacher will be transferred, the grade level, the class level, and the subject level to which the teacher will be assigned (step S71). Specifically, CPU 101 provides administrator terminal 500 with a screen for entering the name of the school to which the teacher will be transferred, the grade level, the class level, and the subject level to which the teacher will be assigned, and obtains the teacher transfer data in response to the input on that screen.

[0130] The CPU 101 updates the teacher master 150, for example, as shown in Figure 8, based on the acquired teacher transfer data (step S72). Specifically, the CPU 101 adds a new record 151 to the teacher master 150 based on the teacher transfer data. After step S72, the teacher master 150 update process is completed.

[0131] (Updating the evaluation results table) Figure 26 is a flowchart showing the flow of the update process for the evaluation results table. The evaluation program 112 causes the CPU 101 to execute steps S81 to S89 shown in Figure 26, depending on whether menu 20a on the selection screen 20 shown in Figure 20 is selected. The teacher can, for example, operate the teacher terminal 600 and select menu 20a when the academic ability table 120 corresponding to the academic ability test is added.

[0132] First, the CPU 101 determines the aggregation period and the evaluation period (step S81). Specifically, the CPU 101 obtains period information indicating the start and end dates for each of the following periods: the first semester, summer vacation, the second semester, winter vacation, and the third semester. This period information is stored in advance on the HDD 104 of the server device 100. Alternatively, the CPU 101 may obtain the period information from the administrator terminal 500. The CPU 101 determines the aggregation period and the evaluation period from the current date and time and the period information. For example, if the current date and time is immediately after the first semester, the CPU 101 determines the first semester as the aggregation period and the first evaluation period. If the current date and time is immediately after the second semester, the CPU 101 determines the first semester, summer vacation, and the second semester as aggregation periods, determines the first semester as the first evaluation period, and determines the period of summer vacation and the second semester combined as the second evaluation period. However, aggregation periods for which academic ability information and learning status information have already been obtained are excluded. Similarly, evaluation periods for which evaluation results have already been obtained are excluded.

[0133] Next, the CPU 101 selects one of the learners registered in the learner master 140 as the target learner (step S82). The CPU 101 then executes steps S83 to S88 for the selected target learner.

[0134] CPU 101 obtains academic ability information for each aggregation period (step S83). Specifically, for each aggregation period, CPU 101 identifies an academic ability table 120 associated with date information 121 indicating the implementation date belonging to that aggregation period, and extracts a record 122 from the identified academic ability table 120 that describes the learner ID of the target learner. CPU 101 classifies the correctness of the answers indicated by field 124 of the extracted record 122 by curriculum guideline code (i.e., by item of the curriculum guideline). Based on the correctness of the answers for each item of the curriculum guideline, CPU 101 generates academic ability information for each item (e.g., correct answer rate, incorrect answer selection rate, IRT score, etc.). For each aggregation period, CPU 101 creates a record 171 describing the academic ability information and adds the created record 171 to the evaluation result table 170.

[0135] Next, the CPU 101 obtains learning status information for each aggregation period (step S84). Specifically, for each aggregation period, the CPU 101 extracts a record 131 from the learning status table 130 that includes a field 133 indicating the date and time of provision belonging to that aggregation period. Based on the extracted record 131, the CPU 101 generates learning status information for each curriculum guideline code (e.g., learning density, number of repetitions, continuous learning time, learning progress, etc.). For each aggregation period, the CPU 101 creates a record 172 describing the learning status information and adds the created record 172 to the evaluation result table 170.

[0136] CPU101 determines the academic level of the target learner based on academic ability information for each evaluation period (step S85). If the evaluation period includes multiple aggregation periods, CPU101 calculates a representative value (e.g., the mean) of the academic ability information for those multiple aggregation periods and determines the academic level for that evaluation period based on that representative value. In step S85, CPU101 determines the academic level for each curriculum guideline code (i.e., for each item of the curriculum guideline).

[0137] The CPU 101 determines the learning status level of the target learner based on the learning status information for each evaluation period (step S86). If the evaluation period includes multiple aggregation periods, the CPU 101 calculates a representative value (e.g., the mean) of the learning status information for those multiple aggregation periods and determines the learning status level for that evaluation period based on that representative value. In step S86, the CPU 101 determines the learning status level for each curriculum guideline code (i.e., for each item of the curriculum guideline).

[0138] Next, CPU 101 associates (merges) the academic ability level and the learning status level for each curriculum guideline code (i.e., for each item in the curriculum guideline) (step S87). Specifically, CPU 101 creates a record 177 indicating the determined academic ability level and adds the created record 177 to the evaluation result table 170. Similarly, CPU 101 creates a record 178 indicating the determined learning status level and adds the created record 178 to the evaluation result table 170. As described above, the evaluation result table 170 contains one or more columns 176, each corresponding to a curriculum guideline code. Therefore, by adding records 177 and 178 to the evaluation result table 170, the academic ability level and the learning status level are associated for each curriculum guideline code.

[0139] Next, the CPU 101 evaluates the target learner by combining academic ability information and learning status information for each evaluation period, and records the evaluation results in the evaluation result table 170 (step S88).

[0140] Specifically, CPU 101 retrieves correlation information 190. CPU 101 may also select the correlation information 190 to retrieve from among multiple correlation information 190 in response to instructions from administrator terminal 500.

[0141] CPU 101 obtains evaluation information by inputting the academic ability level and learning status level for each evaluation period into correlation information 190 for each curriculum guideline code, and determines the obtained evaluation information as the evaluation result. CPU 101 creates a record 177 that shows the evaluation result determined for each curriculum guideline code, and adds the created record 177 to the evaluation result table 170.

[0142] Next, the CPU 101 determines whether or not there are any unselected learners (step S89). If there are unselected learners (YES in step S89), the process of updating the evaluation result table 170 returns to step S82. If there are no unselected learners (NO in step S89), the process of updating the evaluation result table 170 ends.

[0143] (Provision of evaluation by perspective) Figure 27 is a flowchart showing the flow of the evaluation process for each viewpoint. The evaluation program 112 causes the CPU 101 to execute steps S91 to S95 shown in Figure 27, in response to the selection of menu 20b on the selection screen 20 shown in Figure 20.

[0144] The CPU 101 determines the target class (step S91). Specifically, the CPU 101 provides the teacher terminal 600 with a screen for specifying the school name, grade, and class, and determines the target class based on the input on the screen. Alternatively, the CPU 101 may read the current year's record 151 corresponding to the logged-in teacher's teacher ID from the teacher master 150, and determine the class indicated by the read record 151 as the target class.

[0145] Next, the CPU 101 retrieves the learner IDs of the learners belonging to the target class from the learner master 140 (step S92).

[0146] Next, the CPU 101 obtains the evaluation value corresponding to the extracted learner ID from the evaluation result table 170 (step S93).

[0147] Next, the CPU 101 calculates a representative value (e.g., the mean) of the acquired evaluation values ​​for each viewpoint and for each learner (step S94).

[0148] Next, the CPU 101 provides the teacher terminal 600 with a screen showing the evaluation results for the target class based on the calculated representative values ​​(step S95).

[0149] Figure 28 shows an example of a screen provided in step S95 of Figure 27. As shown in Figure 28, screen 23 shows the evaluation results for each subject from each perspective for each student belonging to the target class, XXX Elementary School, 5th grade, class 2. In screen 23 shown in Figure 28, the CPU 101 converts the representative value of the evaluation value into an alphabet and provides that alphabet as the evaluation result.

[0150] (Provision of learner-specific assessments) Figure 29 is a flowchart showing the first half of the process for providing individualized assessments for learners. Figure 30 is a flowchart showing the second half of the process for providing individualized assessments for learners. The assessment program 112 causes the CPU 101 to execute steps S101 to S110 shown in Figure 29 and steps S111 to S117 shown in Figure 30, depending on whether menu 20c on the selection screen 20 shown in Figure 20 or menu 21a on the selection screen 21 shown in Figure 21 is selected.

[0151] First, the CPU 101 determines whether the logged-in user is a teacher or not (step S101). The CPU 101 only needs to determine that the logged-in user is a teacher if the login ID is a teacher ID.

[0152] If the answer is YSE in step S101, the CPU 101 determines the target learners according to the specification (step S102). Specifically, the CPU 101 reads the current year's record 151 of the teacher ID of the logged-in teacher from the teacher master 150. The CPU 101 extracts the names of the students belonging to the school name, grade level, and class indicated by the read record 151 from the learner master 140, and provides the teacher terminal 600 with a screen containing a list of the extracted names. The CPU 101 determines the learners whose names are selected on the screen as the target learners.

[0153] If the answer in step S101 is NO, the CPU 101 determines that the logged-in learner is the target learner (step S103).

[0154] After steps S102 and S103, the CPU 101 determines the target subject (step S104). Specifically, the CPU 101 provides the terminal with a screen for selecting one subject from several subjects, and determines the selected subject as the target subject.

[0155] Next, CPU101 retrieves the evaluation values ​​for the target student for the current academic year from the evaluation results table 170 (step S105).

[0156] Next, the CPU 101 calculates a representative value (e.g., the mean) of the acquired evaluation values ​​for each content (step S106). As mentioned above, "content" is defined by the curriculum guidelines and is a category that includes at least one item.

[0157] Next, the CPU 101 provides a screen showing the evaluation results for each content to the terminal (teacher terminal 600 or learner terminal 700) (step S107). Specifically, the CPU 101 generates drawing information (corresponding to the second drawing information) for drawing the screen showing the evaluation results for each content (corresponding to the first screen), and outputs the generated drawing information to the terminal.

[0158] Figure 31 shows an example of a screen provided in step S107 of Figure 29. Figure 31 shows screen 24, which displays the evaluation results for the subject "Mathematics" for the target learner "Taro Yamada," who is in the fourth grade of elementary school this year.

[0159] As shown in Figure 31, screen 24 includes a button 25 for displaying the progress over time, a button 26 for closing the screen, and an area 27 for displaying evaluation results for each content within the learning scope for fourth grade elementary school.

[0160] Area 27 displays buttons 28 for each content. The buttons 28 are, for example, oval-shaped. The buttons 28 display text indicating the content and the evaluation result. As shown in Figure 31, the CPU 101 converts representative values ​​of the evaluation values ​​into letters and generates drawing information that indicates the letters as evaluation results. The buttons 28 are for receiving instructions to select an evaluation result for each content.

[0161] The display format of button 28 varies depending on the evaluation result. Specifically, a double thick circle is displayed around the outer edge of button 28 corresponding to evaluation result "A". A single thick circle is displayed around the outer edge of button 28 corresponding to evaluation result "B". A thick arc with a central angle greater than 180° is displayed around the outer edge of button 28 corresponding to evaluation result "C". A thick arc with a central angle less than 180° is displayed around the outer edge of button 28 corresponding to evaluation result "D". This makes it easier to see the level of the evaluation result.

[0162] Next, the CPU 101 determines whether or not the button 25 for displaying the aging process has been clicked (step S108). Specifically, the CPU 101 determines that the button 25 has been clicked when it receives a signal from the terminal indicating that the button 25 has been clicked.

[0163] If button 25 has not been clicked (NO in step S108), the CPU 101 determines whether or not any of the buttons 28 have been clicked (step S109). That is, the CPU 101 determines whether or not it has received an instruction to select an evaluation result. Specifically, the CPU 101 determines that button 28 has been clicked by receiving a signal from the terminal indicating that any of the buttons 28 have been clicked.

[0164] If the answer in step S109 is YES, the process of providing individualized learner assessments proceeds to step S116, as shown in Figure 30. Details of step S116 will be described later.

[0165] If the answer in step S109 is NO, the CPU 101 determines whether or not the button 26 for closing the screen has been clicked (step S110). Specifically, the CPU 101 determines that the button 26 has been clicked by receiving a signal from the terminal indicating that the button 26 has been clicked.

[0166] If button 26 is not clicked (NO in step S110), the process of providing individual learner assessments returns to step S108. If button 26 is clicked (YES in step S110), the process of providing individual learner assessments ends.

[0167] If button 25 is clicked (YES in step S108), the CPU 101 provides the terminal with a screen showing the year-on-year trend (step S111 in Figure 30). Specifically, the CPU 101 retrieves the evaluation values ​​for the target subject for the target learner up to the previous year from each evaluation result table 170 up to the previous year, and calculates a representative value (e.g., the average) of the retrieved evaluation values ​​for each year and for each content. These processes are the same as in steps S105 and S106 above. Then, the CPU 101 provides the terminal with a screen showing the evaluation results for each content for each year.

[0168] Figure 32 shows an example of a screen provided in step S111 of Figure 30. Figure 32 shows screen 29, which displays the evaluation results for each content of the subject "Mathematics" for each year from the first to the fourth grade of elementary school for the target learner "Taro Yamada".

[0169] As shown in Figure 32, screen 29 includes a button 30 for displaying predictions of future academic ability, an area 31 for displaying the year-on-year trend of evaluation results, and a button 33 for returning to the previous screen.

[0170] Domain 31 includes Domain 27, which displays the evaluation results for each content item for this academic year, as well as Domains 32a to 32c, which display the evaluation results for each content item for each academic year up to last year. Domains 32a to 32c display the evaluation results for each content item within the learning scope for elementary school grades 1 to 3, respectively. Domains 27 and 32a to 32c display buttons 28 for each content item. The display format of buttons 28 is the same as in Figure 31. Therefore, by checking the display format of buttons 28, it is easy to grasp the year-on-year trends in evaluation results for each content item.

[0171] Next, the CPU 101 determines whether or not button 30, which displays the prediction result of future academic ability, has been clicked (step S112). Specifically, the CPU 101 determines that button 30 has been clicked when it receives a signal from the terminal indicating that button 30 has been clicked.

[0172] If the answer in step S112 is YES, the CPU 101 predicts the academic ability information of the target learner for the next academic year (step S113). Specifically, the CPU 101 obtains an academic ability prediction model 180 from the HDD 104 to predict the academic ability information of the target learner for the next academic year (for example, fifth grade). The CPU 101 reads the academic ability information and learning status information of the target learner up to the current academic year from the evaluation result table 170 and inputs this information into the academic ability prediction model 180. The CPU 101 obtains the information output from the academic ability prediction model 180 as the academic ability information of the target learner for the next academic year.

[0173] Next, the CPU 101 provides the terminal with a screen containing the predicted academic performance information (step S114).

[0174] Figure 33 shows an example of a screen provided in step S114 of Figure 30. Figure 33 shows a screen 34 containing the academic ability information of the target learner "Yamada Taro" for the next academic year (5th grade of elementary school).

[0175] As shown in Figure 33, screen 34 includes, in addition to the button 33, an area 35 containing academic ability information for the next academic year (5th grade).

[0176] Domain 35 differs from Domain 31 shown in Figure 32 in that it includes Domain 36. Domain 36 includes Figure 37, which shows academic ability information for each content in the next academic year. Figure 37 contains text describing the content. Furthermore, the display format of Figure 37 differs depending on the predicted academic ability information. For example, Figure 37 corresponding to content for which the predicted correct answer rate is above a threshold is hatched. Alternatively, the higher the predicted correct answer rate, the higher the density of Figure 37 may be. This allows us to understand the academic ability of the target learner in the next academic year by checking the display format of Figure 37.

[0177] After step S114, or if the answer in step S112 is NO, the CPU 101 determines whether or not one of the buttons 28 has been clicked (step S115). That is, the CPU 101 determines whether or not it has received an instruction to select an evaluation result for each item. Specifically, the CPU 101 determines that one of the buttons 28 has been clicked by receiving a signal from the terminal indicating that one of the buttons 28 has been clicked.

[0178] If the answer is YES in step S115, or YES in step S109 in Figure 29, the CPU 101 provides the terminal with a screen showing detailed information (academic ability information and learning status information) about the content of the clicked button 28 (step S116). Specifically, the CPU 101 generates drawing information (corresponding to the third drawing information) for drawing a screen (corresponding to the second screen) showing academic ability information and learning status information corresponding to the selected evaluation result, and outputs the generated drawing information to the terminal.

[0179] Figure 34 shows an example of a screen provided in step S116 of Figure 30. Figure 34 shows screen 38, which is provided when button 28a of the screens shown in Figures 31 to 33 is clicked. As shown in Figure 34, screen 38 includes, in addition to button 33, an area 39 that displays details of learning information and learning status information.

[0180] The button 28a shown in Figures 31 to 33 corresponds to the topic of "fractions" in the curriculum for fourth grade elementary school students. Therefore, the CPU 101 reads the academic ability information and learning status information corresponding to the topic of "fractions" for the target learner from this year's evaluation results table 170. The CPU 101 generates a screen 38 that includes an area 39 that shows the details of the read academic ability information and learning status information.

[0181] After step S116, or if the answer in step S115 is NO, the CPU 101 determines whether or not the button 33 for returning to the previous screen has been clicked (step S117). Specifically, the CPU 101 determines that the button 33 has been clicked by receiving a signal from the terminal indicating that the button 33 has been clicked.

[0182] If button 33 is clicked (YES in step S117), the process of providing individual learner evaluations returns to the previous screen's provision step. For example, if button 33 on screen 29 shown in Figure 32 is clicked, the process of providing individual learner evaluations returns to step S107.

[0183] If button 33 is not clicked (NO in step S117), the process of providing learner-specific evaluations returns to the next step of the current screen provision step. For example, if the current screen is screen 34 shown in Figure 33, the process of providing learner-specific evaluations returns to step S115.

[0184] (Processing of providing class-based evaluations) Figure 35 is a flowchart showing the flow of the class-based evaluation process. The evaluation program 112 causes the CPU 101 to execute steps S121 to S132 shown in Figure 35, in response to the selection of menu 20d on the selection screen 20 shown in Figure 20.

[0185] First, the CPU 101 determines the target grade level, target subject, and target content (step S121). Specifically, the CPU 101 provides the teacher terminal 600 with a screen for specifying the grade level, subject, and content, and determines the selected grade level, subject, and content as the target grade level, target subject, and target content, respectively.

[0186] Next, CPU101 retrieves academic ability information, learning status information, and evaluation values ​​for the target grade, subject, and content from evaluation results table 170 (step S122). Specifically, CPU101 retrieves academic ability information and learning status information for the aggregation period that has been compiled for this academic year, and evaluation values ​​for the evaluation period that has been evaluated for this academic year.

[0187] Next, the CPU 101 calculates representative values ​​(e.g., average values) for each of the acquired academic ability information, learning status information, and evaluation values ​​for each class (step S123).

[0188] Next, CPU 101 extracts records 161 from the lesson record table 160 that correspond to the target grade, target subject, and target content (step S124). Note that the lesson record table 160 describes units determined by each textbook instead of content defined by the national curriculum guidelines. The relationship between the units determined by the textbook and the content defined by the national curriculum guidelines is provided by the textbook creator. CPU 101 can then use this relationship to extract records 161 that correspond to the target grade, target subject, and target content.

[0189] Next, the CPU 101 determines representative values ​​for attitude indicators and progress in the course for each class based on the extracted records 161 (step S125).

[0190] Next, the CPU 101 provides the teacher terminal 600 with a screen showing the evaluation results for each class (step S126).

[0191] Figure 36 shows an example of a screen provided in step S126 of Figure 35. Figure 36 shows a screen 40 that displays the evaluation results for each class for the target grade "Elementary School 4th Grade", target subject "Mathematics", and target content "Decimals".

[0192] As shown in Figure 36, screen 40 includes an area 41 that displays attitude indicator values, lesson progress, academic ability information, learning status information, and evaluation results for each class, as well as a button 42 for closing the screen.

[0193] In the screen 40 shown in Figure 36, the CPU 101 converts representative values ​​of evaluation values ​​into letters and provides these letters as evaluation results. Furthermore, the CPU 101 also converts representative values ​​of class attitude indicators, lesson progress, academic ability information, and learning status information into letters. By checking screen 40, teachers can compare learning indicators between their own class and other classes.

[0194] Screen 40 is configured to accept a selection instruction for any of the class names 43 in area 41.

[0195] Next, the CPU 101 determines whether or not any of the class names 43 have been clicked (step S127). Specifically, the CPU 101 determines that a class name 43 has been clicked by receiving a signal that identifies the clicked class name 43.

[0196] If the answer in step S127 is YES, the CPU 101 provides the teacher terminal 600 with a screen showing details of the academic ability level and learning status level of the class corresponding to the clicked class name 43 (step S128). Specifically, the CPU 101 obtains the academic ability level and learning status level of learners belonging to the class corresponding to the clicked class name 43 from the evaluation result table 170, and provides a screen showing the distribution of the obtained academic ability level and learning status level. That is, the CPU 101 outputs drawing information (corresponding to the first drawing information) for drawing the screen to the teacher terminal 600.

[0197] Figure 37 is a diagram showing an example of a screen provided in step S128 of Figure 35. As shown in Figure 37, screen 44 includes a button 45 for returning to the previous screen and an area 46 containing a graph showing the distribution of academic ability levels and learning status levels of learners belonging to the class corresponding to the clicked class name 43.

[0198] Area 46 displays three graphs 46a to 46c. Graph 46a is a histogram of academic ability levels. Graph 46b is a histogram of learning status levels. Graph 46c is a graph where academic ability levels are on the horizontal axis and learning status levels are on the vertical axis, plotting points for academic ability levels generated based on academic ability information and learning status levels generated based on learning status information for each learner belonging to the class. In other words, the plotting information for drawing screen 44 shows academic ability levels (corresponding to the first evaluation value) generated based on academic ability information as the coordinate values ​​of the first axis of graph 46c, and learning status levels (corresponding to the second evaluation value) generated based on learning status information as the coordinate values ​​of the second axis of graph 46c. Screen 44 is configured to accept selection instructions for each plot point in graph 46c.

[0199] Next, the CPU 101 determines whether or not any plotted points have been clicked (step S129). Specifically, the CPU 101 determines that a plotted point has been clicked by receiving a signal that identifies the clicked plotted point.

[0200] If the answer in step S129 is YES, the CPU 101 provides the terminal with a screen showing the learner's academic ability information and learning status information corresponding to the clicked plot point (step S130). For example, in step S130, the CPU 101 provides a screen in the same format as screen 38 shown in Figure 34.

[0201] After step S130, or if the answer in step S129 is NO, the CPU 101 determines whether or not buttons 45 and 33 for returning to the previous screen were clicked (step S131). Specifically, the CPU 101 determines that buttons 45 and 33 have been clicked by receiving a signal from the teacher terminal 600 indicating that buttons 45 and 33 have been clicked.

[0202] If buttons 45 and 33 are clicked (YES in step S131), the process of providing class-specific evaluations returns to the previous screen's provision step. For example, if button 45 on screen 44 shown in Figure 37 is clicked, the process of providing class-specific evaluations returns to step S126.

[0203] If buttons 45 and 33 are not clicked (NO in step S131), the process of providing class-specific evaluations returns to the next step of the current screen provision step. For example, if the current screen is screen 44 shown in Figure 37, the process of providing class-specific evaluations returns to step S129.

[0204] If the answer in step S127 is NO, the CPU 101 determines whether or not the button 42 for closing the screen has been clicked (step S132). Specifically, the CPU 101 determines that the button 42 has been clicked by receiving a signal from the teacher terminal 600 indicating that the button 42 has been clicked.

[0205] If button 42 is not clicked (NO in step S132), the process of providing class-specific evaluations returns to step S127. If button 42 is clicked (YES in step S132), the process of providing class-specific evaluations ends.

[0206] (Processing of providing teacher performance data) Figure 38 is a flowchart showing part of the process for providing teacher performance data. Figure 39 is a flowchart showing the remaining part of the process for providing teacher performance data. The evaluation program 112 causes the CPU 101 to execute steps S141 to S149 shown in Figure 38 and steps S150 to S155 shown in Figure 39, depending on whether menu 20e is selected on the selection screen 20 shown in Figure 20.

[0207] First, the CPU 101 uses the teacher master 150 to identify the teacher's assigned class and subject (step S141). Next, the CPU 101 uses the learner master 140 to identify the learners belonging to the assigned class (step S142).

[0208] Next, CPU 101 retrieves the academic ability information, learning status information, and evaluation values ​​of the identified learners for each subject from the evaluation results table 170 (step S143). CPU 101 retrieves the academic ability information and learning status information for the aggregation period that has been compiled for this academic year, and the evaluation values ​​for the evaluation period that has been evaluated for this academic year.

[0209] Next, the CPU 101 calculates representative values ​​(e.g., average values) for each of the acquired academic ability information, learning status information, and evaluation values ​​(step S144).

[0210] Next, CPU101 calculates a representative value of the evaluation scores of all learners for each subject matter (Step S145).

[0211] Next, the CPU 101 provides the teacher terminal 600 with a screen showing the teacher's performance (step S146).

[0212] Figure 40 shows an example of a screen provided in step S146 of Figure 38. Figure 40 shows screen 47, which shows the results for the subject "Arithmetic" taught by teacher "Jiro Tanikawa".

[0213] As shown in Figure 40, screen 47 includes an area 48 that displays evaluation results for each subject area, a button 49 for closing the screen, and a button 52 for displaying the trend over time.

[0214] Each area 48 includes an area 50 that shows the average of the evaluation results of all learners, and a button 51 that shows the average of the evaluation results of the learners belonging to the assigned class. In the screen 47 shown in Figure 40, the CPU 101 converts representative values ​​of the evaluation values ​​into letters and provides these letters as evaluation results.

[0215] Next, the CPU 101 determines whether or not the button 52 for displaying the aging process has been clicked (step S147). Specifically, the CPU 101 determines that the button 52 has been clicked when it receives a signal from the teacher terminal 600 indicating that the button 52 has been clicked.

[0216] If button 52 has not been clicked (NO in step S147), the CPU 101 determines whether or not any of the buttons 51 have been clicked (step S148). Specifically, the CPU 101 determines that button 51 has been clicked by receiving a signal from the teacher terminal 600 indicating that any of the buttons 51 have been clicked.

[0217] If the answer in step S148 is YES, the process of providing teacher performance data proceeds to step S152, as shown in Figure 39. Details of step S152 will be described later.

[0218] If the answer in step S148 is NO, the CPU 101 determines whether or not the button 49 for closing the screen has been clicked (step S149). Specifically, the CPU 101 determines that the button 49 has been clicked by receiving a signal from the teacher terminal 600 indicating that the button 49 has been clicked.

[0219] If button 49 is not clicked (NO in step S149), the teacher performance data provision process returns to step S147. If button 49 is clicked (YES in step S149), the teacher performance data provision process ends.

[0220] If button 52 is clicked (YES in step S147), the CPU 101 provides the terminal with a screen showing the year-on-year trend (step S150 in Figure 39). Specifically, the CPU 101 obtains academic ability information, learning status information, and evaluation values ​​for the assigned subject from each evaluation result table 170 up to the previous year, and calculates representative values ​​(e.g., average values) of the obtained evaluation values ​​for each year and for each content. The CPU 101 calculates representative values ​​of the evaluation values ​​of all learners and representative values ​​of the evaluation values ​​of the learners in the assigned class for each year. These processes are the same as in steps S142 to S145 above. Then, the CPU 101 provides the terminal with a screen showing the evaluation results for each content for each year.

[0221] Figure 41 shows an example of a screen provided in step S150 of Figure 39. Figure 41 shows a screen 53 that shows the year-on-year trend of evaluation results for three years.

[0222] As shown in Figure 41, screen 53 includes an area 58 that displays the year-on-year progression of the evaluation results, and a button 55 for returning to the previous screen.

[0223] Domain 58 includes Domain 48, which displays the evaluation results for each content item for this academic year, as well as Domains 54a and 54b, which display the evaluation results for each content item for each academic year up to last year. Domains 54a and 54b display the evaluation results for each content item for last year and the year before last, respectively. Each of Domains 54a and 54b, like Domain 48, includes Domain 50, which shows the average evaluation result of all learners, and a button 51, which shows the average evaluation result of the learners belonging to the assigned class.

[0224] After step S150, the CPU 101 determines whether or not one of the buttons 51 has been clicked (step S151). Specifically, the CPU 101 determines that one of the buttons 51 has been clicked by receiving a signal from the teacher terminal 600 indicating that one of the buttons 51 has been clicked.

[0225] If the answer is YES in step S151, or YES in step S148 in Figure 38, the CPU 101 provides the teacher terminal 600 with a screen showing details of the class's academic level and learning progress level for the clicked content (step S152). For example, in step S150, the CPU 101 provides a screen in the same format as screen 44 shown in Figure 37.

[0226] Next, the CPU 101 executes steps SS153 and S154, which are the same as steps S129 and S130 shown in Figure 35. That is, the CPU 101 determines whether or not any of the plot points have been clicked (step S153). If the answer in step S153 is YES, the CPU 101 provides the teacher terminal 600 with a screen showing the learner's academic ability information and learning status information corresponding to the clicked plot point (step S154). For example, in step S154, the CPU 101 provides a screen in the same format as screen 38 shown in Figure 34.

[0227] After step S154, or if the answer in step S153 is NO, the CPU 101 determines whether or not buttons 55, 45, and 33 for returning to the previous screen were clicked (step S155). Specifically, the CPU 101 determines that buttons 55, 45, and 33 were clicked by receiving a signal from the teacher terminal 600 indicating that buttons 55, 45, and 33 were clicked.

[0228] If buttons 55, 45, or 33 are clicked (YES in step S155), the teacher performance data provision process returns to the provision step on the previous screen. For example, if button 55 on screen 53 shown in Figure 41 is clicked, the teacher performance data provision process returns to step S146.

[0229] If buttons 55, 45, and 33 have not been clicked (NO in step S155), the teacher performance data provision process returns to the next step in the current screen's provision step. For example, if the current screen is screen 53 shown in Figure 41, the teacher performance data provision process returns to step S151.

[0230] <Variation> (Variation 1) The teaching materials provided by the teaching material provision device 200 include a large amount of content intended for review. It is preferable that a review of a particular unit be conducted during a certain period (for example, one week) after the lesson for that unit. Therefore, it is preferable that learning status information be acquired for each unit, including for a certain period after the lesson for that unit (hereinafter referred to as the "review period"). In other words, the review period may be set as one of the aggregation periods.

[0231] In the example shown in Figure 10, the review period for the unit "Fractions" is set to one week starting from May 10th, the last day of lessons for that unit.

[0232] The review period for each unit is set based on the lesson performance table 160. For example, based on the lesson performance table 160 shown in Figure 9, the start date of the review period for the unit "Decimals" is set to June 1, 2021, which is the first day of lessons for that unit. The end date of the review period for the unit "Decimals" is set to June 9, 2021, one week after June 2, 2021, which is the last day of lessons for that unit.

[0233] Furthermore, learning status information during the review period is generated as follows: The second acquisition unit 13 extracts a record 131 from the learning status table 130 that includes a field 133 (see Figure 6) indicating the date and time of provision that belongs to the review period of the target unit, and a field 135 indicating the curriculum guideline code corresponding to the target unit. Based on the information shown by the extracted record 131, the second acquisition unit 13 generates learning status information for each learner regarding the target unit.

[0234] Furthermore, when acquiring learning progress information for the review period, the second acquisition unit 13 can divide each of the first, second, and third semesters into at least one review period and the remaining period, and use each of the divided periods as the aggregation period.

[0235] (Modification 2) In the processing examples shown in Figures 29 to 41, evaluation results are provided for each "content" as defined by the curriculum guidelines. However, evaluation results may also be provided for each "perspective" as defined by the curriculum guidelines. Evaluation results may also be provided for each "domain" as defined by the curriculum guidelines. Alternatively, evaluation results may be provided for each "unit" as defined by each textbook. As mentioned above, the correlation between units and curriculum guideline codes is provided, for example, by the textbook creator. Based on this correlation, evaluation results for each unit are provided.

[0236] The "perspectives," "content," and "domains" defined by the curriculum guidelines, as well as the "units" defined by each textbook, are classifications that include at least one item from the curriculum guidelines.

[0237] (Variation 3) The evaluation program 112 may cause the CPU 101 to perform the steps of receiving the specification of one of several categories and outputting the evaluation results of the items included in the specified category. As described above, the multiple categories are obtained by classifying the learning scope using at least one of the perspectives defined by the curriculum guidelines, the content defined by the curriculum guidelines, the domains defined by the curriculum guidelines, and the units defined by the textbooks.

[0238] Specifically, CPU101 should obtain the evaluation result by combining the academic ability level and learning status level corresponding to the curriculum code of the items included in the specified category. The method for obtaining the evaluation result is the same as in step S88 in Figure 26.

[0239] Furthermore, "content," "domain," and "unit" may include items corresponding to multiple "perspectives." Therefore, CPU101 may accept designations for classifications defined by combinations of "content" and "perspectives." For example, CPU101 may accept designations for classifications consisting of items corresponding to the content "decimals" and the perspective "knowledge and skills."

[0240] (Modification 4) In step S116 of Figure 30, the CPU 101 may provide the terminal with a screen that displays only one of the selected academic ability information and learning status information.

[0241] (Variation 5) In the above explanation, it was assumed that the scope of learning is subdivided into multiple items defined in the curriculum guidelines. However, the scope of learning may also be subdivided into multiple items according to criteria different from those in the curriculum guidelines. CPU101 can perform a learning assessment for each of these items by combining academic ability information and learning status information.

[0242] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims rather than by the foregoing description, and all modifications within the meaning and scope equivalent to the claims are intended to be included. [Explanation of Symbols]

[0243] 1 Evaluation system, 10 Memory unit, 11 Update unit, 12 First acquisition unit, 13 Second acquisition unit, 14 Evaluation unit, 15 Model generation unit, 16 Prediction unit, 17 Output unit, 20~22 Selection screen, 20a~20e, 21a, 22a~22c Menu, 23, 24, 29, 34, 38, 40, 44, 47, 53 Screen, 25, 26, 28, 28a, 30, 33, 42, 45, 49, 51, 52, 55 Button, 27, 31, 32a, 32c, 35, 36, 39, 41, 46, 48, 50, 54a, 54b, 58 Area, 37 Shape, 43 Class name, 46a~46c Graph, 100 Server device, 101 CPU, 102 RAM, 103 ROM, 104 Hard disk, 105 Memory interface, 106 Network controller, 107 Storage medium, 110 System program, 112 Evaluation program, 120 Academic ability table, 121 Date information, 122, 131, 141, 151, 161, 171, 172, 177~179 Records, 123, 124, 132~136, 142~147, 152~158, 162a~162c, 163, 164a~164c, 165, 166a~166d Fields, 125 Question number, 126 Curriculum code, 130 Learning status table, 140 Learner master, 150 Teacher master, 160 Lesson performance table, 170 Evaluation results table, 173~176 Columns, 180 Academic ability prediction model, 190 Correlation information, 200 educational material provision devices, 300 academic ability test databases, 400 school administration databases, 500 administrator terminals, 600 teacher terminals, 700 student terminals, and N network.

Claims

1. On the computer, Steps to obtain academic ability information to be evaluated, The steps include: obtaining learning status information of the subject to evaluation, The procedure involves performing the step of evaluating the target of evaluation by combining the aforementioned academic ability information and the aforementioned learning status information, The aforementioned learning status information includes, at a minimum, an evaluation program that includes the number of repetitions, which indicates the number of times the same learning material has been used.

2. The aforementioned evaluation step is, A step of retrieving correlation information that shows the relationship between a combination of academic ability level and learning status level and evaluation information that shows the overall evaluation of learning, A step of determining the academic ability level based on the aforementioned academic ability information, A step of determining the learning status level based on the learning status information, The evaluation program according to claim 1, comprising the step of obtaining evaluation information corresponding to the determined combination of academic ability level and learning status level using the correlation information.

3. The evaluation program according to claim 2, wherein the calling step includes the step of selecting the correlation information to be called from among a plurality of correlation information.

4. The learning scope subject to evaluation is subdivided into multiple items, The evaluation program is provided to the computer, For each of the above items, the following steps are performed to associate the academic ability information with the learning status information: The evaluation program according to any one of claims 1 to 3, wherein the evaluation step includes a step of evaluating the target of evaluation for each item using the associated academic ability information and learning status information.

5. The aforementioned items are defined by the curriculum guidelines, The step of acquiring the aforementioned academic ability information includes, for each item, the step of generating the aforementioned academic ability information based on the answers to one or more questions in the academic ability test that correspond to that item, The step of acquiring the learning status information includes, for each item, the step of generating the learning status information from the implementation status of one or more learning materials corresponding to that item. The evaluation program according to claim 4, wherein the associated step includes associating the academic ability information and the learning status information generated for each item.

6. The learning scope to be evaluated is subdivided into multiple sections, Each of the above-mentioned divisions includes at least one of the above-mentioned items, The evaluation program is provided to the computer, The evaluation program according to claim 5, further comprising the step of outputting the evaluation results of the items included in each of the categories.

7. The evaluation program according to claim 6, wherein the plurality of divisions are obtained by classifying the learning scope using at least one of the perspectives defined by the curriculum guidelines, the content defined by the curriculum guidelines, the domains defined by the curriculum guidelines, and the units defined by the textbooks.

8. The evaluation program is provided to the computer, A step of accepting the designation of one of the aforementioned multiple categories, The evaluation program according to claim 6 or 7, further comprising the step of outputting the evaluation results of the items included in the specified category.

9. The aforementioned evaluation target includes one or more learners, The evaluation program is provided to the computer, A step of accepting the selection of target learners from among the one or more learners mentioned above, An evaluation program according to any one of claims 1 to 8, further comprising the step of outputting the evaluation results of the target learner.

10. The aforementioned evaluation subjects include multiple learners, The evaluation program is provided to the computer, A step in which the selection of the target class is accepted from one or more classes, An evaluation program according to any one of claims 1 to 8, further comprising the step of outputting the evaluation results of one or more learners belonging to the target class among the plurality of learners.

11. The step of acquiring the aforementioned academic ability information includes the step of acquiring the aforementioned academic ability information for each period, The step of acquiring the learning status information includes the step of acquiring the learning status information for each of the periods, The evaluation step includes, for each period, a step of evaluating the subject to be evaluated by combining the academic ability information and the learning status information. The evaluation program is provided to the computer, The evaluation program according to any one of claims 1 to 10, further comprising the step of outputting output information showing the time change of the evaluation result of the subject to be evaluated.

12. The step of acquiring the aforementioned academic ability information includes the step of acquiring the aforementioned academic ability information for each period, The step of obtaining the learning status information includes the step of obtaining the learning status information for each period, The evaluation program is provided to the computer, The evaluation program according to any one of claims 1 to 10, further comprising the step of generating an academic ability prediction model by machine learning using the academic ability information and learning status information from the first period and the academic ability information from the second period after a certain period of time has elapsed since the first period as training data.

13. The step of acquiring the aforementioned academic ability information includes the step of acquiring the aforementioned academic ability information for each period, The step of obtaining the learning status information includes the step of obtaining the learning status information for each period, The evaluation program is provided to the computer, A step of predicting the academic ability information after a certain period has elapsed from the target period by inputting the academic ability information and learning status information for the target period into an academic ability prediction model generated by machine learning using the academic ability information and learning status information for the first period and the academic ability information for the second period after a certain period has elapsed from the first period as training data, An evaluation program according to any one of claims 1 to 10, further comprising the step of outputting predicted academic ability information.

14. The evaluation program is provided to the computer, Further steps are performed to output first plotting information for drawing a graph showing academic ability and learning status. The evaluation program according to any one of claims 1 to 13, wherein the first drawing information shows a first evaluation value generated based on the academic ability information as a coordinate value on the first axis of the graph, and a second evaluation value generated based on the learning status information as a coordinate value on the second axis of the graph.

15. The evaluation program is provided to the computer, The steps include outputting second drawing information for drawing a first screen showing evaluation results for each category that includes at least one of the aforementioned multiple items, A step of receiving an instruction to select the evaluation result for the first screen, The evaluation program according to claim 4, further comprising the step of outputting third drawing information for drawing a second screen showing at least one of the academic ability information and the learning status information for the category corresponding to the selected evaluation result.

16. The evaluation program is provided to the computer, The device then performs a step to record the history of providing educational materials from the educational material provisioning device to the terminal being evaluated. The evaluation program according to any one of claims 1 to 15, wherein the step of acquiring the learning status information includes the step of acquiring the number of repetitions as the learning status information based on the history.

17. The first acquisition unit acquires academic ability information to be evaluated, A second acquisition unit acquires learning status information of the subject to evaluation, The system includes an evaluation unit that evaluates the target of evaluation by combining the academic ability information and the learning status information, The device includes, at least, a learning status information, which includes a repetition count indicating the number of times the same learning material has been used.

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