Analysis apparatus, analysis method, and analysis program

Abstract

To provide data suitable for various analyses related to education.SOLUTION: An analysis apparatus includes: a storage unit which stores time-series data of a predetermined time unit, the time-series data indicating a psychological state of a learner; a data collection unit which collects data of items related to learning actions of the leaner, the data corresponding to the time-series data; an extraction unit which extracts time-series data having identical data of a first item selected from among the items related to learning actions of multiple learners, out of time-series data of the learners stored in the storage unit; a statistical processing unit which performs statistical processing on the time-series data extracted by the extraction unit, in a unit of the identical data of the first item; and an output unit which outputs a result of the statistical processing of the statistical processing unit in association with the data of the first item.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to an analysis device, an analysis method, and an analysis program. [Background technology]

[0002] Various educational institutions, such as cram schools, are undertaking various initiatives to provide better educational services and improve the academic abilities of learners.

[0003] For example, various educational institutions are conducting interviews with instructors of each subject about the learning attitudes of students during class, and are then reviewing the content of the textbooks used in class, examining how classes are conducted, and reviewing the learning environment.

[0004] Furthermore, various educational institutions evaluate instructors based on the results of questionnaires given to each student, and allocate appropriate staffing levels.

[0005] Furthermore, various educational institutions visualize each learner's test scores for each subject and unit and provide feedback to each learner. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2023-68455 Summary of the Invention [Problem to be solved by the invention]

[0007] On the other hand, data obtained through interviews with instructors or questionnaires of individual students lack objectivity, and it is difficult to conduct sufficient analysis to improve the educational services provided using only data obtained in this way. Furthermore, improving students' academic abilities requires increasing their learning efficiency, and for this reason, it is desirable to conduct in-depth analysis of, for example, each student's daily learning attitude.

[0008] The present disclosure aims to provide data suitable for performing various analyses related to education. [Means for solving the problem]

[0009] According to one aspect, the analysis device comprises: a storage unit for storing time series data indicating the psychological state of a learner in a predetermined time unit; a data collection unit that collects data on each item related to the learner's learning behavior corresponding to the time-series data; an extracting unit that extracts time-series data of a plurality of learners stored in the storage unit, the time-series data having first items selected from items related to the learning behavior of the learners that are equal to each other; a statistical processing unit that statistically processes the time series data extracted by the extraction unit in units where the data of the first items are equal to each other; The apparatus further includes an output unit that outputs the results of the statistical processing performed by the statistical processing unit in association with the data of the first item. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to provide data suitable for performing various analyses related to education. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of an educational institution to which the analysis system is applied. [Figure 2] FIG. 10 is a diagram showing an example of time-series data relating to the psychological state of a learner, acquired by the analysis system. [Figure 3] FIG. 1 illustrates an example of a system configuration of an analysis system. [Figure 4A] FIG. 10 is a diagram showing a specific example of collected data. [Figure 4B] FIG. 10 is a diagram showing a specific example of feedback information. [Figure 5]FIG. 2 is a diagram illustrating an example of a hardware configuration of an analysis device according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of a functional configuration of an analysis device according to the first embodiment. [Figure 7] FIG. 1 is a first diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 8] FIG. 2 is a second diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 9] FIG. 3 is a third diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 10] FIG. 4 is a fourth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 11] FIG. 5 is a fifth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 12] FIG. 6 is a sixth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 13] FIG. 7 is a seventh diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 14] FIG. 8 is an eighth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 15] FIG. 9 is a ninth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 16] FIG. 10 is a tenth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 17] FIG. 11 is an eleventh diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 18] FIG. 12 is a twelfth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 19] FIG. 13 is a thirteenth diagram showing a specific example of processing by the analysis device according to the first embodiment. [Figure 20A] FIG. 1 is a first diagram showing an example of a proposal. [Figure 20B] FIG. 2 is a second diagram showing an example of a proposal. [Figure 21] This is the third figure showing an example of a proposal. [Figure 22] 4 is an example of a flowchart showing the flow of an analysis process performed by the analysis device according to the first embodiment. [Figure 23] FIG. 10 is a diagram illustrating an example of a functional configuration of an analysis device according to a second embodiment. [Figure 24] FIG. 10 is a diagram showing a specific example of processing by the analysis device according to the second embodiment. [Figure 25] 10 is an example of a flowchart showing the flow of an analysis process performed by an analysis device according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, each embodiment will be described with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0013] [First embodiment] <Examples of application of the analysis system> First, a description will be given of an example of an educational institution to which an analysis system having an analysis device according to the first embodiment is applied. Fig. 1 is a diagram showing an example of an educational institution to which the analysis system is applied.

[0014] As shown in Figure 1, the analysis system is applied to, for example, a large cram school. A large cram school is one that has school buildings in multiple areas and each building has multiple classrooms.

[0015] The example in Figure 1 shows one school building (school building 110) in a given area. School building 110 has ten classrooms, and in classroom 120, for example, a lesson is being taught by instructor 121 to a number of learners 122_1, 122_2, ... using a given textbook.

[0016] 1, a supervisor 131 who manages the entire school building 110 is present in a teacher's room 130 in the school building 110. The supervisor 131 is making various efforts to improve the educational services provided in the school building 110 and the learning efficiency of the learners studying in the school building 110. In this embodiment, the supervisor 131 and the teacher 121 are collectively referred to as instructors.

[0017] As shown in FIG. 1, at the entrance 140 of the school building 110, for example, parents of the learners studying at the school building 110 are waiting to pick up or drop off the learners.

[0018] The analysis system acquires time-series data relating to the psychological state of each learner during a class or test, for a predetermined time unit (each class or each test for each subject). The analysis system also analyzes the acquired time-series data for each class or each test for each subject, and generates feedback information for improving educational services and the learning efficiency of learners based on the results of the analysis process.

[0019] In the first embodiment, the analysis system is described as being applied to a large-scale cram school, but the educational institutions to which the analysis system is applied are not limited to large-scale cram schools, and may also be small-scale cram schools.

[0020] In the first embodiment, the teaching style of the educational institution to which the analysis system is applied is described as a group teaching style in which multiple students receive lessons from one teacher, but the teaching style is not limited to a group teaching style and may be an individual teaching style, or may be a self-study style instead of a class style.

[0021] In addition, in the first embodiment, we will explain the case where the teaching method of the educational institution to which the analysis system is applied is a face-to-face teaching method, but the teaching method is not limited to face-to-face teaching and may be, for example, a teaching method using online video distribution, etc.

[0022] <Data acquired by the analysis system> Next, an example of time-series data relating to the psychological state of a learner acquired by the analysis system in an educational institution to which the analysis system is applied will be described. Figure 2 is a diagram showing an example of time-series data relating to the psychological state of a learner acquired by the analysis system.

[0023] The example in Fig. 2 shows how face image data and vital data are acquired as time-series data relating to the psychological state of the learner 122_1 during class. The method of acquiring face image data is arbitrary, and for example, the face image data may be acquired by taking an image using an imaging device attached to a learning terminal (not shown) operated by the learner 122_1 during class. Alternatively, the face image data may be acquired by taking an image using an imaging device attached to a classroom where the learner 122_1 takes class.

[0024] Furthermore, the method of acquiring the vital data is arbitrary, and for example, the vital data may be acquired by measuring the vital data using a wearable device (not shown) worn by the learner 122_1 during class. The acquired vital data may include, for example, pulse, blood pressure, respiration, body temperature, etc.

[0025] The analysis system generates time-series data indicating the psychological state of the learner 122_1 based on the acquired facial image data and vital data. The psychological state of the learner 122_1 includes emotions such as joy, anger, sadness, and pleasure, concentration / distraction, emotion classification based on the Russell Circle model, emotion classification based on the Plutchik theory, emotion, stress, tension, fatigue, alertness, drowsiness, relaxation, and excitement. The example of FIG. 2 shows that the generated time-series data is two-dimensional data defined based on an axis indicating concentration and distraction and an axis indicating tension and relaxation. However, the time-series data indicating the psychological state of the learner 122_1 generated by the analysis system is not limited to the two-dimensional data and may be two-dimensional data defined based on axes different from those shown in FIG. 2. Alternatively, the time-series data indicating the psychological state of the learner 122_1 generated by the analysis system is not limited to two-dimensional data and may be one-dimensional data or multidimensional data with three or more dimensions.

[0026] <Analysis system configuration> Next, the system configuration of the analysis system will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the system configuration of the analysis system.

[0027] 3, analysis system 300 includes analysis device 310, school building I server device 320, school building I parent terminal 330, etc. In analysis system 300, analysis device 310, school building I server device 320, school building I parent terminal 330, etc. are communicatively connected via network 340.

[0028] The school building I server device 320 is, for example, a server device owned by the school building 110 (school building name: school building I). The school building I server device 320 transmits collected data collected for one lesson (referred to as "collected data (lesson)") and collected data collected for one subject's test (referred to as "collected data (test)") to the analysis device 310.

[0029] In addition, the school building I server device 320 receives feedback information (for instructors) transmitted from the analysis device 310 in response to transmitting the collected data to the analysis device 310. As a result, the instructor of the school building 110 having the school building I server device 320 can, based on the feedback information (for instructors), -Improvement of educational services provided at School Building 110; -Improvement of learning efficiency of students studying in School Building 110 Various initiatives can be undertaken to achieve this.

[0030] The collected data (lessons) sent by the school building I server device 320 to the analysis device 310 includes, for example, Data related to a learner's lessons, covering the time range of one lesson (a specified time unit), Time series data related to the psychological state of learners, covering the time range of one lesson (predetermined time unit); Data related to educational services that changes within the time range of one lesson (within a specified time unit), Data related to the learning environment, including data for the time range of one lesson (predetermined time unit) or time series data for the time range of one lesson (predetermined time unit); Data on learner behavior that is outside the time range of one lesson (outside the specified time unit), Includes:

[0031] In addition, the collected data (test) transmitted by the school building I server device 320 to the analysis device 310 includes, for example, Data related to learner tests, including data for the time range (predetermined time unit) of a test for one subject; Time series data related to the learner's psychological state, which is time series data for the test time range (predetermined time unit) for one subject; Data related to educational services that changes within the time range (within a specified time unit) of a test for one subject; Data related to the test environment, including data for the time range (predetermined time unit) of a test for one subject or time series data for the time range (predetermined time unit) of a test for one subject; Data on learner behavior that is outside the time range of a test for one subject (outside the specified time unit), Includes:

[0032] The school building I server device 320 transmits the collected data (lessons) to the analysis device 310 each time a corresponding lesson ends or after a predetermined time or number of days has passed. Similarly, the school building I server device 320 transmits the collected data (tests) to the analysis device 310 each time a test for one corresponding subject ends or after a predetermined time or number of days has passed.

[0033] The analysis device 310 receives collected data (lessons) and collected data (tests) transmitted from server devices in multiple school buildings, including the school building I server device 320.

[0034] In the first embodiment, data on each item related to a learner's lessons and data on each item related to a learner's tests are collectively referred to as "data on each item related to a learner's learning behavior." Also, in the first embodiment, data on each item related to a learning environment and data on each item related to a test environment are collectively referred to as "data on each item related to the environment."

[0035] Furthermore, the analysis device 310 generates time series data indicating the learner's psychological state from the time series data relating to the learner's psychological state contained in the received collected data (lesson) and collected data (test). Next, the analysis device 310 calculates the generated time series data indicating the learner's psychological state as follows: -Data on each item related to the learner's learning behavior, -Data on various items related to educational services; -Data on various environmental items, -Data on each item of learner behavior, and store it in correspondence with

[0036] Furthermore, the analysis device 310 analyzes the relationship between the time-series data indicating the psychological state of the learner and the data for each item. The analysis device 310 receives suggestions from the analyst regarding the analysis content, and generates feedback information (for the instructor) or feedback information (for the learner).

[0037] Furthermore, the analysis device 310 transmits feedback information (for the instructor) to the school building I server device 320 and the like, and transmits feedback information (for the learner) to the school building I parent terminal 330 and the like.

[0038] The school building I parent terminal 330 is, for example, a terminal owned by the parent of a learner studying at school building 110 (school building name: school building I). For example, when a parent inputs data on the learner's behavior, the school building I parent terminal 330 accepts this and transmits it to the school building I server device 320.

[0039] In addition, the parent terminal 330 in school building I receives feedback information (for the learner) from the analysis device 310. This allows the parent to check the learner's learning attitude during class, the learner's attitude during tests, etc. based on the feedback information (for the learner).

[0040] <Examples of collected data and feedback information> Next, specific examples of collected data (lesson) and collected data (test) received by analysis device 310, and feedback information (for instructor) and feedback information (for learner) transmitted by analysis device 310 will be described.

[0041] (1) Specific examples of collected data FIG. 4A is a diagram showing a specific example of collected data.

[0042] (1-1) Collected data (classes) As shown in Figure 4A, the "data related to learners' lessons" in the collected data (lessons) includes: - Learner's "Student number", "Name", "Age", "Gender", "Temperament", The "class", "school building" and "classroom" in which the learner took the class; -The "subject" and "unit" of the class that the learner took, - The "day of the week" and "start and end times" of the lesson taken by the student, -The "teacher" of the class that the learner took, - The type of textbook used in the class the learner took, - The "class format" of the class the learner took, The data for each item of "data related to the learner's lesson" is automatically received by the analysis device 310 from, for example, the school server device 320 every time one lesson ends.

[0043] In addition, the "class format" mentioned here refers to, for example, - Individual lessons or group lessons? - Will the lessons be delivered via video or face-to-face? In the case of video lessons, are they real-time lessons or lessons that are recorded and played back? Classes or self-study? The data is set in advance before the lesson and clearly states the above.

[0044] In addition, as shown in Figure 4A, the "time series data related to the learners' psychological state" of the collected data (classes) includes the following: - "Facial image data" of the learner during class, - "Vital data" of the learner during the lesson, The time series data for each item of "time series data related to the psychological state of the learner" is automatically received by the analysis device 310 from, for example, the school building server device 320 after each lesson (i.e., for each lesson).

[0045] In addition, as shown in Figure 4A, the "data related to educational services" of the collected data (classes) includes: "How to conduct classes" "Class Contents" "Difficulty level", "Class proceedings" refers to the way in which a teacher conducts a class, for example: When and how long did you take breaks? -How long were the consecutive lectures? -Whether or not there was a quiz to confirm the results, and when the quiz was conducted -When and how long did you chat? · Content of the chat, The data is data that clearly states the manner in which a lesson was conducted by a teacher, and is data related to how the lesson was conducted. The data related to how the lesson was conducted is input by the teacher into, for example, the school building I server device 320 after each lesson, and is received by the analysis device 310.

[0046] "Class content" refers to what the instructor will say in a lesson about a specific unit, for example: What content was taught? · What is the length and order of each content? The data is data that clearly states the contents of the lessons taught by the instructor, etc. The data on the contents of the lessons is input by the instructor to, for example, the school building I server device 320 after each lesson, and is received by the analysis device 310.

[0047] "Difficulty" refers to, for example, the level of difficulty in one lesson. -Difficulty level of each content, The data clearly states the above, and is received by the analysis device 310 when the instructor inputs the data into the school server device 320, for example, at the end of each lesson.

[0048] However, the "difficulty level" is not limited to the case where the instructor inputs data determined in advance for each content. For example, the difficulty level may be determined based on time-series data indicating the learner's psychological state. Determining the difficulty level based on time-series data indicating the learner's psychological state means, for example, determining that the corresponding content is difficult if it is determined from the time-series data indicating the learner's psychological state that the learner is concentrating. Also, determining that the corresponding content is easy if it is determined from the time-series data indicating the learner's psychological state that the learner is not concentrating.

[0049] Alternatively, the "difficulty level" may be determined based on the response time of the learner. Determining the difficulty level based on the response time of the learner means, for example, determining that the corresponding content is of high difficulty when the learner's response time is long, or determining that the corresponding content is of low difficulty when the learner's response time is short.

[0050] When determining the difficulty level based on time-series data indicating the learner's psychological state or based on the learner's response time, the time-series data or response time should be measured in advance.

[0051] In addition, as shown in Figure 4A, the "data related to the learning environment" of the collected data (classes) includes: - The "room temperature," "humidity," "air pressure," "lighting intensity," "lighting color," "carbon dioxide concentration," "wind speed" (wind speed from the air conditioner), "particle concentration" (dust, pollen), and "noise" around the classroom in which the student took the class. - Whether or not there was an "odor" in the classroom where the learner took the class, whether or not "aromatherapy" was used, - Equipment used by the learner during class (materials of desks and chairs, height settings, specifications of learning devices, presence or absence of writing implements), The data for each item of "data related to the learning environment" is input by the instructor into, for example, the school building I server device 320 at the end of each lesson, and is received by the analysis device 310. Alternatively, data that can be measured by a measurement device among the data for each item of "data related to the learning environment" is measured by the measurement device, and is automatically received by the analysis device 310 from, for example, the school building I server device 320 at the end of each lesson (i.e., on a lesson-by-class basis).

[0052] In addition, as shown in Figure 4A, the "data on learner behavior" in the collected data (classes) includes: - The "hours of sleep" on the day before the day the learner attended classes, the "amount of food" and "timing of meals" on the day the learner attended classes, entered by the learner's guardian, - The learner's "break behavior" during the lesson, as entered by the learner; - Whether or not there were any notable events that occurred before the day the learner took the class, as entered by the learner (events that were out of the ordinary for the learner, such as the illness of an acquaintance, the death of a friend, or a broken heart), -Whether or not the learner took a nap just before the lesson, as entered by the learner; The data for each item of "data on learner behavior" is input by the learner or the learner's guardian to, for example, the school server device 320 before a lesson starts or at a predetermined timing after a lesson ends, and is received by the analysis device 310.

[0053] (1-2) Collected data (test) As shown in Figure 4A, the collected data (tests) includes the following: - Learner's "Student number", "Name", "Age", "Gender", "Temperament", The "class", "school building" or "classroom" where the learner took the test for the subject, - The subject of the test taken by the learner; - The "day of the week", "start and end times" on which the learner took the test for the subject, - "Test name" of the test for the subject taken by the learner, The data for each item of "data related to learner's test" is automatically received by the analysis device 310 from, for example, the school server device 320 each time a test for one subject is completed.

[0054] In addition, as shown in Figure 4A, the "time series data related to the learner's psychological state" of the collected data (test) includes the following: - "Facial image data" of the learner during the test of the subject, - "Vital data" of the learner during the test in the subject; The data for each item of the "time-series data related to the psychological state of the learner" is automatically received by the analysis device 310, for example, from the school server device 320, each time a test for one subject is completed (i.e., for each test for one subject).

[0055] In addition, as shown in Figure 4A, the "data related to educational services" in the collected data (tests) includes: Test content, difficulty level, "Test content" refers to the unit of each question in a test for one subject. "Difficulty" refers to the difficulty of each question in a test for one subject.

[0056] Also, as shown in Figure 4A, the "Test environment data" of the collected data (test) includes: - The "room temperature," "humidity," "air pressure," "lighting intensity," "lighting color," "carbon dioxide concentration," "wind speed" (wind speed from the air conditioner), "particle concentration" (dust, pollen), and "noise" around the classroom in which the learner took the test for one subject. - Whether or not there was an "odor" in the classroom where the learner took the test for one subject, whether or not "aromatherapy" was used, - Equipment used by the learner during the test (materials of desks and chairs, height settings, presence or absence of writing implements), The data for each item of "data related to the test environment" is input by the test administrator into, for example, the school I server device 320 each time a test for one subject is completed, and is received by the analysis device 310. Alternatively, data for each item of "data related to the test environment" that can be measured by a measurement device may be measured by the measurement device. In this case, the data measured by the measurement device is automatically received by the analysis device 310 from, for example, the school I server device 320 each time a test for one subject is completed (i.e., for each test for one subject).

[0057] Also, as shown in Figure 4A, the "data on learner behavior" in the collected data (test) includes: - The "hours of sleep" of the learner on the day before the test, the "amount of food" and "meal timing" of the learner on the day the test was taken, entered by the learner's guardian; - The learner's "break behavior" between tests for each subject, entered by the learner; - Whether or not there were any notable events that occurred before the day the learner took the test, as entered by the learner (such as the illness of an acquaintance, the death of a friend, or a broken heart, or other unusual events that occurred to the learner), -Whether or not the learner took a nap just before the test, as entered by the learner; The data for each item of "data related to learner's behavior" is received by the analysis device 310 when the learner or the learner's guardian inputs the data into, for example, the school server device 320 before the test starts or at a predetermined timing after the test ends.

[0058] (2) Examples of feedback information FIG. 4B is a diagram showing a specific example of feedback information.

[0059] (2-1) Feedback information (for instructors) As shown in Figure 4B, the feedback information (for instructors) includes, for example, Classes or subjects that are not of appropriate difficulty -Teachers who have appropriate teaching methods, -Teachers who are not conducting classes properly, - Key points for how to conduct lessons to improve students' psychological state, - Subjects, units, or textbooks used in class that are not of appropriate difficulty classrooms where the learning or testing environment is not appropriate; - Test classes, subjects, etc. where the test difficulty level is not appropriate The above feedback information (for instructors) is merely an example. For example, the feedback information (for instructors) can be created arbitrarily depending on which item of collected data (lessons) or collected data (tests) is focused on, or on how to process the time-series data that indicates the psychological state of the learner.

[0060] (2-2) Feedback information (for learners) As shown in Figure 4B, the feedback information (for learners) includes, e.g. The relationship between the student and the instructor - The relationship between the learner and the course or unit, - The relationship between the learner and the difficulty of the class Data about the learner's behavior that affects lessons or tests; - The relationship between the learner and the test subject or unit, The relationship between the learner and the difficulty of the test The above feedback information (for learners) is merely an example. For example, feedback information (for learners) can be created arbitrarily depending on which item of collected data (lessons) or collected data (tests) is focused on, or on how time-series data indicating the learner's psychological state is processed.

[0061] <Hardware configuration of the analysis device> Next, the hardware configuration of the analysis device 310 according to the first embodiment will be described. Fig. 5 is a diagram showing an example of the hardware configuration of the analysis device according to the first embodiment. As shown in Fig. 5, the analysis device 310 has a processor 501, a memory 502, an auxiliary storage device 503, an I / F (Interface) device 504, a communication device 505, and a drive device 506. The hardware components of the analysis device 310 are connected to each other via a bus 507.

[0062] The processor 501 has various arithmetic devices such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. The processor 501 reads various programs (for example, an analysis program, etc.) into the memory 502 and executes them.

[0063] The memory 502 has a main storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The processor 501 and the memory 502 form a so-called computer, and the computer realizes various functions by the processor 501 executing various programs read onto the memory 502.

[0064] The auxiliary storage device 503 stores various programs and various data used when the processor 501 executes the various programs.

[0065] I / F device 504 accepts operations of an analyst (not shown) on analysis device 310 via operation device 511. In addition, I / F device 504 outputs the results of analysis processing by analysis device 310 and displays them to the analyst via display device 512.

[0066] The communication device 505 is a communication device that connects to the network 340 and communicates with the school building I server device 320, the school building I parent terminal 330, and the like.

[0067] The drive device 506 is a device for setting a recording medium 513. The recording medium 513 here includes media that record information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, a magneto-optical disk, etc. The recording medium 513 may also include semiconductor memories that record information electrically, such as ROMs, flash memories, etc.

[0068] The various programs to be installed in the auxiliary storage device 503 are installed, for example, by setting the distributed recording medium 513 in the drive device 506 and reading the various programs recorded on the recording medium 513 by the drive device 506. Alternatively, the various programs to be installed in the auxiliary storage device 503 may be installed by being downloaded from a network (not shown) via the communication device 505.

[0069] <Functional configuration of the analysis device> Next, the functional configuration of the analysis device 310 according to the first embodiment will be described. Fig. 6 is a diagram showing an example of the functional configuration of the analysis device according to the first embodiment. As described above, an analysis program is installed in the analysis device 310, and by executing the program, the analysis device 310 performs the following as shown in Fig. 6: Data collection unit 601, data generation unit 602, Item selection unit 603, extraction unit 604, Statistical processing unit 605, classification statistical processing unit 606, Output unit 607, analysis unit 608, Evaluation input unit 609, transmission unit 610, It functions as:

[0070] The data collection unit 601 receives, for example, collected data (lessons) and collected data (tests) transmitted from the school I server device 320. The data collection unit 601 notifies the data generation unit 602 of the received collected data (lessons) and collected data (tests).

[0071] The data generating unit 602 generates time series data indicating the psychological state of the learner based on time series data (face image data, vital data) relating to the psychological state of the learner that is included in the notified collected data (lesson). The data generating unit 602 also generates time series data indicating the psychological state of the learner based on time series data (face image data, vital data) relating to the psychological state of the learner that is included in the notified collected data (test).

[0072] The time-series data indicating the mental state of the learner generated by the data generating unit 602 is included in the notified collected data (lesson) or collected data (test). -Data on each item related to the learner's learning behavior, -Data on various items related to educational services; -Data on various environmental items, -Data on each item of learner behavior, are stored in the collected data storage unit 611 in association with the above.

[0073] The item selection unit 603 accepts an item selected by the analyst from among the various items according to the purpose of analysis. The item according to the purpose of analysis specifies the processing unit when processing the time-series data indicating the psychological state of the learner stored in the collected data storage unit 611. The item selection unit 603 notifies the extraction unit 604 of the accepted item.

[0074] The extracting unit 604 extracts time series data indicating the psychological states of a plurality of learners stored in the collected data storage unit 611, the time series data having the same data for the items notified by the item selecting unit 603.

[0075] For example, suppose that the item selection unit 603 notifies one or more items among the multiple items included in "data related to the learner's lessons" in the collected data (lessons) or "data related to the learner's tests" in the collected data (tests) as the first item. In this case, the extraction unit 604 extracts time-series data indicating the learner's psychological state, the data of which are equal to each other in the first item.

[0076] Furthermore, suppose that in addition to the first item, any one of the multiple items included in the "data related to educational services" of the collected data (lessons) or collected data (tests) is notified as the second item from the item selection unit 603. In this case, the extraction unit 604 extracts time-series data indicating the psychological state of the learner, the data of which are equal to each other in the first item and the data of which are equal to each other in the second item.

[0077] Furthermore, suppose that in addition to the first item, any one of the multiple items included in the "data related to the learning environment" of the collected data (lesson) or the "data related to the test environment" of the collected data (test) is notified as the third item from the item selection unit 603. In this case, the extraction unit 604 extracts time-series data indicating the psychological state of the learner, the data of which are equal to each other in the first item and the data of which are equal to each other in the third item.

[0078] Furthermore, suppose that in addition to the first item, the item selection unit 603 notifies one or more items among the multiple items included in the "data related to learner behavior" of the collected data (lesson) or the collected data (test) as the fourth item. In this case, the extraction unit 604 extracts time-series data indicating the psychological state of the learner, the data of which are equal to each other in the first item and the data of which are equal to each other in the fourth item.

[0079] The statistical processing unit 605 performs statistical processing on the time-series data indicating the psychological state of the learner extracted by the extraction unit 604. The statistical processing here includes any statistical processing such as calculating the maximum value, minimum value, average value, median value, standard deviation, etc.

[0080] Specifically, it is assumed that the extraction unit 604 has extracted time-series data indicating the psychological states of learners in which the first item of data is equal. In this case, the statistical processing unit 605 performs statistical processing on the extracted time-series data indicating the psychological states of learners for each unit in which the first item of data is equal. Note that performing statistical processing for each unit in which the first item of data is equal means grouping data in which the first item of data is equal into a group and performing statistical processing on each group.

[0081] Furthermore, it is assumed that the extraction unit 604 has extracted time-series data indicating the psychological states of learners in which the data for the first items are equal to each other and the data for the third items are equal to each other. In this case, the statistical processing unit 605 performs statistical processing on the extracted time-series data indicating the psychological states of learners for each unit in which the data for the first items are equal to each other and the data for the third items are equal to each other. Note that performing statistical processing for each unit in which the data for the first items are equal to each other and the data for the third items means grouping together data in which the data for the first items are equal to each other and the data for the third items are equal to each other and performing statistical processing on each of these groups.

[0082] Furthermore, it is assumed that the extraction unit 604 has extracted time-series data indicating the psychological states of learners in which the data for the first items are equal to each other and the data for the fourth items are equal to each other. In this case, the statistical processing unit 605 performs statistical processing on the extracted time-series data indicating the psychological states of learners for each unit in which the data for the first items are equal to each other and the data for the fourth items are equal to each other. Note that performing statistical processing for each unit in which the data for the first items are equal to each other and the data for the fourth items means grouping together data in which the data for the first items are equal to each other and the data for the fourth items are equal to each other and performing statistical processing on each of these groups.

[0083] The time series data (statistical data) indicating the psychological state of the learner, which has been statistically processed by the statistical processing unit 605, is stored in the processing unit data storage unit 612 as processing unit data in correspondence with the data of each item used in the statistical processing.

[0084] The segmented statistical processing unit 606 performs segmented statistical processing on the time series data indicating the psychological state of the learner extracted by the extraction unit 604. The segmented statistical processing refers to dividing the time series data indicating the psychological state of the learner into a plurality of time segments according to the data of the second item, and performing statistical processing for each unit of data of the first item that is equal to each other and for each time segment unit. Note that performing statistical processing for each unit of data of the first item that is equal to each other and for each time segment unit means grouping data of the first item that is equal to each other and for each time segment into a group, and performing statistical processing on each of these groups.

[0085] Specifically, it is assumed that the extracting unit 604 has extracted time series data indicating the psychological state of a learner in which the first item data is equal to each other and the second item data is equal to each other. In this case, the sectional statistical processing unit 606 performs sectional statistical processing on the extracted time series data indicating the psychological state of the learner for each unit in which the first item data is equal to each other and for each time segment unit.

[0086] The time series data (category statistical data) indicating the psychological state of the learner, which has been subjected to category statistical processing by the category statistical processing unit 606, is stored in the processing unit data storage unit 612 as processing unit data in correspondence with the data of each item used in the category statistical processing.

[0087] The output unit 607 reads out data of items according to the analysis purpose and corresponding statistical data (or segmented statistical data) from the processing unit data storage unit 612, associates them, and outputs them to the analysis unit 608.

[0088] The analysis unit 608 analyzes the relationship between the data of the item according to the analysis purpose output by the output unit 607 and the corresponding statistical data (or categorized statistical data), and determines whether or not there is a correlation. The analysis unit 608 notifies the evaluation input unit 609 of the data of the item according to the analysis purpose and the corresponding statistical data (or categorized statistical data) that it has determined to be correlated.

[0089] The evaluation input unit 609 outputs the correlation notified by the analysis unit 608 to the analyst. Furthermore, in response to the output of the correlation, if the analyst makes a recommendation about what should be done to improve educational services or what should be done to improve the learning efficiency of learners, the evaluation input unit 609 accepts this recommendation.

[0090] Furthermore, the evaluation input unit 609 generates feedback information (for the instructor) or feedback information (for the learner) including suggestions from the analyst, and notifies the transmission unit 610 of the same.

[0091] When the evaluation input unit 609 notifies the transmission unit 610 of feedback information (for the instructor), the transmission unit 610 transmits the feedback information (for the instructor), for example, to the school building I server device 320. Furthermore, when the evaluation input unit 609 notifies the transmission unit 610 of feedback information (for the learner), the transmission unit 610 transmits the feedback information (for the learner), for example, to the school building I parent terminal 330.

[0092] <Examples of processing by analytical equipment> Next, a specific example of processing by the analysis device 310 according to the first embodiment will be described.

[0093] (1) Example 1 Fig. 7 is a first diagram showing a specific example of processing by the analysis device according to the first embodiment. In Fig. 7, processing unit data 700 shows that "class", "subject", "unit", and "text type" are selected as first items by the item selection unit 603 from among multiple items included in "data related to learner's lessons" of the collected data (lessons).

[0094] In addition, in FIG. 7, the processing unit data 700 includes the following time-series data indicating the mental state of the learner, in which the data of the first item is equal to each other: Time series data 701 showing the psychological state of a learner in "Class" = "A", "Subject" = "Arithmetic", "Unit" = "Geometry", and "Textbook Type" = "Type α"; Time-series data 702 showing the psychological state of a learner in "Class" = "B", "Subject" = "Arithmetic", "Unit" = "Geometry", and "Textbook Type" = "Type α"; Time series data 703 showing the psychological state of a learner in "Class" = "C", "Subject" = "Mathematics", "Unit" = "Geometry", and "Textbook Type" = "Type β"; shows the extracted state.

[0095] Furthermore, in FIG. 7, processing unit data 700 shows how statistical data is calculated by performing statistical processing on each of time-series data 701 to 703 indicating the psychological state of the learner.

[0096] As shown in FIG. 7, according to the processing unit data 700, for example, for each lesson class, The content being studied is too difficult for the learner to understand, or The content being studied is too easy and learners get bored. It is possible to analyze whether or not the following events have occurred (see reference numeral 710).

[0097] In the example of Figure 7, the case where "Subject" = "Arithmetic" is shown, but other subjects can be analyzed in the same way. Also, in the example of Figure 7, the case where "Unit" = "Geometry" is shown, but other units can be analyzed in the same way.

[0098] (2) Example 2 Fig. 8 is a second diagram showing a specific example of processing by the analysis device according to the first embodiment. In Fig. 8, processing unit data 800 shows that "school building," "subject," "unit," and "teacher" have been selected as first items by the item selection unit 603 from among multiple items included in "data related to learner's lessons" of the collected data (lessons).

[0099] In addition, in FIG. 8, the processing unit data 800 includes the following time-series data indicating the mental state of the learner, in which the data of the first item is equal to each other: Time series data 801 showing the psychological state of a learner in "school building" = "I", "subject" = "arithmetic", "unit" = "geometry", and "teacher" = "teacher a", Time series data 802 showing the psychological state of a learner in "school building" = "II", "subject" = "arithmetic", "unit" = "geometry", and "teacher" = "teacher b", Time series data 803 showing the psychological state of a learner in "school building" = "III", "subject" = "arithmetic", "unit" = "geometry", and "teacher" = "teacher c", shows the extracted state.

[0100] Furthermore, in FIG. 8, processing unit data 800 shows how statistical data is calculated by performing statistical processing on each of time-series data 801 to 803 indicating the psychological state of the learner.

[0101] As shown in FIG. 8, the processing unit data 800 makes it possible to analyze whether there are differences in the psychological state of learners depending on the teacher, even for the same subject and unit (see reference numeral 810).

[0102] In the example of Figure 8, the case where "Subject" = "Arithmetic" is shown, but other subjects can be analyzed in the same way. Also, in the example of Figure 8, the case where "Unit" = "Geometry" is shown, but other units can be analyzed in the same way.

[0103] (3) Example 3 9 is a third diagram showing a specific example of processing by the analysis device according to the first embodiment. In FIG. 9, processing unit data 900 includes: Among the multiple items included in the "data related to the learner's lessons" of the collected data (lessons), the "subject" and "lesson instructor" are selected as the first items by the item selection unit 603, Among the multiple items included in the "data related to educational services," "lesson progress" is selected as the second item by the item selection unit 603. It shows the situation.

[0104] In addition, in FIG. 9, the processing unit data 900 is represented as time-series data indicating the psychological states of learners whose first item data is equal to each other and whose second item data is equal to each other, as follows: Time-series data 901 showing the psychological state of a learner in which "subject" = "arithmetic", "teacher" = "teacher A", and "teaching method" = "pattern 1"; Time-series data 902 showing the psychological state of a learner in which "subject" = "arithmetic", "teacher" = "teacher A", and "teaching method" = "pattern 2"; Time-series data 903 showing the psychological state of learners in the case of "subject" = "arithmetic", "teacher" = "teacher A", and "teaching method" = "pattern 3", shows the extracted state.

[0105] Furthermore, in FIG. 9, processing unit data 900 shows how sectional statistical data is calculated by performing sectional statistical processing on each of time-series data 901 to 903 indicating the psychological state of the learner.

[0106] 9, according to the processing unit data 900, it is possible to analyze whether there are any differences in the psychological state of learners when the way a lesson is taught is changed, even for the same subject and teacher (see reference numeral 910). Note that the example in Fig. 9 shows cases where the way a lesson is taught is varied in various ways, such as the length of breaks, the number of breaks, the timing of breaks, whether or not casual conversation is allowed, the timing of casual conversation, the length of one lecture, etc.

[0107] (4) Example 4 10 is a fourth diagram illustrating a specific example of processing by the analysis device according to the first embodiment. In FIG. 10, processing unit data 1000 is Among the multiple items included in the "data related to the learner's lessons" of the collected data (lessons), the "class", "subject", "unit", and "text type" are selected as first items by the item selection unit 603, Among the multiple items included in the "data related to educational services," the "lesson content" is selected as the second item by the item selection unit 603. It shows the situation.

[0108] In addition, in FIG. 10, the processing unit data 1000 is time-series data indicating the psychological states of learners whose first item data is equal to each other and whose second item data is equal to each other, as follows: Time series data 1001 showing the psychological state of a learner in "Class" = "A", "Subject" = "Arithmetic", "Unit" = "Geometry", "Textbook Type" = "Type α", "Lesson Content" = "Pattern AA", Time-series data 1002 showing the psychological state of a learner in "Class" = "B", "Subject" = "Arithmetic", "Unit" = "Geometry", "Textbook Type" = "Type α", "Lesson Content" = "Pattern AA", Time series data 1003 showing the psychological state of a learner in "Class" = "C", "Subject" = "Arithmetic", "Unit" = "Geometry", "Textbook Type" = "Type β", "Lesson Content" = "Pattern BB", shows the extracted state.

[0109] Furthermore, in FIG. 10, processing unit data 1000 shows how sectional statistical data is calculated by performing sectional statistical processing on each of time-series data 1001 to 1003 indicating the psychological state of the learner.

[0110] As shown in FIG. 10, the processing unit data 1000 can be used to analyze whether, for example, the content being studied is too difficult to understand, or too easy and the student is getting bored (see symbol 1010).

[0111] In the example of Figure 10, the case where "Subject" = "Arithmetic" is shown, but analysis can be performed in the same way for other subjects. In addition, in the example of Figure 10, the case where "Unit" = "Geometry" is shown, but analysis can be performed in the same way for other units. In addition, in the example of Figure 10, the case where "Text Type" = "Type α" and the case where "Text Type" = "Type β" are shown, but analysis can be performed in the same way for other text types.

[0112] (5) Example 5 Fig. 11 is a fifth diagram showing a specific example of processing by the analysis device according to the first embodiment. In Fig. 11, processing unit data 1100 shows that "school building," "classroom," "day of the week," and "start and end times" have been selected as first items by the item selection unit 603 from among multiple items included in "data related to learner's lessons" in the collected data (lessons).

[0113] Also, in FIG. 11, the processing unit data 1100 shows that, of the multiple items included in the "data related to the learning environment" of the collected data (lesson), "room temperature" has been selected by the item selection unit 603 as the third item.

[0114] In addition, in FIG. 11, the processing unit data 1100 is time-series data indicating the psychological states of learners whose first item data is equal to each other and whose third item data is equal to each other, as follows: Time series data 1101 showing the psychological state of a learner in "school building" = "I", "classroom" = "Room 31", "day of the week" = "Wednesday", "start and end times" = "17:30-19:00", and "room temperature" = "T1 degrees" Time series data 1102 showing the psychological state of a learner in "school building" = "I", "classroom" = "Room 32", "day of the week" = "Wednesday", "start and end times" = "17:30-19:00", and "room temperature" = "T2 degrees", Time series data 1103 showing the psychological state of a learner in "school building" = "I", "classroom" = "Room 33", "day of the week" = "Wednesday", "start and end times" = "17:30-19:00", "room temperature" = "T3 degrees", shows the extracted state.

[0115] Furthermore, in FIG. 11, processing unit data 1100 shows how statistical data is calculated by performing statistical processing on each of time-series data 1101 to 1103 indicating the psychological state of the learner.

[0116] As shown in FIG. 11, the processing unit data 1100 makes it possible to analyze, for example, whether the room temperature in each classroom is a factor that is deteriorating the psychological state of the learners, for each day of the week, start time, and end time (see symbol 1110).

[0117] In the example of Figure 11, we have shown the case where "room temperature" is analyzed, which is one of the items included in "data related to the learning environment." However, other items included in "data related to the learning environment" can also be analyzed in the same way.

[0118] (6) Example 6 12 is a sixth diagram illustrating a specific example of processing by the analysis device according to the first embodiment. In FIG. 12, processing unit data 1200 includes: Among the multiple items included in the "data related to the learner's test" of the collected data (test), the "class" and "subject" are selected as first items by the item selection unit 603, Among the multiple items included in the "data related to educational services," "difficulty level" is selected as the second item by the item selection unit 603; It shows the situation.

[0119] In addition, in FIG. 12, the processing unit data 1200 is time-series data indicating the psychological states of learners whose first item data is equal to each other and whose second item data is equal to each other, as follows: Time series data 1201 showing the psychological state of a learner in "Class" = "A", "Subject" = "Mathematics", and "Difficulty" = "Pattern L"; Time series data 1202 showing the psychological state of a learner in "Class" = "B", "Subject" = "Mathematics", and "Difficulty" = "Pattern L"; shows the extracted state.

[0120] Furthermore, in FIG. 12, processing unit data 1200 shows how sectional statistical data is calculated by performing sectional statistical processing on each of time-series data 1201 and 1202 indicating the psychological state of the learner.

[0121] As shown in FIG. 12, according to the processing unit data 1200, it is possible to analyze whether there is a difference in the psychological state of the learner depending on the difficulty of the test questions, for example, in test class units (see reference numeral 1210).

[0122] (7) Example 7 Fig. 13 is a seventh diagram showing a specific example of processing by the analysis device according to the first embodiment. In Fig. 13, processing unit data 1300 shows that, of multiple items included in "data related to learner's lessons" of collected data (lessons), "subject" and "lesson instructor" are selected as first items by the item selection unit 603. Note that the example in Fig. 13 shows a case where one specific learner is the target.

[0123] In addition, in FIG. 13, the processing unit data 1300 is time-series data indicating the psychological states of learners whose first item data is equal to each other, as follows: Time series data 1301 showing the psychological state of a learner whose "subject" is "arithmetic" and whose "teacher" is "Teacher A"; Time series data 1302 showing the psychological state of a learner whose "subject" is "Japanese" and whose "teacher" is "Teacher B"; Time series data 1303 showing the psychological state of a learner whose "subject" is "science" and whose "teacher" is "teacher c"; shows the extracted state.

[0124] Furthermore, in FIG. 13, processing unit data 1300 shows how statistical data is calculated by performing statistical processing on each of time-series data 1301 to 1303 indicating the psychological state of the learner.

[0125] As shown in FIG. 13, the processing unit data 1300 makes it possible to analyze, for example, whether there are differences in the psychological state depending on the teacher for each learner (see reference numeral 1310).

[0126] (8) Example 8 Fig. 14 is an eighth diagram showing a specific example of processing by the analysis device according to the first embodiment. In Fig. 14, processing unit data 1400 shows that "subject" and "unit" are selected as first items by the item selection unit 603 from among multiple items included in "data related to lessons of learners" of collected data (lessons). Note that the example in Fig. 14 shows a case where one specific learner is the target.

[0127] In addition, in FIG. 14, the processing unit data 1400 is time-series data indicating the psychological states of learners whose first item data is equal to each other, as follows: Time series data 1401 showing the psychological state of learners in "subject" = "arithmetic" and "unit" = "calculation"; Time series data 1402 showing the psychological state of learners for "subject" = "arithmetic" and "unit" = "speed"; Time series data 1403 showing the psychological state of learners in "subject" = "arithmetic" and "unit" = "geometry"; shows the extracted state.

[0128] Furthermore, in FIG. 14, processing unit data 1400 shows how statistical data is calculated by performing statistical processing on each of time-series data 1401 to 1403 indicating the psychological state of the learner.

[0129] As shown in FIG. 14, according to the processing unit data 1400, it is possible to analyze for each learner whether there are differences in psychological states depending on the subject or unit, for example (see reference numeral 1410).

[0130] (9) Example 9 15 is a ninth diagram illustrating a specific example of processing by the analysis device according to the first embodiment. In FIG. 15, processing unit data 1500 includes: Among the multiple items included in the "data related to the learner's lessons" of the collected data (lessons), the "subject" is selected as the first item by the item selection unit 603, Among the multiple items included in the "data related to educational services," "difficulty level" is selected as the second item by the item selection unit 603; The example in Figure 15 shows the situation when one specific learner is the target.

[0131] In addition, in FIG. 15, the processing unit data 1500 is time-series data indicating the psychological states of learners whose first item data is equal to each other and whose second item data is equal to each other, as follows: Time series data 1501 showing the psychological state of learners for "subject" = "arithmetic" and "difficulty" = "1 → 3 → 3 → 4" Time series data 1502 showing the psychological state of learners for "subject" = "Japanese" and "difficulty" = "1 → 4 → 4 → 2"; Time series data 1503 showing the psychological state of learners for "subject" = "science" and "difficulty" = "3 → 5 → 5 → 1" Time series data 1504 showing the psychological state of learners for "Subject" = "Social Studies" and "Difficulty" = "2 → 4 → 5 → 1" shows the extracted state.

[0132] Furthermore, in FIG. 15, processing unit data 1500 shows how sectional statistical data is calculated by performing sectional statistical processing on each of time-series data 1501 to 1504 indicating the psychological state of the learner.

[0133] As shown in FIG. 15, according to the processing unit data 1500, it is possible to analyze for each learner whether there is a difference in psychological state depending on the difficulty level of the lesson (see reference numeral 1510).

[0134] (10) Example 10 16 is a tenth diagram illustrating a specific example of processing by the analysis device according to the first embodiment. In FIG. 16, processing unit data 1600 includes: Among the multiple items included in the "data related to the learner's test" of the collected data (test), the "test name" and "start and end times" are selected as first items by the item selection unit 603, Among the multiple items included in the "data on learner behavior" of the collected data (test), "sleep time" and "meal amount and meal timing" are selected as the fourth item by the item selection unit 603. The example in Figure 16 shows the situation when one specific learner is the target.

[0135] In addition, in FIG. 16, the processing unit data 1600 is time-series data showing the psychological states of learners whose first item data is equal to each other (here, the test time is in the morning) and whose fourth item data is equal to each other, as follows: Time-series data 1601 showing the psychological state of a learner with "Test name" = "First test", "Start and end time" = morning ("9:00-9:45" to "11:00-11:30"), "Sleep time" = "less than 6 hours", and "Meal amount and timing" = "No breakfast", Time series data 1602 showing the psychological state of a learner with "test name" = "Second test", "start and end time" = morning ("9:00-9:45" to "11:00-11:30"), "sleep time" = "8 hours or more", and "meal amount and timing" = "no breakfast", shows the extracted state.

[0136] Furthermore, in FIG. 16, processing unit data 1600 shows how statistical data is calculated by performing statistical processing on time-series data 1601 indicating the psychological state of the learner.

[0137] As shown in FIG. 16, the processing unit data 1600 can be used to analyze whether, for example, among the multiple items of data included in the "data related to learner behavior," there is any data that is correlated with data indicating the learner's psychological state during the test (see symbol 1610).

[0138] (11) Example 11 17 is an eleventh diagram showing a specific example of processing by the analysis device according to the first embodiment. In FIG. 17, processing unit data 1700 includes: Among the multiple items included in the "data related to the learner's test" of the collected data (test), the "subject" is selected as the first item by the item selection unit 603, Among the multiple items included in the "data related to educational services," the "test content" is selected as the second item by the item selection unit 603. The example in Figure 17 shows the situation when one specific learner is the target.

[0139] In addition, in FIG. 17, the processing unit data 1700 is time-series data indicating the psychological states of learners whose first item data is equal to each other and whose second item data is equal to each other, as follows: Time series data 1701 showing the psychological state of learners in "subject" = "arithmetic" and "test content" = "calculation → speed → shape → ratio" Time series data 1702 showing the psychological state of learners for "subject" = "Japanese language" and "test content" = "Kanji → Idioms → Writing → Reading comprehension" Time series data 1703 showing the psychological state of learners for "subject" = "science" and "test content" = "plants → moon → pulley → electric current" Time series data 1704 showing the psychological state of learners for "Subject" = "Social Studies" and "Test Content" = "History → Geography → Civics → Current Affairs" shows the extracted state.

[0140] Furthermore, in FIG. 17, processing unit data 1700 shows how sectional statistical data is calculated by performing sectional statistical processing on each of time-series data 1701 to 1704 indicating the psychological state of the learner.

[0141] As shown in FIG. 17, according to the processing unit data 1700, it is possible to analyze for each learner whether there is a difference in psychological state depending on the test content, for example (see reference numeral 1710).

[0142] (12) Example 12 18 is a twelfth diagram showing a specific example of processing by the analysis device according to the first embodiment. In FIG. 18, processing unit data 1800 includes: Among the multiple items included in the "data related to the learner's test" of the collected data (test), the "subject" is selected as the first item by the item selection unit 603, Among the multiple items included in the "data related to educational services," "difficulty level" is selected as the second item by the item selection unit 603; The example in Figure 18 shows the situation when one specific learner is the target.

[0143] In addition, in FIG. 18, the processing unit data 1800 is · Time series data 1801 showing the psychological state of learners for "subject" = "arithmetic" and "difficulty" = "1 → 4 → 3 → 5" Time series data 1802 showing the psychological state of learners for "subject" = "Japanese" and "difficulty" = "4 → 2 → 4 → 4" Time series data 1803 showing the psychological state of learners for "Subject" = "Science" and "Difficulty" = "3 → 4 → 2 → 4" Time series data 1804 showing the psychological state of learners for "Subject" = "Social Studies" and "Difficulty" = "3 → 5 → 3 → 1" shows the extracted state.

[0144] Furthermore, in FIG. 18, processing unit data 1800 shows how sectional statistical data is calculated by performing sectional statistical processing on each of time-series data 1801 to 1804 indicating the psychological state of the learner.

[0145] As shown in FIG. 18, according to the processing unit data 1800, for example, it is possible to analyze for each learner whether there is a difference in psychological state depending on the difficulty of test questions in each subject (see reference numeral 1810).

[0146] (13) Example 13 19 is a thirteenth diagram showing a specific example of processing by the analysis device according to the first embodiment. In FIG. 19, processing unit data 1900 includes: Among the multiple items included in the "data related to the learner's lessons" of the collected data (lessons), the "class", "day of the week", and "start and end times" are selected as first items by the item selection unit 603, Among the multiple items included in the "data on learner behavior" of the collected data (lessons), "sleep time," "meal amount and meal timing," and "whether or not naps were taken" were selected as the fourth item by the item selection unit 603. The example in Figure 19 shows the situation when one specific learner is the target.

[0147] In addition, in FIG. 19, the processing unit data 1900 is time-series data indicating the psychological states of learners whose first item of data is equal to each other (here, the class time is either morning or afternoon) and whose fourth item of data is equal to each other, as follows: Time series data 1903 showing the psychological state of a learner with "Class" = "Summer Intensive Class", "Day of the Week" = "Monday", "Start and End Times" = "Morning (9:00~12:15)", "Sleep Hours" = "8 hours or more", and "Amount of Meal, Meal Timing" = "No Breakfast" Time series data 1904 showing the psychological state of a learner, where "class" = "summer intensive class", "day of the week" = "Monday", "start and end time" = afternoon ("13:00-14:45"), and "whether or not a nap was taken" = "yes"; Time series data 1901 showing the psychological state of a learner with "Class" = "Summer Intensive Class", "Day of the Week" = "Tuesday", "Start and End Times" = "Morning (9:00~12:15)", "Sleep Hours" = "Less than 6 hours", and "Meal Amount and Timing" = "No Breakfast" Time series data 1902 showing the psychological state of a learner, where "class" = "summer intensive class", "day of the week" = "Tuesday", "start and end time" = afternoon ("13:00-14:45"), and "whether or not taking a nap" = "no", shows the extracted state.

[0148] Furthermore, in FIG. 19, processing unit data 1900 shows how statistical data is calculated by performing statistical processing on time-series data 1901 to 1904 indicating the psychological state of the learner.

[0149] As shown in FIG. 19, the processing unit data 1900 can be used to analyze whether, for example, among multiple items of data contained in data relating to learner behavior, there is data that is correlated with data indicating the learner's psychological state during class (see symbol 1910).

[0150] <Example of a recommendation to be entered by the analyst 1> Next, the result of the analysis process by the analysis unit 608 is output to the analyst, and the following proposals are made by the analyst: What leaders should do to improve educational services, or -What instructors or students (or parents) should do to improve students' learning efficiency, A specific example of this will be described.

[0151] 20A and 20B are first and second diagrams showing a recommendation example. In FIG. 20A, the "analysis content" of "specific example 1" indicates the content analyzed based on the processing unit data 700 shown in FIG. 7. As a result of the analysis process, for example, the analyst determines that the learners belonging to class B may not be able to understand the arithmetic textbook (text type = type α) because it is too difficult. In this case, the analyst can recommend to the instructor that the textbook used in class B's arithmetic lessons be changed from type α to type β.

[0152] In Fig. 20A, the "analysis content" of "specific example 2" indicates the content analyzed based on the processing unit data 800 shown in Fig. 8. If, as a result of the analysis process, the analyst determines that, for example, math classes taught by teacher b often leave learners in a bad psychological state, the analyst can recommend to the instructor that teacher b be reassigned.

[0153] In Fig. 20A, the "analysis content" of "specific example 3" indicates the content analyzed based on the processing unit data 900 shown in Fig. 9. If, as a result of the analysis process, the analyst determines that, for example, the number of breaks during class and the presence or absence of chatting are correlated with the psychological state of the learners, the analyst can suggest to the instructor the optimal number of breaks, the optimal number of chats, etc. for progressing the class.

[0154] In FIG. 20A, the "analysis content" of "specific example 4" indicates the content analyzed based on the processing unit data 1000 shown in FIG. 10. As a result of the analysis process, for example, the analyst determines that the content of area ratio in the unit on shapes in the arithmetic textbook (text type = type α) is too easy and that the learners in class A may be bored. In this case, the analyst can recommend to the instructor that the difficulty level of the content of area ratio in the unit on shapes in the arithmetic textbook (text type = type α) be increased.

[0155] In FIG. 20A, the "analysis content" of "specific example 5" indicates the content analyzed based on the processing unit data 1100 shown in FIG. 11. As a result of the analysis process, for example, the analyst determines that during daytime hours in August, a learner attending a class in a classroom on the third floor may be feeling sleepy because the room temperature is too high. In this case, the analyst can recommend to the instructor that the air conditioning be set to a lower temperature for that particular classroom during that time period.

[0156] In FIG. 20A, the "analysis content" of "Specific Example 6" indicates the content analyzed based on the processing unit data 1200 shown in FIG. 12. As a result of the analysis process, for example, the analyst may determine that learners belonging to class A maintain a good psychological state even when the difficulty of test questions increases, but that learners belonging to class B tend to have a worsening psychological state when the difficulty of test questions increases. In this case, the analyst can recommend to the instructor that the difficulty of test questions taken by learners belonging to class B be lowered.

[0157] In FIG. 20A, the "analysis content" of "Specific Example 7" indicates the content analyzed based on the processing unit data 1300 shown in FIG. 13. As a result of the analysis process, for example, the analyst determines that learner X's psychological state is good in classes taught by teacher a, but that his psychological state deteriorates in classes taught by teacher b. In this case, the analyst determines that learner X and teacher b do not get along well, and can recommend to the instructor that learner X be transferred to another class.

[0158] In FIG. 20B, the "analysis content" of "specific example 8" indicates the content analyzed based on the processing unit data 1400 shown in FIG. 14. As a result of the analysis process, for example, the analyst determines that Learner X's psychological state has deteriorated in the unit on speed in arithmetic. In this case, since Learner X feels that he is not good at the unit on speed in arithmetic, the analyst can recommend to the instructor that Learner X be given supplementary lessons on this unit.

[0159] In FIG. 20B, the "analysis content" of "Specific Example 9" indicates the content analyzed based on the processing unit data 1500 shown in FIG. 15. As a result of the analysis process, for example, the analyst may determine that Learner X's psychological state tends to worsen as the difficulty level of the class decreases. In this case, the analyst can recommend to the instructor that Learner X should actively ask applied questions in quizzes during class.

[0160] In FIG. 20B, the "analysis content" of "Specific Example 10" indicates the content analyzed based on the processing unit data 1600 shown in FIG. 16. As a result of the analysis process, for example, the analyst determines that learner X's psychological state tends to worsen when he or she sleeps less. In this case, the analyst can recommend to the guardian of learner X that learner X increase the amount of sleep the day before the test.

[0161] In FIG. 20B, the "analysis content" of "Specific Example 11" indicates the content analyzed based on the processing unit data 1700 shown in FIG. 17. As a result of the analysis process, for example, the analyst determines that Learner X is able to maintain a good psychological state regardless of the test content, but when the test results are reviewed, it is found that Learner X made many careless mistakes. In this case, the analyst can determine that Learner X may not have fully mastered the learning content and recommend that Learner X prioritize mastering the learning content.

[0162] In FIG. 20B, the "analysis content" of "Specific Example 12" indicates the content analyzed based on the processing unit data 1800 shown in FIG. 18. As a result of the analysis process, for example, the analyst determines that Learner X's psychological state tends to worsen when the difficulty level is high. In this case, the analyst can recommend to the instructor that Learner X's test class be changed and the difficulty level of the test questions be adjusted.

[0163] In FIG. 20B, the "analysis content" of "Specific Example 13" indicates the content analyzed based on the processing unit data 1900 shown in FIG. 19. As a result of the analysis process, for example, the analyst determines that learner X's psychological state tends to worsen when he or she sleeps less, but that taking a nap during breaks can improve that psychological state. In this case, the analyst can recommend to learner X that he or she ensures sufficient sleep, and if he or she is unable to ensure sufficient sleep, can recommend that he or she actively take a nap during breaks.

[0164] <Example of recommendation entered by analyst 2> Next, the result of the analysis process by the analysis unit 608 is output to the analyst, and the following proposals are made by the analyst: What leaders should do to improve educational services, or -What instructors or students (or parents) should do to improve students' learning efficiency, Another specific example will be described.

[0165] FIG. 21 is a third diagram showing a proposal example, and is a diagram showing the results of analysis processing based on processing unit data other than the processing unit data 700 to 1900 shown in FIGS.

[0166] In FIG. 21, the "analysis content" of "Other specific example 1" shows a case where an analysis is performed to determine whether there are any differences in the psychological state of learners due to differences in classroom furniture (desks, chairs, materials and height settings, specifications of the learning devices used, writing implements). As a result of the analysis process, the analyst may determine, for example, that learners belonging to a class using learning devices with low specifications tend to have a worsening psychological state overall. In this case, the analyst can recommend to the instructor that the learning devices for that class be updated.

[0167] Also, in Figure 21, the "analysis content" of "Other specific example 2" shows a case where an analysis is performed to determine whether aromatherapy introduced in a specific classroom has a relaxing effect on the students using that classroom. As a result of the analysis process, the analyst may determine that aromatherapy tends to have a stronger relaxing effect in, for example, difficult Japanese language classes. In this case, the analyst can recommend to the instructor that aromatherapy be actively introduced in difficult Japanese language classes.

[0168] Furthermore, in FIG. 21, the "analysis content" of "Other specific example 3" shows a case where an analysis is performed on a lesson instructor basis to determine whether there are differences in the psychological state of learners depending on the school, region, or country. As a result of the analysis process, the analyst may determine, for example, that learners at school I have a better psychological state for a particular subject than learners at school II. Furthermore, as a result of the analysis process, the analyst may determine, for example, that learners at school I have a better psychological state in particular in teacher A's class. In this case, the analyst may recommend, for example, that the teaching method of school I be applied to school II for a particular subject.

[0169] Furthermore, in FIG. 21, the "analysis content" of "Other specific example 4" shows a case where an analysis is performed to determine whether there is any difference in the psychological state of a learner depending on the start and end times of a class. As a result of the analysis process, for example, the analyst determines that Learner X's psychological state tends to improve in the morning and that he is a morning person. In this case, the analyst can suggest to Learner X that he should study his weak subjects in the morning when studying on his own.

[0170] Furthermore, in FIG. 21, the "analysis content" of "Other specific example 5" shows a case where an analysis is performed to determine whether there are any differences in the learner's psychological state depending on the self-study subject. As a result of the analysis process, for example, the analyst may determine that in the case of learner X, the psychological state is good when self-studying Japanese, but tends to worsen when self-studying arithmetic. In this case, the analyst may recommend to learner X that he or she take breaks more frequently when self-studying arithmetic.

[0171] <Flow of analysis processing by the analysis device> Next, a description will be given of the flow of analysis processing by the analysis device 310 according to the first embodiment. Fig. 22 is an example of a flowchart showing the flow of analysis processing by the analysis device according to the first embodiment.

[0172] In step S2201, analysis device 310 acquires time-series data relating to the psychological state of the learner. Analysis device 310 also generates time-series data indicating the psychological state of the learner and stores it in collected data storage unit 611.

[0173] In step S2202, analysis device 310 acquires data relating to the learner's lessons or tests, associates the data with time-series data indicating the learner's psychological state, and stores the data in collected data storage unit 611.

[0174] In step S2203, analysis device 310 acquires data relating to educational services, associates the data with time-series data indicating the psychological state of the learner, and stores the data in collected data storage unit 611.

[0175] In step S2204, analysis device 310 acquires data relating to the learning environment or test environment, associates it with time-series data indicating the psychological state of the learner, and stores it in collected data storage unit 611.

[0176] In step S2205, analysis device 310 acquires data relating to the behavior of the learner, associates it with time-series data indicating the psychological state of the learner, and stores it in collected data storage unit 611.

[0177] In step S2206, analysis device 310 accepts a selection of items according to the purpose of analysis from the analyst.

[0178] In step S2207, the analysis device 310 extracts time series data indicating the psychological state of the learner, in which the data for the selected items are equal to each other, from the collected data storage unit 611, and generates processing unit data by performing statistical processing or segmented statistical processing.

[0179] In step S2208, the analysis device 310 outputs the generated processing unit data.

[0180] In step S2209, the analysis device 310 analyzes the generated processing unit data to determine whether there is a correlation between the time series data indicating the psychological state of the learner that has been subjected to statistical processing or segmental statistical processing and the data of the selected item.

[0181] In step S2210, analysis device 310 outputs the results of the analysis process.

[0182] In step S2211, analysis device 310 accepts the advice input by the analyst in response to outputting the results of the analysis process, and generates feedback information (for the instructor) or feedback information (for the learner).

[0183] In step S2212, the analysis device 310 transmits the generated feedback information (for the instructor) to the server device of the corresponding school building. Also, the analysis device 310 transmits the generated feedback information (for the learner) to the parent terminal of the parent of the corresponding learner.

[0184] In step S2213, analysis device 310 determines whether or not to continue the analysis process. If it is determined in step S2213 that the analysis process should be continued (NO in step S2213), the process returns to step S2206.

[0185] On the other hand, if it is determined in step S2213 that the analysis process is to be ended (YES in step S2213), the analysis process is ended.

[0186] <Summary> As is clear from the above description, the analysis device 310 according to the first embodiment: Stores time series data indicating the psychological state of the learner in a predetermined time unit. Collect data on each item related to the learner's learning behavior that corresponds to time-series data showing the learner's psychological state. From the stored time series data of a plurality of learners, time series data in which the data of the first items selected from among the items related to the learning behavior of the learners are equal to each other is extracted. Statistical processing is performed on the extracted time series data in units where the first item of data is equal to each other. · Statistical data is output in correspondence with the data of the first item.

[0187] As a result, according to the first embodiment, it is possible to provide data suitable for carrying out various analyses related to education.

[0188] [Second embodiment] The analysis device 310 according to the first embodiment analyzes whether there is a correlation between data of items according to the analysis purpose and corresponding statistical data (or category statistical data), and generates feedback information by accepting suggestions input by the analyst.

[0189] However, the generated feedback information is not limited to this. For example, the relationship between data of items according to the analysis purpose and corresponding statistical data (or category statistical data) may be visualized using a graph or the like, and the graph or the like may be transmitted as feedback information. The second embodiment will be described below, focusing on the differences from the first embodiment.

[0190] <Functional configuration of the analysis device> First, the functional configuration of an analysis device according to the second embodiment will be described. Fig. 23 is a diagram showing an example of the functional configuration of an analysis device according to the second embodiment. The difference from analysis device 310 according to the first embodiment described using Fig. 6 is that analysis device 2310 according to the second embodiment has a visualization unit 2311.

[0191] The visualization unit 2311 visualizes the relationship between the data of the items according to the analysis purpose and the corresponding statistical data (or category statistical data) output by the output unit 607 using graphs, etc. The graphs, etc. generated by the visualization unit 2311 include graphs, etc. for instructors and graphs, etc. for learners.

[0192] The visualization unit 2311 also notifies the transmission unit 610 of the generated graph etc. (for instructor) or graph etc. (for learner) as feedback information (for instructor) or feedback information (for learner).

[0193] <Examples of processing by analytical equipment> Next, a specific example of processing by the analysis device 2310 according to the second embodiment will be described. FIG. 24 is a diagram showing a specific example of processing by the analysis device according to the second embodiment. In FIG. 24, graph 2410 is a graph obtained by statistically processing time-series data showing the psychological state of a specific learner in each class by month. In graph 2410, the horizontal axis represents each month, and the vertical axis represents the time-series data showing the psychological state of a specific learner in each class, statistically processed on a monthly basis. Note that the dotted line in graph 2410 represents the average value of the time-series data showing the psychological states of all learners in all classes for all months.

[0194] Similarly, graph 2420 is a graph obtained by statistically processing time-series data showing the psychological state of a particular learner in Japanese language classes by month. In graph 2420, the horizontal axis represents each month, and the vertical axis represents the time-series data showing the psychological state of a particular learner in Japanese language classes, statistically processed by month. Note that the dotted line in graph 2420 represents the average value of the time-series data showing the psychological states of all learners in Japanese language classes for all months.

[0195] Similarly, graph 2430 is a graph obtained by statistically processing time-series data showing the psychological state of a specific learner in arithmetic class by month. In graph 2430, the horizontal axis represents each month, and the vertical axis represents the time-series data showing the psychological state of a specific learner in arithmetic class, statistically processed by month. Note that the dotted line in graph 2430 represents the average value of the time-series data showing the psychological states of all learners in arithmetic class for all months.

[0196] In this way, by generating a graph showing the relationship between data for an item according to the purpose of analysis and the corresponding statistical data, it is possible to check, for example, the transition of time-series data showing the psychological state of a particular learner.

[0197] 24, a graph is generated to enable checking of the transition of time-series data indicating the psychological state of a specific learner, but the graph generated by the visualization unit 2311 is not limited to this. For example, a graph may be generated to enable checking of the transition of time-series data indicating the psychological states of multiple learners. This allows instructors to work on improving educational services from a macro perspective, for example, by reviewing the curriculum for each grade or changing the length of class time.

[0198] <Analysis process flow by analysis device> Next, the flow of analysis processing by the analysis device 2310 according to the second embodiment will be described. Fig. 25 is an example of a flowchart showing the flow of analysis processing by the analysis device according to the second embodiment. The difference from the flowchart showing the flow of analysis processing by the analysis device according to the first embodiment shown in Fig. 22 is the processing of step S2501.

[0199] In step S2501, analysis device 2310 visualizes the relationship between data of items according to the analysis purpose and corresponding statistical data (or segmented statistical data) using a graph or the like.

[0200] <Summary> As is clear from the above description, the analysis device 2310 according to the second embodiment: Stores time series data indicating the psychological state of the learner in a predetermined time unit. Collect data on each item related to the learner's learning behavior that corresponds to time-series data showing the learner's psychological state. From the stored time series data of a plurality of learners, time series data in which the data of the first items selected from among the items related to the learning behavior of the learners are equal to each other is extracted. Statistical processing is performed on the extracted time series data in units where the first item of data is equal to each other. ·Graph the statistical data using the data from the first item.

[0201] As a result, according to the second embodiment, for example, it is possible to check the transition of time-series data indicating the psychological state of the learner.

[0202] [Other embodiments] In the first embodiment, the items related to the learner's learning behavior, the items related to educational services, the items related to the environment, and the items related to the learner's behavior are exemplified in Fig. 4A. However, the items related to the learner's learning behavior, the items related to educational services, the items related to the environment, and the items related to the learner's behavior are not limited to the items exemplified in Fig. 4A.

[0203] In the first embodiment, the "data related to the learning environment," "data related to the learner's behavior," "data related to the test environment," and "data related to the learner's behavior" shown in FIG. 4A are selected as the third or fourth item. However, suppose that the "data related to the learning environment" and the "data related to the learner's behavior" are factors that cause outliers in the time-series data indicating the learner's psychological state. In this case, these data may be used to identify outliers in the time-series data indicating the learner's psychological state.

[0204] For example, suppose a bird hits a classroom window during a lesson, causing a temporary, significant change in the time-series data indicating the learner's psychological state. Such a change is merely temporary, and will be considered noise when analyzing whether there is a correlation with the way the lesson is conducted. Therefore, in order to remove such noise, it is possible to identify outliers in the time-series data indicating the learner's psychological state using "data related to the learning environment," etc.

[0205] In the first embodiment, the contents of the feedback information (for instructor) and the feedback information (for learners) are exemplified in Fig. 4B. However, the contents of the feedback information (for instructor) and the feedback information (for learners) are not limited to the items exemplified in Fig. 4B.

[0206] In addition, in the above first embodiment, two types of feedback information are generated, one for the instructor and one for the learner, but it is also possible to generate three or more types of feedback information and send each to a different destination.

[0207] Furthermore, in the first embodiment, the analysis system 300 has been described as having one analysis device 310, but the analysis system 300 may have a plurality of analysis devices. In this case, each analysis device may be configured to independently execute an analysis program, or each analysis device may be configured to cooperate with each other to execute an analysis program.

[0208] In the second embodiment, a specific example of visualizing the relationship between data of items according to the analysis purpose and the corresponding statistical data (or categorized statistical data) is shown in Fig. 24. However, the method of visualizing the relationship between data of items according to the analysis purpose and the corresponding statistical data (or categorized statistical data) is not limited to the specific example shown in Fig. 24.

[0209] Although the above embodiments have described a scenario in which the analysis system is applied to an educational institution, the application of the analysis system is not limited to educational institutions. For example, the analysis system may be applied to situations in which qualification studies, vocational training, in-house / off-site training for working adults, reskilling, and other education-related services are provided. The analysis system of the present disclosure can provide data suitable for various analyses related to education in such scenarios.

[0210] The present invention is not limited to the configurations described in the above embodiments, but may be combined with other elements, etc. These aspects can be changed without departing from the spirit of the present invention, and can be appropriately determined depending on the application form. [Explanation of symbols]

[0211] 300: Analysis System 310:Analysis equipment 601: Data Collection Department 602: Data generation unit 603: Item selection section 604:Extraction part 605: Statistical processing unit 606: Sectional statistical processing unit 607: Output section 608:Analysis Department 609: Evaluation input section 610: Transmitter 611: Collected data storage unit 612: Processing unit data storage unit 2310:Analysis equipment 2311 :Visualization part

Claims

1. a storage unit for storing time series data indicating the psychological state of a learner in a predetermined time unit; a data collection unit that collects data on each item related to the learner's learning behavior corresponding to the time-series data; an extracting unit that extracts time-series data of a plurality of learners stored in the storage unit, the time-series data having first items selected from items related to the learning behavior of the learners that are equal to each other; a statistical processing unit that statistically processes the time series data extracted by the extraction unit in units of the first items of data that are equal to each other; an output unit that outputs the result of the statistical processing performed by the statistical processing unit in association with the data of the first item; An analysis device having:

2. the data collection unit collects data on each item related to the educational service that changes within the predetermined time unit in association with the time-series data; the extraction unit extracts, from the time-series data of the plurality of learners stored in the storage unit, time-series data in which the data of the first items are equal to each other and in which the data of second items selected from the items related to the educational service are equal to each other; The analysis device further comprises: a segmented statistical processing unit that divides the time series data extracted by the extraction unit into a plurality of time segments according to the data of the second item, and performs statistical processing for each unit of the data of the first item that is equal to each other and for each time segment unit; the output unit outputs the results of the statistical processing performed by the segmented statistical processing unit in association with the first item of data and the second item of data. The analysis device according to claim 1 .

3. The predetermined time unit is the time range of one lesson or the time range of a test for one subject. The analysis device according to claim 1 or 2.

4. Each item related to the learner's learning behavior includes the class to which the learner belongs, the school building and classroom in which the learner took the class, the subject and unit in which the learner took the class, the day of the week and time when the learner took the class, the instructor of the class in which the learner took the class, the textbook used in the class by the learner, and the teaching format of the class in which the learner took the class. The analysis device according to claim 1 .

5. Each item related to the learner's learning behavior includes any one of the test class to which the learner belongs, the school building and classroom in which the learner took the test, the subject in which the learner took the test, the day of the week and time in which the learner took the test, and the name of the test taken by the learner. The analysis device according to claim 1 .

6. Each item related to the educational service includes any one of the method of proceeding with the lesson in the predetermined time unit, the content of the lesson in the predetermined time unit, and the difficulty level of the lesson in the predetermined time unit. The analysis device according to claim 2 .

7. Each item related to the educational service includes either the content of each test question in the predetermined time unit or the difficulty level of each test question in the predetermined time unit. The analysis device according to claim 2 .

8. The data collection unit further collects data on each item related to the environment corresponding to the time-series data, the extraction unit extracts, from the time-series data of the plurality of learners stored in the storage unit, time-series data in which the data of the first items are equal to each other and the data of the third items selected from the items related to the environment are equal to each other; the statistical processing unit statistically processes the time series data extracted by the extraction unit for each unit in which the data of the first item are equal to each other and for each unit in which the data of the third item are equal to each other; the output unit outputs the result of the statistical processing performed by the statistical processing unit in association with the first item of data and the third item of data. The analysis device according to claim 1 .

9. The environmental items include the temperature in the classroom, the humidity in the classroom, the air pressure in the classroom, the lighting intensity in the classroom, the lighting color in the classroom, the carbon dioxide concentration in the classroom, the wind speed from the air conditioner in the classroom, the particle concentration in the classroom, the noise around the classroom, the smell in the classroom, the aromatherapy in the classroom, and the equipment in the classroom. The analysis device according to claim 8.

10. the extraction unit extracts time-series data for each learner; The analysis device according to claim 1 .

11. The data collection unit further collects data on each item related to the learner's behavior outside the predetermined time unit, the extraction unit extracts, from the time-series data of the specific learner stored in the storage unit, time-series data in which the data of the first item are equal to each other and in which the data of a fourth item selected from items related to the learner's behavior outside the predetermined time unit are equal to each other; the statistical processing unit statistically processes the time series data extracted by the extraction unit for each unit in which the data of the first item are equal to each other and for each unit in which the data of the fourth item are equal to each other, the output unit outputs the result of the statistical processing performed by the statistical processing unit in association with the first item of data and the fourth item of data. The analysis device according to claim 10.

12. The items related to the learner's behavior outside the predetermined time unit include any one of the learner's sleeping time, meal amount, meal timing, behavior during breaks, whether or not an event has occurred, and whether or not a nap has occurred. The analysis device according to claim 11.

13. the data collection unit collects face image data and vital data of the learner at the predetermined time intervals; The analysis device a generation unit that generates time-series data indicating the mental state of the learner based on the face image data and vital data collected by the data collection unit; The analysis device of claim 1 further comprising:

14. storing time-series data indicating the psychological state of a learner in a predetermined time unit in a storage unit; collecting data on each item related to the learner's learning behavior corresponding to the time-series data; extracting time-series data of the plurality of learners stored in the storage unit, the time-series data having first items selected from items related to the learning behavior of the learners being equal to each other; statistically processing the extracted time series data in units of the first item data that are equal to each other; outputting the statistically processed result in association with the first item of data; An analytical method performed by a computer.

15. storing time-series data indicating the psychological state of a learner in a predetermined time unit in a storage unit; collecting data on each item related to the learner's learning behavior corresponding to the time-series data; extracting time-series data of the plurality of learners stored in the storage unit, the time-series data having first items selected from items related to the learning behavior of the learners being equal to each other; statistically processing the extracted time series data in units of the first item data that are equal to each other; outputting the statistically processed result in association with the first item of data; An analysis program for running the above on a computer.

Images