Information providing device, information providing method, and information providing program

Abstract

The information providing device is provided with feedback information by performing an analysis suitable for improving educational services or learning efficiency. The information providing device has a storage unit that stores time-series data in a predetermined time unit, which is time-series data indicating the psychological state of a learner, a data collection unit that collects one or more of data of each item related to the learning behavior of the learner corresponding to the time-series data, data of each item related to educational services that change within the predetermined time unit, data of each item related to the environment, and data of each item related to the behavior of the learner outside the predetermined time unit, and an item extraction unit that extracts data items that contribute to the level or change in level of the time-series data of the predetermined time unit corresponding to a specific learner or a specific teacher from the time-series data of the predetermined time unit stored in the storage unit.

Description

[Technical field]

[0001] The present disclosure relates to an information providing device, an information providing method, and an information providing 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 their students.

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

[0004] Furthermore, various educational institutions evaluate instructors based on, for example, the results of surveys of each learner, and make appropriate staffing arrangements.

[0005] Furthermore, various educational institutions are visualizing each student's test scores for each subject and unit and providing feedback to each student. [Prior art documents] [Patent documents]

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

[0007] On the other hand, data obtained from interviews with instructors or questionnaires of individual students lack objectivity, and it is difficult to perform sufficient analysis to improve the educational services provided using only data obtained in this way. Also, in order to improve the academic ability of students, it is essential to increase learning efficiency, and for this reason, it is desirable to conduct an analysis that goes as far as to include, for example, each student's daily learning attitude.

[0008] The present disclosure realizes an information providing device, an information providing method, and an information providing program capable of providing feedback information by performing analysis suitable for improving educational services or improving learning efficiency. [Means for solving the problem]

[0009] According to one aspect, an information providing device includes: a storage unit for storing time series data indicating a psychological state of a learner in a predetermined time unit; a data collection unit that collects one or more of data on each item related to the learner's learning behavior corresponding to the time-series data, data on each item related to educational services that change within the predetermined time unit, data on each item related to the environment, and data on each item related to the learner's behavior outside the predetermined time unit; Among the time series data stored in the storage unit, a specific 1 of Learners To an item extraction unit that extracts, based on the results of the multivariate analysis, data items that contribute to the level or change in level of the time series data in a corresponding predetermined time unit, or data items that contribute to the level or change in level of the segmented time series data, which is the time series data of each data segment within the corresponding predetermined time unit; 、 The extracted item data is Data identified for the specific learner; Data identifying a learner other than the specific learner or a part of learners other than the specific learner in a specific group into which the specific learner is classified; a feedback information generating unit for generating feedback information indicating a characteristic of the psychological state of the particular one learner by comparing the has. Effect of the Invention

[0010] According to the present disclosure, it becomes possible to provide feedback information by performing analysis suitable for improving educational services or improving learning efficiency. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing an example of an educational institution to which an information providing device is applied. [Diagram 2] FIG. 2 is a diagram showing an example of time-series data relating to the mental state of a learner, which is acquired by the information providing device. [Diagram 3] FIG. 3 is a diagram illustrating an example of a system configuration of a system including an information providing device. [Figure 4A] FIG. 4A is a diagram showing a specific example of collected data. [Figure 4B] FIG. 4B is a diagram showing a specific example of feedback information. [Diagram 5] FIG. 5 is a diagram illustrating an example of a hardware configuration of the information providing device. [Figure 6] FIG. 6 is a diagram illustrating an example of a functional configuration of the information providing device. [Figure 7] FIG. 7 shows a specific example of groups into which learners are classified. [Figure 8] FIG. 8 is a diagram showing a specific example of groups into which teacher instructors are classified. [Figure 9] FIG. 9 is a diagram showing an example of processing unit data for one particular learner. [Figure 10] FIG. 10 is a diagram showing an example of processing unit data of a learner classified into a specific group. [Figure 11] FIG. 11 is a diagram showing an example of the processing unit data of all learners. [Figure 12] FIG. 12 is a diagram showing an example of processing unit data for one particular teacher of a class. [Figure 13] FIG. 13 is a diagram showing an example of processing unit data of a teacher who is classified into a specific group. [Figure 14] FIG. 14 is a diagram showing an example of processing unit data for all teaching staff. [Figure 15] FIG. 15 is a diagram showing a first specific example of a process for generating feedback information (for a learner). [Figure 16] FIG. 16 is a diagram showing a second specific example of the process of generating feedback information (for a learner). [Figure 17] FIG. 17 is a diagram showing a third specific example of the process of generating feedback information (for learners). [Figure 18] FIG. 18 is a diagram showing a first specific example of a process for generating feedback information (for instructors). [Figure 19] FIG. 19 is a diagram showing a second specific example of the process for generating feedback information (for instructors). [Figure 20] FIG. 20 is a first flowchart showing the flow of the information provision process. [Figure 21] FIG. 21 is a second flowchart showing the flow of the information provision process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

[0014] As shown in Fig. 1, the information providing device is applied to, for example, a large-scale cram school. A large-scale cram school refers to a cram school that has school buildings in multiple areas and each school building has multiple classrooms.

[0015] The example in Fig. 1 shows one school building (school building 110) in a given area. The school building 110 has ten classrooms, and in classroom 120, for example, a teacher 121 is teaching a class to a number of students 122_1, 122_2, ..., using a given textbook.

[0016] 1, a manager 131 who manages the entire school building 110 is present in a teacher's room 130 in the school building 110. The manager 131 takes on various initiatives to improve the educational services provided in the school building 110, and to improve the academic ability and learning efficiency of the learners studying in the school building 110. In this embodiment, the manager 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, the parents of the learners studying at the school building 110 are waiting to pick up or drop off the learners.

[0018] The information providing device acquires time series data relating to the psychological state of each learner during a lesson or test, the time series data being for a predetermined time unit (for each lesson or for each test of each subject). The information providing device also analyzes the acquired time series data for each lesson or for each test of each subject, and generates feedback information useful for efforts to improve educational services or improve the learning efficiency of learners, based on the results of the analysis process. Furthermore, the information providing device transmits the generated feedback information to the learner, guardian, or instructor.

[0019] In the first embodiment, a case is described in which the information providing device is applied to a large cram school. However, the educational institution to which the information providing device is applied is not limited to a large cram school, and may also be a small cram school.

[0020] In the first embodiment, the teaching style of the educational institution to which the information providing device is applied is a group teaching style in which a plurality of students receive a lesson from one teacher, but the teaching style is not limited to a group teaching style and may be an individual teaching style. Alternatively, the teaching style may be a self-study style instead of a class style.

[0021] In addition, in the first embodiment, a case will be described in which the teaching method of the educational institution to which the information providing device 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 information providing device> Next, an example of time series data related to the psychological state of a learner acquired by the information providing device in an educational institution to which the information providing device is applied will be described. Fig. 2 is a diagram showing an example of time series data related to the psychological state of a learner acquired by the information providing device.

[0023] The example of Fig. 2 shows how face image data and vital data are acquired as time-series data related 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 photographing the learner 122_1 with 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 photographing the learner 122_1 with 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 includes, for example, pulse, blood pressure, respiration, body temperature, etc.

[0025] The information providing device generates time series data showing the mental state of the learner 122_1 based on the acquired face image data and vital data. The mental state of the learner 122_1 includes emotions such as joy, anger, sorrow, and happiness, concentration / distraction, emotion classification based on the Russell Circumplex model, emotion classification based on the Plutchik theory, emotion, stress, tension, fatigue, arousal, drowsiness, relaxation, and excitement. The example of FIG. 2 shows that the time series data generated by the information providing device 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 showing the mental state of the learner 122_1 generated by the information providing device is not limited to the two-dimensional data, and may be two-dimensional data defined based on an axis different from the axis shown in FIG. 2. Alternatively, the time series data showing the mental state of the learner 122_1 generated by the information providing device is not limited to two-dimensional data, and may be one-dimensional data or multidimensional data of three or more dimensions.

[0026] However, in the following, regardless of the dimensional data, when a learner is in a different psychological state, the time series data indicating the learner's psychological state is expressed as having different levels, or the time series data indicating the learner's psychological state is expressed as having different levels. In this case, one of the learners' psychological states is expressed as having a high level of time series data indicating the psychological state, and the other of the learners' psychological states is expressed as having a low level of time series data indicating the psychological state. Note that the one and the other psychological states referred to here may be, for example, the learner's psychological state at a first time and the learner's psychological state at a second time (for example, a psychological state at a time earlier than the first time).

[0027] Alternatively, the one psychological state and the other psychological state may be the average psychological state of the learner and the psychological state of the learner at a given time. Note that the average psychological state of the learner may be an average value of the psychological states of the learner in a given period of time, or may be a mode of the psychological states of the learner in a given period of time.

[0028] Alternatively, the one psychological state and the other psychological state may be a reference value of the learner's psychological state and the learner's psychological state at a given time, that is, the level of the time-series data indicating the learner's psychological state at a given time may be expressed as high or low based on a comparison with the reference value.

[0029] <System configuration of a system including an information providing device> Next, a system configuration of a system including an information providing device will be described with reference to Fig. 3. As shown in Fig. 3, the system configuration of a system including an information providing device is illustrated.

[0030] 3, the system 300 including the information providing device 310 has, in addition to the information providing device 310, a school building I server device 320, a school building I parent terminal 330, etc. In the system 300, the information providing device 310, the school building I server device 320, the school building I parent terminal 330, etc. are connected to each other via a network 340 so as to be able to communicate with each other.

[0031] The school I server device 320 is, for example, a server device owned by the school 110 (school name: school I). The school 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 information providing device 310.

[0032] In addition, the school I server device 320 receives feedback information (for instructors) transmitted from the information providing device 310 in response to transmitting the collected data to the information providing device 310. As a result, the instructor of the school 110 having the school 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 at School Building 110; Various initiatives can be undertaken to achieve this.

[0033] The collected data (class) transmitted by the school I server device 320 to the information providing device 310 includes, for example, -Data on each item 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 (a specified time unit); Data on each item related to educational services that changes for each data category within the time range of one lesson (within a specified time unit); Data on each item 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 each item of learner behavior that is outside the time range of one lesson (outside the specified time unit), Includes:

[0034] In addition, the collected data (test) transmitted by the school I server device 320 to the information providing device 310 includes, for example, Data on each item related to a learner's test, including the time range (specified time unit) of one test subject; -Time series data related to the psychological state of learners, which is time series data for the time range (predetermined time unit) of a test for one subject; Data on each item related to educational services that changes within the time range (within a specified time unit) of a test for one subject; Data on each item related to the test environment, including data on the time range (predetermined time unit) of a test for one subject or time series data on the time range (predetermined time unit) of a test for one subject; Data on each item of learner behavior that is outside the time range of a test for one subject (outside the specified time unit), Includes:

[0035] The school I server device 320 transmits collected data (class) to the information providing device 310 each time a corresponding class ends, or each time a predetermined time or number of days has passed. Similarly, the school I server device 320 transmits collected data (test) to the information providing device 310 each time a corresponding test for one subject ends, or each time a predetermined time or number of days has passed.

[0036] The information providing device 310 receives collected data (lessons) and collected data (tests) transmitted from server devices in a plurality of school buildings, including the school building I server device 320.

[0037] In the first embodiment, data on each item related to the learner's lessons and data on each item related to the learner's tests are collectively referred to as "data on each item related to the learner's learning behavior." Also, in the first embodiment, data on each item related to the learning environment and data on each item related to the test environment are collectively referred to as "data on each item related to the environment."

[0038] In addition, the information providing device 310 generates time series data indicating the mental state of the learner from the time series data related to the mental state of the learner contained in the received collected data (lesson) and collected data (test). Next, the information providing device 310 converts the generated time series data indicating the mental state of the learner into -Data on each item related to the learner's learning behavior, -Data on various items related to educational services; -Data on each item related to the environment, -Data on each item of learner behavior, The data is stored in correspondence with the above.

[0039] In addition, the information providing device 310 extracts data items from the stored time series data indicating the psychological state of learners that contribute to the level or change in level of the time series data of a specific learner or a learner who has taken a class taught by a specific instructor.

[0040] Furthermore, the information providing device 310 generates feedback information (for the instructor) or feedback information (for the learner) based on the data of the extracted items.

[0041] Furthermore, the information providing device 310 transmits feedback information (for the instructor) to the school I server device 320 and the like, and transmits feedback information (for the learner) to the school I parent terminal 330 and the like.

[0042] The school building I parent terminal 330 is, for example, a terminal owned by a parent of a learner studying at the school building 110 (school building name: school building I). For example, when a parent inputs data for each item related to the learner's behavior, the school building I parent terminal 330 accepts the data and transmits it to the school building I server device 320.

[0043] In addition, the parent terminal 330 of the school I receives feedback information (for the learner) from the information providing device 310. Based on the feedback information (for the learner), the parent can: -Improvement of learner's learning efficiency, Various initiatives can be undertaken to achieve this.

[0044] <Examples of collected data> Next, a description will be given of specific examples of the collected data (lesson) and the collected data (test) received by the information providing device 310. Fig. 4A is a diagram showing a specific example of the collected data.

[0045] (1) Collected data (classes) As shown in Figure 4A, the "data related to students' classes" in the collected data (classes) include: - Learner's "Student number", "Name", "Age", "Gender", "Temperament", - The "class", "school building" or "classroom" in which the learner took the class; -The "subjects" and "units" of the lessons taken by the learner, -The "day of the week", "start and end times" of the lesson that the student took, - The "teacher" of the class the learner took (including data on impressions); - The type of textbook used in the class the learner took; - The "class format" of the class the learner took, The items of data for each item of "learner's lesson-related data" are automatically received by the information providing device 310 from, for example, the school I server device 320 every time one lesson ends.

[0046] In addition, the "class format" referred to here includes, for example, - Individual lessons or group lessons? - Will the lessons be delivered via video or face-to-face? -If lessons are delivered via video, will they be delivered in real time or will they be delivered by playing back a recorded video? Classes or self-study? The data is set in advance before the lesson and includes the above information.

[0047] In addition, as shown in Figure 4A, the "time series data related to the mental state of the learners" in the collected data (classes) includes the following: -Facial image data of the learner during class; - The learner's "vital data" during class; The time series data for each item of the "time series data related to the mental state of the learner" is automatically received by the information providing device 310 from, for example, the school I server device 320 every time a lesson is completed (i.e., for each lesson).

[0048] In addition, as shown in Figure 4A, the "data related to educational services" in the collected data (classes) includes the following: "How to conduct classes" "Course Content", "Difficulty", The data on "class progress" is data showing the flow of a class, such as lectures, breaks, quizzes, and chats. For example, the instructor -When did you take a break (break timing)? How long the break was? How long should the consecutive lectures be? Did you include a quiz for confirmation? When did you include a quiz for confirmation? -How long the review quiz was (percentage of practice) When did you start chatting? How much small talk was included (ratio of main topic to small talk) Content of the chat, -Voice volume and speaking speed during lectures, The data on "how to proceed with the lesson" is input by the teacher in charge of the lesson to the school I server device 320, for example, every time a lesson ends, and is received by the information providing device 310.

[0049] "Class content" data is data that indicates the assignment of each unit in a class, and the instructor, for example, -Which unit did you teach? - How long and how did you order each unit? The data on the "lesson contents" is input by the instructor to the school I server device 320, for example, every time a lesson ends, and the information providing device 310 receives the data.

[0050] "Difficulty" data refers to, for example, the difficulty of one lesson. -Difficulty level of each unit, The data on "difficulty" is specified. The data on "difficulty" is input by the teacher in charge of the lesson, for example, to the school I server device 320 every time a lesson is completed, and is received by the information providing device 310.

[0051] However, the "difficulty" data is not limited to data determined in advance for each content and input by the instructor. For example, it may be data determined based on time series data indicating the mental state of the learner. Determining the difficulty level based on time series data indicating the mental state of the learner means, for example, determining that the corresponding content is difficult when it is determined from the time series data indicating the mental state of the learner that the learner is concentrating. Also, it means determining that the corresponding content is easy when it is determined from the time series data indicating the mental state of the learner that the learner is not concentrating.

[0052] Alternatively, the "difficulty" data may be data determined based on the response time of the learner. Determining the difficulty based on the response time of the learner means, for example, determining that the corresponding content has a high level of difficulty when the learner's response time is long, and determining that the corresponding content has a low level of difficulty when the learner's response time is short.

[0053] When the level of difficulty is determined based on time-series data indicating the mental state of the learner or based on the response time of the learner, the time-series data or the response time is measured in advance.

[0054] In addition, as shown in Figure 4A, the "data related to the learning environment" of the collected data (classes) includes the following: - The "room temperature", "humidity", "air pressure", "lighting intensity", "lighting color", "carbon dioxide concentration", "wind speed" (wind speed from the air conditioner), "particulate matter 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 they used "aroma therapy," - Equipment used by the learner during class (materials of desks and chairs, height settings, specifications of learning devices, presence or absence of writing utensils), The items of data for each item of "data related to the learning environment" are included, and are received by the information providing device 310 when the instructor inputs the data into, for example, the school I server device 320 each time a lesson is completed. Alternatively, data that can be measured by a measuring device among the items of data for "data related to the learning environment" is measured by the measuring device, and is automatically received by the information providing device 310, for example, from the school I server device 320 each time a lesson is completed (i.e., on a lesson-by-lesson basis).

[0055] In addition, as shown in Figure 4A, the "data on learner behavior" in the collected data (classes) includes the following: - The "hours of sleep" on the day before the student attended classes, the "amount of food" and "meal timing" on the day the student attended classes, entered by the student'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 learner's everyday life, such as the illness or death of an acquaintance, 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 the "data on the learner's behavior" is input by the learner or the learner's guardian to, for example, the school I server device 320 at a predetermined timing before one lesson starts or after one lesson ends, and is received by the information providing device 310.

[0056] (2) Collected data (test) As shown in Figure 4A, the "data related to learners' tests" in the collected data (tests) includes: - Learner's "Student number", "Name", "Age", "Gender", "Temperament", - The "class", "school building" or "classroom" in which 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 in the subject taken by the learner, The data for each item of "data related to the test of the learner" is automatically received by the information providing device 310 from, for example, the school I server device 320 every time a test for one subject is completed.

[0057] In addition, as shown in Figure 4A, the "time series data related to the learner's psychological state" in 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 items of data for each item of the "time-series data related to the mental state of the learner" are automatically received by the information providing 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).

[0058] In addition, as shown in Figure 4A, the "data related to educational services" in the collected data (test) includes Test content, Difficulty level, The data for "test content" refers to the unit of each question in a test for one subject. The data for "difficulty" refers to the difficulty of each question in a test for one subject.

[0059] In addition, as shown in Figure 4A, the "Test environment data" in the collected data (test) includes the following: -The "room temperature", "humidity", "air pressure", "lighting intensity", "lighting color", "carbon dioxide concentration", "wind speed" (wind speed from air conditioner), "particulate matter 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 in which the learner took the test for one subject, and whether or not "aroma therapy" was used, - Equipment used by the learner during the test (materials of desks and chairs, height settings, presence or absence of writing utensils), etc. The data for each item of "data related to the test environment" is received by the information providing device 310 when the tester inputs the data into, for example, the school I server device 320 each time a test for one subject is completed. Alternatively, among the data for each item of "data related to the test environment", data 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 information providing device 310, for example, from the school I server device 320 each time a test for one subject is completed (i.e., for each test for one subject).

[0060] In addition, as shown in Figure 4A, the "data on learner behavior" in the collected data (test) includes the following: - The "hours of sleep" on the day before the student took the test, the "amount of food" and "meal timing" on the day the student took the test, entered by the student's guardian; - The "break behavior" between tests for each subject of the learner, entered by the learner; -Whether or not there were any notable "events" that occurred before the date the learner took the test, as entered by the learner (events that were out of the learner's everyday life, such as poor health, the death of an acquaintance, or a broken heart), -Whether or not the learner took a nap immediately before the test, as entered by the learner The data for each item of the "data on the learner's behavior" is input by the learner or the learner's guardian to, for example, the school I server device 320 before the test starts or at a predetermined timing after the test ends, and is received by the information providing device 310.

[0061] <Examples of feedback information> Next, a description will be given of specific examples of feedback information (for learners) and feedback information (for instructors) transmitted by the information providing device 310. Fig. 4B is a diagram showing a specific example of feedback information.

[0062] (1) Feedback information (for learners) The feedback information (for learners) is useful information for taking various measures to improve the learning efficiency of the learners. For example, as shown in FIG. 4B, - the characteristics of the psychological state of a particular learner; It should be noted that the above feedback information (for learners) is merely an example.

[0063] (2) Feedback information (for instructors) The feedback information (for instructors) is useful information for carrying out various efforts to improve educational services or improve the learning efficiency of learners. For example, as shown in FIG. 4B, -Improvement information for improving the psychological state of students taking classes taught by a particular instructor; The above feedback information (for instructors) is just one example.

[0064] <Hardware configuration of information providing device> Next, a hardware configuration of the information providing device 310 will be described. Fig. 5 is a diagram showing an example of the hardware configuration of the information providing device. As shown in Fig. 5, the information providing 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 information providing device 310 are connected to each other via a bus 507.

[0065] 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 information provision program, etc.) onto the memory 502 and executes them.

[0066] 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 processor 501 executes various programs read onto the memory 502, whereby the computer realizes various functions.

[0067] The auxiliary storage device 503 stores various programs and various data used when the processor 501 executes the various programs.

[0068] The I / F device 504 accepts operations of an analyst (not shown) on the information providing device 310 via an operation device 511. In addition, the I / F device 504 outputs results of information provision processing by the information providing device 310, and displays them to the analyst via a display device 512.

[0069] 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.

[0070] The drive device 506 is a device for setting the 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 ROM, flash memory, etc.

[0071] 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 out the various programs recorded in 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 the network 340 via the communication device 505.

[0072] <Functional configuration of the information providing device> Next, the functional configuration of the information providing device 310 will be described. Fig. 6 is a diagram showing an example of the functional configuration of the information providing device. As described above, an information providing program is installed in the information providing device 310, and by executing the program, the information providing device 310 performs the following functions as shown in Fig. 6: Data collection unit 601, data generation unit 602, Selection unit 603, extraction unit 604, Statistical processing unit 605, classification statistical processing unit 606, ·Item extraction unit 607, A subject data calculation unit 608, a tendency data calculation unit 609, Feedback information generating unit 610, transmitting unit 611, It functions as:

[0073] The data collection unit 601 receives, for example, collected data (lesson) and collected data (test) transmitted from the school I server device 320, etc. The data collection unit 601 notifies the data generation unit 602 of the received collected data (lesson) and collected data (test).

[0074] The data generating unit 602 generates time series data showing the mental state of the learner based on the time series data (face image data, vital data) related to the mental state of the learner included in the notified collected data (lesson). The data generating unit 602 also generates time series data showing the mental state of the learner based on the time series data (face image data, vital data) related to the mental state of the learner included in the notified collected data (test).

[0075] 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 each item related to the environment, -Data on each item of learner behavior, The collected data is stored in the collected data storage unit 621 in association with the above.

[0076] The selection unit 603 accepts a specific learner or a specific teacher selected by the analyst from among the various items.

[0077] A specific learner refers to a specific learner, or a learner classified into a specific group or a part of learners classified into a specific group. A learner classified into a specific group or a part of learners classified into a specific group is a learner or a part of learners classified into a specific group among multiple groups defined based on differences in learner attributes, including personality and temperament.

[0078] Here, "student attributes" refers to information that includes elements for distinguishing one particular learner from other learners, and may include information such as personality and temperament, as well as gender, age (grade), school attended, liberal arts or science choice, preferred school, grades (deviation score), etc. In other words, "student attributes" are not necessarily limited to "data related to the learner's classes" or "data related to the learner's tests."

[0079] However, in this embodiment, the "attributes of the learner" are extracted from "data related to the learner's lessons" or "data related to the learner's tests." Specifically, a specific learner is selected by, for example, specifying specific data for "student number" or "name" among the data items related to the learner's learning behavior. Also, a learner classified into a specific group or a part of learners classified into a specific group is selected by, for example, specifying specific data for "temperament" among the data items related to the learning behavior. Hereinafter, a learner classified into a specific group or a part of learners classified into a specific group will be simply referred to as a learner classified into a specific group.

[0080] Similarly, a specific instructor refers to one specific instructor, or instructors classified into a specific group or a part of instructors classified into a specific group. An instructor classified into a specific group or a part of instructors classified into a specific group is an instructor classified into a specific group or a part of instructors classified into a specific group among multiple groups defined based on differences in instructor attributes including impressions.

[0081] Here, "attributes of a teacher" refers to information including elements for distinguishing one particular teacher from other teachers, and may include information such as impressions, gender, age (years of service), school attended, subjects taught, etc. In other words, "attributes of a teacher" are not necessarily limited to data in the "teacher" field of "data related to learner's classes."

[0082] However, in this embodiment, a particular one instructor is selected, for example, by specifying specific data related to the name of the "instructor" among the data items related to the learner's classes. Also, an instructor classified into a particular group or some of the instructors classified into a particular group are selected, for example, by specifying specific data related to the impression of the "instructor" among the data items related to the learner's classes. In the following, an instructor classified into a particular group or some of the instructors classified into a particular group will be simply referred to as an instructor classified into a particular group.

[0083] The selection unit 603 notifies the extraction unit 604 of the selected specific learner or the selected specific teacher.

[0084] The extraction unit 604 extracts time series data indicating the psychological states of a learner that is associated with a specific learner or a specific instructor notified by the selection unit 603 from the time series data indicating the psychological states of multiple learners stored in the collected data storage unit 621.

[0085] 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, calculating the minimum value, calculating the average value, calculating the median value, calculating the standard deviation, and the like.

[0086] In addition, 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 622 as processing unit data in correspondence with the data of each item used in the statistical processing.

[0087] The sectional statistical processing unit 606 performs sectional statistical processing on the time series data indicating the psychological state of the learner extracted by the extraction unit 604. The sectional statistical processing refers to dividing the time series data indicating the psychological state of the learner into a plurality of data segments according to the data of each item related to the educational service, and performing statistical processing for each data segment.

[0088] In addition, the time series data (segment statistical data) indicating the psychological state of the learner, which has been subjected to segment statistical processing by the segment statistical processing unit 606, is stored in the processing unit data storage unit 622 as processing unit data in correspondence with the data of each item used in the segment statistical processing.

[0089] The item extraction unit 607 reads out the statistical data and the data of each item used in the statistical processing from the processing unit data storage unit 622, and performs, for example, multivariate analysis. The item extraction unit 607 extracts data items that contribute to the level of the statistical data or to changes in the level based on the results of the multivariate analysis.

[0090] The item extraction unit 607 also reads out the categorized statistical data and the data of each item used in the categorized statistical processing from the processing unit data storage unit 622, and performs, for example, multivariate analysis. The item extraction unit 607 extracts data items that contribute to the level of the categorized statistical data or the change in level based on the results of the multivariate analysis.

[0091] When the subject is a specific learner, the subject data calculation unit 608 calculates from the processing unit data storage unit 622 the statistical data or the segment statistical data of the specific learner, Statistical data corresponding to the data of the item notified by the item extraction unit 607, or Category statistical data corresponding to the data of the items notified by the item extraction unit 607, is read out, and for each item, for example, multivariate analysis is performed.

[0092] In this way, the subject data calculation section 608 can identify data that contributes to the level or change in level of the statistical data or category statistical data for each item for one specific learner.

[0093] Furthermore, when the subject is a specific teacher, the subject data calculation unit 608 reads out data of the items notified by the item extraction unit 607 for the specific teacher from the processing unit data storage unit 622. The subject data calculation unit 608 also calculates the average value (or data with a high frequency of occurrence) of the data for each of the read items. This allows the subject data calculation unit 608 to calculate the average value (or data with a high frequency of occurrence) of the data for each of the items for the specific teacher.

[0094] In addition, the subject data calculation unit 608 Data specific to one particular learner, or -The average value (or data with high frequency of occurrence) calculated for a specific instructor The feedback information generating unit 610 is notified of the above as “subject data”.

[0095] The tendency data calculation unit 609 obtains from the processing unit data storage unit 622 the statistical data of learners other than the specific learner or the classification statistical data of the specific group into which the specific learner is classified, Statistical data corresponding to the data of the item notified by the item extraction unit 607, or Category statistical data corresponding to the data of the items notified by the item extraction unit 607, is read out, and for each item, for example, multivariate analysis is performed.

[0096] This enables the trend data calculation unit 609 to identify data that contributes to the level or change in level of statistical data or category statistical data for each item for a specific group into which a specific learner is classified.

[0097] The tendency data calculation unit 609 also receives from the processing unit data storage unit 622 the statistical data of all learners other than the specific learner or the segment statistical data, Statistical data corresponding to the data of the items notified by the item extraction unit 607, or Category statistical data corresponding to the data of the items notified by the item extraction unit 607, is read out, and for each item, for example, multivariate analysis is performed.

[0098] In this way, the tendency data calculation section 609 can identify data that contributes to the level or change in level of the statistical data or category statistical data for each item for all learners other than the one particular learner.

[0099] Furthermore, the tendency data calculation unit 609 judges which of the instructors other than the specific instructor in the specific group into which the specific instructor is classified has a high level of statistical data or category statistical data of the learners who attended the class. The tendency data calculation unit 609 reads out the data of the items notified by the item extraction unit 607 from the processing unit data storage unit 622 among the data of each item of the judged instructor, and calculates the average value (or data with a high frequency of occurrence) of the data of each item.

[0100] As a result, the tendency data calculation unit 609 calculates the number of teachers other than the specific teacher in the specific group into which the specific teacher is classified. -Teachers with high-level statistical data on students who have attended their classes, or -Teachers with high level of statistical data on the students who attended the class; For each item, the average data value (or the most frequently occurring data) can be calculated.

[0101] Furthermore, the tendency data calculation unit 609 judges, among the instructors other than the specific instructor, an instructor who has a high level of statistical data or a high level of category statistical data of learners who attended the class. The tendency data calculation unit 609 reads out the data of the items notified by the item extraction unit 607 from the processing unit data storage unit 622 among the data of each item of the judged instructor, and calculates the average value (or data with a high occurrence frequency) of the data of each item.

[0102] As a result, the tendency data calculation unit 609 calculates the number of teachers other than the specific teacher. -Teachers with high-level statistical data on students who have attended their classes, or -Teachers with high level of statistical data on the students who attended the class; For each item, the average data value (or the most frequently occurring data) can be calculated.

[0103] The tendency data calculation unit 609 Data identifying learners other than the particular learner or all learners other than the particular learner in the particular group into which the particular learner has been classified; or The average value (or data with high frequency of occurrence) calculated for a portion of the instructors other than the instructor of the specific class in the group into which the instructor of the specific class was classified, or for a portion of the instructors other than the instructor of the specific class, The feedback information generating unit 610 is notified of this as “tendency data”.

[0104] The feedback information generating unit 610 compares the subject data notified from the subject data calculating unit 608 with the trend data notified from the trend data calculating unit 609. In this way, the feedback information generating unit 610 generates feedback information (for the learner) indicating the characteristics of the psychological state of a specific learner.

[0105] Specifically, when the feedback information generation unit 610 is notified of subject data based on a specific learner from the subject data calculation unit 608 and of corresponding trend data from the trend data calculation unit 609, it generates feedback information (for the learner).

[0106] Furthermore, the feedback information generating unit 610 compares the subject data notified from the subject data calculating unit 608 with the tendency data notified from the tendency data calculating unit 609. In this way, the feedback information generating unit 610 generates feedback information (for the instructor) for improving the psychological state of learners attending a class taught by a specific instructor.

[0107] Specifically, when the subject data calculation unit 608 notifies the feedback information generation unit 610 of subject data based on a specific teacher and the trend data calculation unit 609 notifies the feedback information generation unit 610 of corresponding trend data, the feedback information generation unit 610 generates feedback information (for the instructor).

[0108] When the feedback information (for the learner) is notified from the feedback information generation unit 610, the transmission unit 611 transmits the notified feedback information (for the learner) to the school I parent terminal 330. Also, when the feedback information (for the instructor) is notified from the feedback information generation unit 610, the transmission unit 611 transmits the notified feedback information (for the instructor) to, for example, the school I server device 320.

[0109] <Specific examples of groups into which learners are classified> Next, the groups into which learners are classified will be described. As described above, in this embodiment, each learner is classified into one of a plurality of groups defined based on differences in temperament or personality. Fig. 7 is a diagram showing a specific example of groups into which learners are classified.

[0110] As shown in reference numeral 710, human temperament can be classified into groups based on five factors, for example, "creativity," "extroversion," "conscientiousness," "agreeableness," and "emotionality." Reference numeral 711 shows how people are classified into X groups, from temperament type Tm1 to temperament type TmX, based on a combination of diagnostic results for each of the five factors.

[0111] Also, as shown by reference numeral 720, human personality can be classified into groups based on, for example, four factors: "extrovert / introvert," "sensing / intuition," "thinking / feeling," and "judging / recognizing." Reference numeral 721 shows how people are classified into 16 groups, from personality type P1 to personality type P16, based on which of the diagnostic results for each of the four factors they fall into.

[0112] The "temperament" included in the data related to the learning behavior of the learner described using Figure 4A stores the result of diagnosing the learner in question based on the five factors shown in reference numeral 710 and determining which of the groups shown in reference numeral 711 the learner falls into based on the diagnosis.

[0113] Alternatively, the "temperament" included in the data related to the learning behavior of the learner described using Figure 4A stores the result of diagnosing the learner in question with respect to the four factors shown in reference numeral 720 and determining which of the groups shown in reference numeral 721 the learner falls into based on the diagnosis.

[0114] The groupings shown by the reference numerals 711 and 721 are merely examples, and groupings different from either the reference numerals 711 or 721 may be performed by diagnosing other factors.

[0115] <Specific examples of groups into which instructors are classified> Next, the groups into which the teacher is classified will be described. As described above, in this embodiment, each teacher is classified into one of a plurality of groups defined based on differences in impressions. Fig. 8 is a diagram showing a specific example of the groups into which the teacher is classified.

[0116] As indicated by reference numeral 810, human impressions can be grouped based on factors such as "appearance", "figure", "voice quality", "gender", "age", etc. Reference numeral 811 shows how impressions are divided into Y groups, from impression type I1 to impression type IY, based on a combination of the judgment results of each of the four factors.

[0117] In addition, the terms "appearance", "figure", and "voice quality" mentioned here are concepts that include "facial expression", "gestures", "attitude", "way of speaking", etc. For example, the impression that a person gives to learners that they are timid is due to their appearance and voice quality, specifically facial expressions such as wandering eyes, eye gestures such as not making eye contact with the learner, a high-pitched voice, and a fast way of speaking.

[0118] Moreover, the "age" referred to here may include the age of the instructor (absolute value) as well as the age difference (relative value) between the instructor and the student.

[0119] The "instructor" included in the data related to the learner's lessons explained with reference to Fig. 4A stores data other than the name of the instructor. Specifically, the "instructor" stores the result of judging the instructor based on the four factors shown in reference numeral 810 and judging which of the groups shown in reference numeral 811 the instructor belongs to based on the judgment result.

[0120] The grouping shown in reference numeral 811 is an example, and grouping different from reference numeral 811 may be performed by diagnosing other factors.

[0121] <Examples of processing unit data> Next, a specific example of processing unit data stored in the processing unit data storage section 622 by executing the information providing device 310 will be described.

[0122] (1) A specific example of processing unit data for one particular learner When a specific learner is selected by the analyst, the information providing device 310 generates processing unit data for the selected specific learner and stores the processing unit data in the processing unit data storage section 622. Fig. 9 is a diagram showing an example of the processing unit data for the specific learner.

[0123] When a specific learner is selected by the analyst, all the time series data indicating the psychological states associated with the student number or name of the selected specific learner are read out from the collected data storage unit 621. In the processing unit data 900 in Fig. 9, each data stored in the "time series data indicating psychological states" is time series data indicating all the psychological states of the specific learner when he or she took each class.

[0124] In addition, "data related to the learner's classes," "data related to educational services," "data related to the learning environment," and "data related to the learner's behavior," which are associated with the time-series data indicating the psychological state, are also read out from the collected data storage unit 621.

[0125] The read-out time-series data indicating the psychological state is subjected to statistical processing for each class, and statistical data for each class is calculated. In addition, the read-out time-series data indicating the psychological state is divided into a plurality of data segments according to the data of each item included in the "data related to educational services", and classified statistical processing is performed to calculate classified statistical data for each class.

[0126] For the sake of simplicity, the processing unit data 900 in FIG. 9 shows only one type of sectional statistical data for one class, but the sectional statistical data is calculated for one class for the number of items included in the “data related to educational services.”

[0127] (2) Specific examples of processing unit data for learners classified into specific groups When a specific learner is selected by the analyst, the information providing device 310 generates processing unit data of learners other than the specific learner who are classified into the specific group into which the selected specific learner is classified, and stores the data in the processing unit data storage section 622. Fig. 10 is a diagram showing an example of processing unit data of a learner classified into a specific group.

[0128] When a specific learner is selected by the analyst, all the time series data indicating the psychological states of learners other than the specific learner who are classified into the specific group into which the selected specific learner is classified is read out from the collected data storage unit 621. In the processing unit data 1000 of Fig. 10, each data stored in the "time series data indicating psychological states" is time series data indicating the psychological states of learners other than the specific learner who are classified into the specific group into which the selected specific learner is classified, when they took each class.

[0129] In addition, "data related to the learner's classes," "data related to educational services," "data related to the learning environment," and "data related to the learner's behavior," which are associated with the time-series data indicating the psychological state, are also read out from the collected data storage unit 621.

[0130] The read-out time-series data indicating the psychological state is subjected to statistical processing for each class, and statistical data for each class is calculated. In addition, the read-out time-series data indicating the psychological state is divided into a plurality of data segments according to the data of each item included in the "data related to educational services", and classified statistical processing is performed to calculate classified statistical data for each class.

[0131] For the sake of simplicity, the processing unit data 1000 in FIG. 10 shows only one type of sectional statistical data for one class, but the sectional statistical data is calculated for one class for the number of items included in the “data related to educational services.”

[0132] (3) Specific examples of processing unit data for all learners When one specific learner is selected by the analyst, the information providing device 310 generates processing unit data for all learners other than the selected specific learner, and stores the processing unit data in the processing unit data storage section 622. Fig. 11 is a diagram showing an example of the processing unit data for all learners.

[0133] When a specific learner is selected by the analyst, all the time series data indicating the psychological states of all learners other than the specific selected learner are read out from the collected data storage unit 621. In the processing unit data 1100 in Fig. 11, each data stored in the "time series data indicating psychological states" is time series data indicating all the psychological states of learners other than the specific selected learner when they took each class.

[0134] In addition, "data related to the learner's classes," "data related to educational services," "data related to the learning environment," and "data related to the learner's behavior," which are associated with the time-series data indicating the psychological state, are also read out from the collected data storage unit 621.

[0135] The read-out time-series data indicating the psychological state is subjected to statistical processing for each class, and statistical data for each class is calculated. In addition, the read-out time-series data indicating the psychological state is divided into a plurality of data segments according to the data of each item included in the "data related to educational services", and classified statistical processing is performed to calculate classified statistical data for each class.

[0136] For the sake of simplicity, the processing unit data 1100 in FIG. 11 shows only one type of sectional statistical data for one class, but the sectional statistical data is calculated for one class for the number of items included in the "data related to educational services."

[0137] (4) Specific example of processing unit data for a specific instructor When the analyst selects one particular teacher, the information providing device 310 generates processing unit data of the selected one particular teacher and stores it in the processing unit data storage section 622. Fig. 12 is a diagram showing an example of the processing unit data of the one particular teacher.

[0138] When the analyst selects one particular instructor, all the time series data indicating psychological states associated with the name of the selected one particular instructor are read out from the collected data storage unit 621. In the processing unit data 1200 in Fig. 11, each data stored in the "time series data indicating psychological states" is time series data indicating the psychological states of each lesson of each learner who attended the lesson taught by the one particular instructor.

[0139] In addition, from the collected data storage unit 621, "data related to the learner's lessons" and "data related to educational services" that are associated with the time-series data indicating the psychological state are also read out.

[0140] The read-out time-series data indicating the psychological state is subjected to statistical processing for each class, and statistical data for each class is calculated. In addition, the read-out time-series data indicating the psychological state is divided into a plurality of data segments according to the data of each item included in the "data related to educational services", and classified statistical processing is performed to calculate classified statistical data for each class.

[0141] For the sake of simplicity, the processing unit data 1200 in FIG. 12 shows only one type of sectional statistical data for one class, but the sectional statistical data is calculated for one class for the number of items included in the “data related to educational services.”

[0142] (5) Specific examples of processing unit data for instructors classified into specific groups When the analyst selects one specific teacher, the information providing device 310 generates processing unit data for teachers other than the one specific teacher who are classified into a specific group into which the selected one specific teacher is classified. The information providing device 310 also stores the generated processing unit data for teachers other than the one specific teacher in the processing unit data storage section 622. Fig. 13 is a diagram showing an example of processing unit data for a teacher classified into a specific group.

[0143] When the analyst selects one particular instructor, all the time series data indicating the psychological states of instructors other than the one particular instructor who are classified into the particular group into which the selected one particular instructor is classified is read out from the collected data storage unit 621. In the processing unit data 1300, the "time series data indicating psychological states" stores time series data indicating all the psychological states of learners who attended classes taught by instructors other than the one particular instructor who are classified into the particular group into which the selected one particular instructor is classified.

[0144] In addition, from the collected data storage unit 621, "data related to the learner's lessons" and "data related to educational services" that are associated with the time-series data indicating the psychological state are also read out.

[0145] The read-out time-series data indicating the psychological state is subjected to statistical processing for each class, and statistical data for each class is calculated. In addition, the read-out time-series data indicating the psychological state is divided into a plurality of data segments according to the data of each item included in the "data related to educational services", and classified statistical processing is performed to calculate classified statistical data for each class.

[0146] For the sake of simplicity, the processing unit data 1200 in FIG. 12 shows only one type of sectional statistical data for one class, but the sectional statistical data is calculated for one class for the number of items included in the “data related to educational services.”

[0147] (6) Specific examples of processing unit data for all course instructors When the analyst selects one particular teacher, the information providing device 310 generates processing unit data for all teachers other than the selected one teacher, and stores the data in the processing unit data storage section 622. Fig. 14 is a diagram showing an example of the processing unit data for all teachers.

[0148] When the analyst selects one particular instructor, all the time series data indicating the psychological state of each of the learners who attended classes taught by all instructors other than the one particular instructor selected is read out from the collected data storage unit 621. In the processing unit data 1400 in Fig. 14, each data stored in the "time series data indicating psychological state" is time series data indicating the psychological state of each of the learners who attended classes taught by all instructors other than the one particular instructor selected.

[0149] In addition, from the collected data storage unit 621, "data related to the learner's lessons" and "data related to educational services" that are associated with the time-series data indicating the psychological state are also read out.

[0150] The read-out time-series data indicating the psychological state is subjected to statistical processing for each class, and statistical data for each class is calculated. In addition, the read-out time-series data indicating the psychological state is divided into a plurality of data segments according to the data of each item included in the "data related to educational services", and classified statistical processing is performed to calculate classified statistical data for each class.

[0151] For the sake of simplicity, the processing unit data 1400 in FIG. 14 shows only one type of categorized statistical data for one class, but the categorized statistical data is calculated for one class for the number of items included in the "data related to educational services."

[0152] <Example of a process for generating feedback information> Next, a specific example of a process executed by the information providing device 310 to generate feedback information from the processing unit data stored in the processing unit data storage section 622 will be described.

[0153] (1) Example 1 of the process for generating feedback information (for learners) First, a specific example of a process for generating feedback information (for a learner) based on processing unit data of a specific learner will be described. Fig. 15 is a diagram showing a first specific example of a process for generating feedback information (for a learner).

[0154] When a specific learner is selected by the analyst and the processing unit data 900 is stored in the processing unit data storage unit 622, the information providing device 310 reads the processing unit data 900 from the processing unit data storage unit 622 and performs multivariate analysis. In this way, the information providing device 310 extracts data items that are correlated with the statistical data or sectional statistical data (i.e., that contribute to the level or change in level of the time series data or sectional time series data that indicate the psychological state of each class).

[0155] Reference numeral 1500 in Figure 15 indicates that, as a result of the multivariate analysis, "subject," "teacher," "start and end times," "room temperature," and "amount of food eaten" were extracted as data items that contribute to the level or change in level of the time-series data indicating the psychological state of each class.

[0156] Next, the information providing device 310 performs a multivariate analysis for each extracted item, and identifies data that contributes to raising the level of the time series data showing the psychological state. Reference numeral 1510 in FIG. 15 indicates that, as a result of performing a multivariate analysis for the item="subject", in the case of a specific one learner, data="Japanese language" contributes the most to raising the level of the time series data showing the psychological state. Reference numeral 1510 in FIG. 15 indicates that, as a result of performing a multivariate analysis for the item="teacher", in the case of a specific one learner, data="teacher β" contributes the most to raising the level of the time series data showing the psychological state. Reference numeral 1510 in FIG. 15 indicates that, as a result of performing a multivariate analysis for the item="start, end time", in the case of a specific one learner, data="9:00-10:00" contributes the most to raising the level of the time series data showing the psychological state. Also, reference numeral 1510 in Fig. 15 indicates that, as a result of performing a multivariate analysis on item="room temperature", in the case of one specific learner, data="24°C" contributed most to raising the level of the time-series data indicating the psychological state. Also, reference numeral 1510 in Fig. 15 indicates that, as a result of performing a multivariate analysis on item="amount of food eaten", in the case of one specific learner, data="small" contributed most to raising the level of the time-series data indicating the psychological state.

[0157] Next, the information providing device 310 generates feedback information (for the learner) based on the target data indicated by reference numeral 1510. Reference numeral 1520 in FIG. 15 indicates that a specific learner selected by the analyst: -High concentration in Japanese lessons taught by Teacher β, - I am more focused in the morning. The lower the indoor temperature, the higher the concentration. - Eating less before class helps you concentrate better. This indicates that the characteristics described above will be sent to the learner or guardian as feedback information (for the learner).

[0158] (2) Example 2 of the process for generating feedback information (for learners) Next, a specific example of the process of generating feedback information (for a learner) based on a comparison between the processing unit data of a specific one learner and the processing unit data of learners other than the specific one learner who are classified into the group to which the specific one learner is classified will be described. Fig. 16 is a diagram showing a second specific example of the process of generating feedback information (for a learner).

[0159] When a specific learner is selected by the analyst and the processing unit data 900 and processing unit data 1000 are stored in the processing unit data storage unit 622, the information providing device 310 reads the processing unit data 1000 from the processing unit data storage unit 622 and performs multivariate analysis.

[0160] As a result, the information providing device 310 extracts data items that are correlated with the statistical data or sectional statistical data (that is, that contribute to the level or change in level of the time series data or sectional time series data indicating the psychological state of each class).

[0161] Reference numeral 1600 in Figure 16 indicates that, as a result of the multivariate analysis, "subject" and "teacher" were extracted as data items that contribute to the level or change in level of the time-series data indicating the psychological state of each class.

[0162] Next, the information providing device 310 performs a multivariate analysis for each extracted item using the processing unit data 900, and identifies data that contributes to raising the level of the time-series data indicating the psychological state. Reference numeral 1610 in Fig. 16 indicates that, as a result of performing a multivariate analysis for item="subject", in the case of a specific learner, data="Japanese language" contributes most to raising the level of the time-series data indicating the psychological state. Reference numeral 1610 in Fig. 16 indicates that, as a result of performing a multivariate analysis for item="teacher", in the case of a specific learner, data="teacher β" contributes most to raising the level of the time-series data indicating the psychological state.

[0163] Next, the information providing device 310 performs a multivariate analysis for each extracted item using the processing unit data 1000, and identifies data that contributes to raising the level of the time-series data indicating the psychological state. Reference numeral 1610 in FIG. 16 indicates that, as a result of performing a multivariate analysis on the item="subject", in the case of learners other than the specific one learner who are classified into the group (here, temperament type Tm1) into which the specific one learner is classified, the following data contributes to raising the level of the time-series data indicating the psychological state: ·data="Japanese" Also, the reference number 1610 in FIG. 16 shows that, as a result of performing a multivariate analysis on the item ="teacher", in the case of learners other than the specific one learner who are classified into the group into which the specific one learner is classified, in order to raise the level of the time-series data showing the psychological state, ·data="teacher α" This shows that it is the most contributing factor.

[0164] Next, the information providing device 310 generates feedback information (for the learner) based on the target data indicated by reference numeral 1610 and the tendency data indicated by reference numeral 1620. Reference numeral 1630 in FIG. 16 indicates that a specific learner selected by the analyst: Unlike other students with the same temperament, Teacher β tends to be more focused in Japanese classes than Teacher α. This indicates that the characteristics described above will be sent to the learner or guardian as feedback information (for the learner).

[0165] (3) Example 3 of the process for generating feedback information (for learners) Next, a specific example of the process of generating feedback information (for a learner) based on a comparison between the processing unit data of a specific learner and the processing unit data of all learners other than the specific learner will be described. Fig. 17 is a diagram showing a third specific example of the process of generating feedback information (for a learner).

[0166] When a specific learner is selected by the analyst and the processing unit data 900 and processing unit data 1100 are stored in the processing unit data storage unit 622, the information providing device 310 reads the processing unit data 1100 from the processing unit data storage unit 622 and performs multivariate analysis.

[0167] As a result, the information providing device 310 extracts data items that are correlated with the statistical data or sectional statistical data (that is, that contribute to the level or change in level of the time series data or sectional time series data indicating the psychological state of each class).

[0168] Reference numeral 1700 in Figure 17 indicates that, as a result of the multivariate analysis, "subject" and "teacher" were extracted as data items that contribute to the level or change in level of the time-series data indicating the psychological state of each class.

[0169] Next, the information providing device 310 performs a multivariate analysis for each extracted item using the processing unit data 900, and identifies data that contributes to raising the level of the time-series data indicating the psychological state. Reference numeral 1710 in Fig. 17 indicates that, as a result of performing a multivariate analysis for item="subject", in the case of a specific learner, data="Japanese language" contributes most to raising the level of the time-series data indicating the psychological state. Reference numeral 1710 in Fig. 17 indicates that, as a result of performing a multivariate analysis for item="teacher", in the case of a specific learner, data="teacher β" contributes most to raising the level of the time-series data indicating the psychological state.

[0170] Next, the information providing device 310 performs a multivariate analysis for each extracted item using the processing unit data 1100, and identifies data that contributes to raising the level of the time series data showing the psychological state. Reference numeral 1720 in Fig. 17 indicates that, as a result of performing a multivariate analysis for item="subject", data="Japanese language" contributes most to raising the level of the time series data showing the psychological state for all learners other than one specific learner. Reference numeral 1720 in Fig. 17 indicates that, as a result of performing a multivariate analysis for item="teacher", data="teacher α" contributes most to raising the level of the time series data showing the psychological state for all learners other than one specific learner.

[0171] Next, the information providing device 310 generates feedback information (for the learner) based on the target data indicated by reference numeral 1710 and the tendency data indicated by reference numeral 1720. Reference numeral 1730 in FIG. 17 indicates that a specific learner selected by the analyst: Compared to all other students, the concentration level in Teacher Alpha's Japanese class tends to be low. This indicates that the characteristics described above will be sent to the learner or guardian as feedback information (for the learner).

[0172] (4) Example 1 of a process for generating feedback information (for instructors) Next, a specific example of a process for generating feedback information (for instructors) based on a comparison between the processing unit data of one particular instructor and the processing unit data of instructors other than the one particular instructor who are classified into the group to which the one particular instructor is classified will be described. Fig. 18 is a diagram showing a first specific example of a process for generating feedback information (for instructors).

[0173] When a specific instructor is selected by the analyst and the processing unit data 1200 and the processing unit data 1300 are stored in the processing unit data storage unit 622, the information providing device 310 reads the processing unit data 1300 from the processing unit data storage unit 622 and performs multivariate analysis. In this way, the information providing device 310 extracts data items that correlate with the statistical data or sectional statistical data (i.e., that contribute to the level or change in level of the time series data or sectional time series data indicating the psychological state of each class).

[0174] Reference numeral 1800 in FIG. 18 indicates that, as a result of the multivariate analysis, the “proportion of practical training,” “timing of breaks,” and “proportion of main topic / chats” were extracted as data items that contribute to the level or change in level of the time-series data indicating the psychological state of each class.

[0175] Next, the information providing device 310 calculates the average value (or the data with the highest frequency of occurrence) of the processing unit data 1200 for each extracted item. Reference numeral 1810 in FIG. 18 indicates the result of calculating the data with the highest frequency of occurrence for the item="proportion of practical training", and for a specific instructor, ·data="20%" The reference numeral 1810 in FIG. 18 indicates that the data with the highest occurrence frequency for the item ="break timing" is calculated, and for a specific instructor, ·data="pre-training" Also, reference numeral 1810 in FIG. 18 indicates that, as a result of calculating the data with the highest occurrence frequency for the item="proportion of main topic / small talk", in the case of a specific instructor, data="8:2" This indicates that the frequency of occurrence of

[0176] Next, the information providing device 310 determines, from among the instructors included in the processing unit data 1300, those instructors who have a high level of statistical data of learners who attended the class or category statistical data. Furthermore, the information providing device 310 reads out the extracted item data from the processing unit data 1300 among the data of each item of the determined instructor, and calculates the average value of the data of each item (or data with a high occurrence frequency). Reference numeral 1820 in FIG. 18 indicates the result of calculating the data with the highest occurrence frequency for the item="proportion of practical training". According to reference numeral 1820, in the case of other instructors classified into the group (impression type I2) into which a specific instructor is classified, and who have a high level of statistical data of learners who attended the class or category statistical data, ·data="15%" The figure also shows that the frequency of occurrence of the item "break timing" is the highest. Also, reference numeral 1820 in FIG. 18 shows the result of calculating the data with the highest frequency of occurrence for the item "break timing". According to reference numeral 1820, in the case of other instructors who are classified into the group (impression type I2) into which a specific instructor is classified, and who have a high level of statistical data of students who have attended the class or statistical data of categories, ·data="After the chat" The frequency of occurrence of was the highest. Also, reference numeral 1820 in FIG. 18 indicates the result of calculating the data with the highest frequency of occurrence for the item="proportion of main topic / small talk". According to reference numeral 1820, in the case of other instructors who are classified into the group (impression type I2) into which a specific instructor is classified, and who have a high level of statistical data of students who have attended the class or statistical data of categories, ·data="7:3" This indicates that the frequency of occurrence of

[0177] Next, the information providing device 310 generates feedback information (for the instructor) based on the subject data indicated by reference numeral 1810 and the tendency data indicated by reference numeral 1820. Reference numeral 1830 in FIG. 18 indicates improvement information for a lesson of a specific instructor selected by the analyst. - Classes where students are highly concentrated tend to have frequent breaks and long chats. - Because this is a tendency of teachers with the same impression type, there is a high possibility that incorporating this method will increase the concentration of students. This indicates that improvement information such as the above is sent to the instructor as feedback information (for instructors).

[0178] (5) Example 2 of the process for generating feedback information (for instructors) Next, a specific example of the process of generating feedback information (for instructors) based on a comparison between the processing unit data of a specific instructor and the processing unit data of all instructors other than the specific instructor will be described. Fig. 19 is a diagram showing a second specific example of the process of generating feedback information (for instructors).

[0179] When a specific instructor is selected by the analyst and the processing unit data 1200 and the processing unit data 1400 are stored in the processing unit data storage unit 622, the information providing device 310 reads the processing unit data 1400 from the processing unit data storage unit 622 and performs multivariate analysis. In this way, the information providing device 310 extracts data items that correlate with the statistical data or sectional statistical data (i.e., that contribute to the level or change in level of the time series data or sectional time series data indicating the psychological state of each class).

[0180] The reference numeral 1900 in FIG. 19 indicates, as a result of the multivariate analysis, the items of data that contribute to the level or change in level of the time series data showing the psychological state of each lesson. "Break timing", "voice volume", "speaking speed", "ratio of main topic / small talk" It shows how it was extracted.

[0181] Next, the information providing device 310 calculates the average value (or the data with the highest frequency of occurrence) of the processing unit data 1200 for each extracted item. Reference numeral 1910 in FIG. 19 indicates the result of calculating the data with the highest frequency of occurrence for the item="break timing". According to reference numeral 1910, in the case of a specific teacher, ·data="pre-training" The frequency of occurrence of the item "voice volume" is the highest. Also, reference numeral 1910 in FIG. 19 indicates the result of calculating the data with the highest frequency of occurrence for the item "voice volume". According to reference numeral 1910, in the case of a specific teacher, ·data="medium" The frequency of occurrence of the item "speaking speed" is the highest. Also, reference numeral 1910 in FIG. 19 indicates the result of calculating the data with the highest frequency of occurrence for the item "speaking speed". According to reference numeral 1910, in the case of a specific instructor, ·data="slowly" The frequency of occurrence of the item "Main topic / chatter ratio" is the highest. Also, reference numeral 1910 in FIG. 19 indicates the result of calculating the data with the highest frequency of occurrence for the item "Main topic / chatter ratio". According to reference numeral 1910, in the case of a specific instructor, ·data="7:3" This indicates that the frequency of occurrence of

[0182] Next, the information providing device 310 determines, from among the instructors included in the processing unit data 1400, those instructors who have a high level of statistical data of learners who attended the class or category statistical data. Furthermore, the information providing device 310 reads out the extracted item data from the processing unit data 1400, from among the data of each item of the determined instructor, and calculates the average value of the data of each item (or data with a high occurrence frequency). Reference numeral 1920 in FIG. 19 indicates the result of calculating the data with the highest occurrence frequency for the item="break timing". According to reference numeral 1920, in the case of an instructor other than the specific instructor of the class who has a high level of statistical data of learners who attended the class or category statistical data, ·data="pre-training" The reference numeral 1920 in FIG. 19 indicates the result of calculating the data with the highest occurrence frequency for the item="voice volume". According to the reference numeral 1920, in the case of a teacher other than the specific teacher who has a high level of statistical data of students who have taken the class or statistical data of classification, ·data="big" The frequency of occurrence of the item "speaking speed" is the highest. Also, reference numeral 1920 in FIG. 19 indicates the result of calculating the data with the highest frequency of occurrence for the item "speaking speed". According to reference numeral 1920, in the case of a teacher other than the specific teacher, who has a high level of statistical data of learners who have attended the class or statistical data of classification, ·data="somewhat fast" The frequency of occurrence of the item "Main topic / chatter ratio" is the highest. Also, reference numeral 1920 in FIG. 19 indicates the result of calculating the data with the highest frequency of occurrence for the item "Main topic / chatter ratio". According to reference numeral 1920, in the case of a teacher other than the specific teacher, who has a high level of statistical data of students who have attended the class or statistical data of classification, data="8:2" This indicates that the frequency of occurrence of

[0183] Next, the information providing device 310 generates feedback information (for the instructor) based on the subject data indicated by reference numeral 1910 and the tendency data indicated by reference numeral 1920. Reference numeral 1930 in FIG. 19 indicates improvement information for a lesson of a specific instructor selected by the analyst. - Classes where students are highly concentrated tend to have frequent breaks and long chats. - The volume of the voice and the speaking speed are largely dependent on the impression type, so even if they are introduced, it is unlikely that they will increase the learner's concentration. This indicates that improvement information such as the above is sent to the instructor as feedback information (for instructors).

[0184] <Information provision process flow> Next, a flow of the information providing process by the information providing device 310 will be described. As shown in Figs. 15 to 19, the information providing process by the information providing device 310 includes the following processes for generating feedback information: - A process for generating feedback information using only the subject data (Figure 15); A process of generating feedback information using subject data and trend data (FIGS. 16 to 19); Therefore, in the following, the two will be explained separately.

[0185] (1) When using only subject data FIG. 20 is a first flowchart showing the flow of the information provision process.

[0186] In step S2001, the information providing device 310 acquires time-series data relating to the mental state of the learner. The information providing device 310 also generates time-series data indicating the mental state of the learner and stores it in the collected data storage unit 621.

[0187] In step S2002, the information providing device 310 acquires data relating to the learner's lessons or tests, and stores the data in the collected data storage unit 621 in association with time-series data indicating the learner's psychological state.

[0188] In step S2003, the information providing device 310 acquires data relating to educational services, and stores the data in the collected data storage unit 621 in association with time-series data indicating the psychological state of the learner.

[0189] In step S2004, the information providing device 310 acquires data relating to the learning environment or the test environment, and stores the data in the collected data storage unit 621 in association with the time-series data indicating the psychological state of the learner.

[0190] In step S2005, the information providing device 310 acquires data on the behavior of the learner, and stores the data in the collected data storage unit 621 in association with time-series data indicating the psychological state of the learner.

[0191] In step S2006, the information providing device 310 accepts the selection of one particular learner from the analyst.

[0192] In step S2007, the information providing device 310 generates processing unit data 900 for one specific learner. The information providing device 310 also performs multivariate analysis on the generated processing unit data 900 to extract data items that contribute to the level or change in level of the time-series data or sectioned time-series data indicating the psychological state of each lesson.

[0193] In step S2008, the information providing device 310 performs multivariate analysis on each of the extracted items to extract data that contributes to the level or change in level of the time series data or segmented time series data indicating the psychological state of each class as subject data.

[0194] In step S2009, the information providing device 310 generates feedback information (for the learner) based on the target student data.

[0195] In step S2010, the information providing device 310 transmits feedback information (for the learner) to the learner or the guardian.

[0196] In step S2011, it is determined whether or not to end the information provision processing. If it is determined in step S2011 that the information provision processing is to be continued (NO in step S2011), the process returns to step S2006. On the other hand, if it is determined in step S2011 that the information provision processing is to be ended (YES in step S2011), the information provision processing is ended.

[0197] (2) When using subject data and trend data Fig. 21 is a second flowchart showing the flow of the information provision process. Note that the difference from the first flowchart shown in Fig. 20 is steps S2101 to S2106. Therefore, steps S2101 to S2106 will be described below.

[0198] In step S2101, the information providing device 310 selects one particular learner or one particular teacher.

[0199] In step S2102, the information providing device 310 reads out the processing unit data 1000 of the learners other than the specific one learner who are classified into the group into which the specific one learner is classified, or the processing unit data 1100 of all learners other than the specific one learner. The information providing device 310 also performs a multivariate analysis on the read processing unit data 1000 or 1100 to extract data items that contribute to the high / low level or level change of the time series data or section time series data showing the psychological state. Alternatively, the information providing device 310 reads out the processing unit data 1300 of the teacher other than the specific one teacher who are classified into the group into which the specific one teacher is classified, or the processing unit data 1400 of all teacher other than the specific one teacher. The information providing device 310 also performs a multivariate analysis on the read processing unit data 1300 or 1400 to extract data items that contribute to the high / low level or level change of the time series data or section time series data showing the psychological state.

[0200] In step S2103, the information providing device 310 generates processing unit data 900 for one specific learner, and generates subject data by performing multivariate analysis for each extracted item on the generated processing unit data 900. Alternatively, the information providing device 310 generates processing unit data 1200 for one specific teacher, and generates subject data by calculating the average data value or the data with the highest frequency of occurrence for each extracted item from the generated processing unit data 1200.

[0201] In step S2104, the information providing device 310 reads out the processing unit data 1000 of learners other than the specific one learner who is classified into the group to which the specific one learner is classified, or the processing unit data 1100 of all learners other than the specific one learner. The information providing device 310 also generates trend data by performing a multivariate analysis for each extracted item on the read processing unit data 1000 or 1100. Alternatively, the information providing device 310 A teacher other than the specific teacher who is classified into the group into which the specific teacher is classified, and who has a high level of statistical data or classification statistical data of students who have taken the class, or All instructors other than a specific instructor who have high levels of statistical data or category statistical data of students who have taken the class In addition, the information providing device 310 reads out the processing unit data 1300 or 1400 of the determined teacher in charge of the class, and calculates the average value of the data or the data with the highest frequency of occurrence for each extracted item, thereby generating trend data.

[0202] In step S2105, the information providing device 310 compares the target data generated in step S2103 as a result of the selection of one specific learner with the trend data generated in step S2104 as a result of the selection of one specific learner. As a result, the information providing device 310 generates feedback information (for the learner). The information providing device 310 also compares the target data generated in step S2103 as a result of the selection of one specific teacher with the trend data generated in step S2104 as a result of the selection of one specific teacher. As a result, the information providing device 310 generates feedback information (for the instructor).

[0203] In step S2106, the information providing device 310 transmits feedback information (for the learner) to the learner or the guardian, and transmits feedback information (instructor) to the instructor.

[0204] <Summary> As is clear from the above description, the information providing device 310 according to the first embodiment: Time series data showing the mental state of the learner is stored in a predetermined time unit. -Supports time series data, Data on each item related to the learner's learning behavior, Data on each item related to educational services that changes within a specified time unit; Data on each item related to the environment, Data for each item on learner behavior outside of a given time unit; Collect one or more of the following: From the time series data of a predetermined time unit stored in the storage unit, extract data items that contribute to the level or level change of the time series data of the predetermined time unit corresponding to a specific learner or a specific teacher, or extract data items that contribute to the level or level change of the section time series data, which is the time series data of each data section within the predetermined time unit.

[0205] In this way, the information providing device 310 according to the first embodiment collects time series data or sectioned time series data indicating the psychological state of the learner in association with data for each item. In addition, the information providing device 310 according to the first embodiment extracts data items that contribute to the level or change in level of the collected time series data or sectioned time series data. As a result, the information providing device 310 according to the first embodiment can perform an analysis suitable for improving educational services or learning efficiency, and can provide feedback information useful for efforts to improve educational services or learning efficiency.

[0206] [Second embodiment] In the first embodiment, a multivariate analysis is performed when extracting data items that contribute to the level or change in level of time-series data in a predetermined time unit. However, the method of extracting data items that contribute to the level or change in level of time-series data in a predetermined time unit is not limited to this. For example, a machine learning model may be used to extract the data items.

[0207] Similarly, in the first embodiment, the subject data or tendency data is generated by performing a multivariate analysis or calculating an average value (calculating data with the highest frequency of occurrence), but the method of generating the subject data or tendency data is not limited to this. For example, the subject data or tendency data may be generated using a model that has undergone machine learning.

[0208] 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.

[0209] In the first embodiment, when the subject is a specific learner, the extracted item data is -Identifying data about one particular learner; Data identifying learners other than the specific learner in the specific group into which the specific learner was classified; and However, the comparison combination is not limited to this. For example, the extracted item data is -Identifying data about one particular learner; Data identifying learners who were classified into groups other than the specific group into which a particular learner was classified; may be compared.

[0210] In the first embodiment, when the subject is a specific teacher, the average value (or data with a high frequency of occurrence) of the extracted items of data is -The average value (or data with high frequency of occurrence) calculated for a specific instructor, The average value (or data with high frequency of occurrence) calculated for a portion of the instructors other than the instructor of the specific class in the specific group into which the instructor of the specific class was classified, However, the comparison combination is not limited to this. For example, the average value of the data of the extracted items (or data with a high frequency of occurrence) -The average value (or data with high frequency of occurrence) calculated for a specific instructor, The average value (or data with high frequency of occurrence) calculated for instructors classified into groups other than the specific group into which a specific instructor was classified, may be compared.

[0211] In the first embodiment, when the subject is a specific learner, Data specific to learners other than the particular learner into the particular group into which the particular learner was classified; or Identifying data about all learners except one specific learner; However, the trend data is not limited to this. For example, Data identifying a subset of learners other than the particular learner into which the particular learner was classified; or Data identifying a subset of all learners other than one specific learner; may be used as trend data.

[0212] Similarly, in the first embodiment, if the subject is a specific instructor, The average value (or data with high frequency of occurrence) calculated for a portion of the instructors other than the instructor of the specific instructor in the group into which the instructor of the specific instructor was classified, or -The average value (or data with high frequency of occurrence) calculated for a portion of the instructors other than the instructor of a specific class, However, the trend data is not limited to this. For example, The average value (or data with high frequency of occurrence) calculated for the instructors other than the instructor of the specific instructor in the group into which the instructor of the specific instructor was classified, or -The average (or data with high frequency of occurrence) calculated for all instructors other than the instructor of a specific class, may be used as trend data.

[0213] In the above first embodiment, a specific example of some of the instructors who are classified into a specific group is described with reference to Fig. 18. In contrast to this, a specific example of some of the learners who are classified into a specific group is described here.

[0214] For example, suppose that a specific learner is a "male," and the group to which the specific learner is classified is the class to which the specific learner belongs. In this case, the portion of learners classified into a specific group refers to, for example, male learners among the learners in the class to which the specific learner belongs.

[0215] In this way, item data may be specified for some of the learners who are classified into a specific group. This makes it possible to more accurately analyze items that affect the psychological state of a specific learner and the learning efficiency, compared to when item data is specified for all learners who are classified into a specific group.

[0216] In other words, by using a portion of the group rather than the entire group, the accuracy of the analysis can be improved.

[0217] In the first embodiment, Figs. 9 to 11 show processing unit data including time series data in units of lessons as a specific example of processing unit data, but the same applies to processing unit data including time series data in units of tests for each subject. In the first embodiment, Figs. 15 to 17 show a case where feedback information (for learners) is generated based on processing unit data including time series data in units of lessons as a specific example of the process of generating feedback information (for learners). However, the same process is also applied to the case where feedback information (for learners) is generated based on processing unit data including time series data in units of tests for each subject.

[0218] In the above first embodiment, the system 300 has been described as having one information providing device 310, but the system 300 may have a plurality of information providing devices 310. In this case, each information providing device may be configured to independently execute an information providing program, or each information providing device may be configured to cooperate with each other to execute one information providing program.

[0219] [Other embodiments] In the above first and second embodiments, no detailed description is given of how the feedback information provided by the information providing device 310 is used, but the feedback information may be used in any manner.

[0220] For example, in the case of a group lesson, it has been difficult to grasp the psychological state of all the learners in the past, but with the information providing device 310, the feedback information can be used as objective data for the instructor to improve his / her teaching method.

[0221] Furthermore, by using the feedback information, for example, a learner can determine his / her compatibility with a teacher. In the case of video lessons, it is generally difficult to ask a teacher to make short-term improvements, and it takes time to change the content of teaching materials, curriculum, etc. In contrast, by using the feedback information, a learner can determine which teacher he / she is compatible with, and can select a video lesson by a teacher with whom he / she can concentrate better.

[0222] Furthermore, in the case of video lessons, whereas it was previously impossible to grasp the psychological state of the learner, the above feedback information allows the instructor to grasp the psychological state of the learner even in a video lesson. Furthermore, in the past, it was difficult to grasp the psychological state of learners whose psychological states were difficult to grasp even in individual lessons, but the above feedback information allows the instructor to grasp the psychological state of such learners.

[0223] In addition, in the past, those providing career guidance had to provide career guidance without understanding the psychological state of students during class. However, by using the above feedback information, it is possible to provide career guidance after understanding the psychological state of students during class.

[0224] In addition, in the above embodiments, the statistical processing unit 605 and the section statistical processing unit 606 have been described as performing arbitrary statistical processing, but the arbitrary statistical processing includes a process of calculating an arbitrary feature. The arbitrary feature may include, for example, a duration during which the level of the time-series data indicating the mental state of the learner is high (or low). Alternatively, the arbitrary feature may include, for example, a fluctuation amount or a fluctuation amount per unit time of the level of the time-series data indicating the mental state of the learner.

[0225] As a result, the item extraction unit 607 extracts the following data items that affect these feature quantities: -Each item included in "Data related to learners' learning behavior" Each item included in "Data related to educational services" - Each item included in "Environmental Data" Each item included in "Data on learner behavior" It is possible to analyze one or more of the items. As a result, the information providing device 310 can analyze which item has an influence when, for example, a high state of concentration of the learner is maintained (or when a high state is not maintained), which can lead to improvement of learning efficiency. It is also possible to analyze the item that plays an important role in quickly shifting the learner to a high state of concentration, which can lead to improvement of learning efficiency.

[0226] In addition, in each of the above embodiments, the information providing device is applied to an educational institution, but the application of the information providing device is not limited to an educational institution. For example, the information providing device may be applied to a scene that provides services related to qualification learning, vocational training, in-house training / out-of-house training for working adults, reskilling, and other education. According to the information providing device of the present disclosure, even in such an application, data suitable for performing various analyses related to education can be provided.

[0227] The present invention is not limited to the configurations shown in the above embodiments, combinations with other elements, etc. These points can be changed without departing from the spirit of the present invention, and can be appropriately determined according to the application form.

[0228] This application claims priority based on Japanese Patent Application No. 2024-038362, filed on March 12, 2024, the entire contents of which are incorporated herein by reference. [Explanation of symbols]

[0229] 310: Information provision device 601: Data Collection Department 602: Data generation unit 603: Selection section 604:Extraction part 605: Statistical processing unit 606: Sectional statistical processing unit 607: Item extraction part 608: Subject Data Calculation Department 609: Trend data calculation unit 610: Feedback information generating unit 611: Transmission unit 621: Collected data storage unit 622: Processing unit data storage unit

Claims

1. a storage unit for storing time series data indicating a psychological state of a learner in a predetermined time unit; a data collection unit that collects one or more of data on each item related to the learner's learning behavior corresponding to the time-series data, data on each item related to educational services that change within the predetermined time unit, data on each item related to the environment, and data on each item related to the learner's behavior outside the predetermined time unit; an item extraction unit that extracts, based on a result of a multivariate analysis, data items that contribute to the level or level change of the time-series data for the predetermined time unit corresponding to a specific learner from the time-series data for the predetermined time unit stored in the storage unit, or data items that contribute to the level or level change of segmented time-series data, which is time-series data for each data segment within the predetermined time unit; The extracted item data is Data identified for the specific learner; Data identifying a learner other than the specific learner or a part of learners other than the specific learner in a specific group into which the specific learner is classified; a feedback information generating unit for generating feedback information indicating a characteristic of the psychological state of the specific one learner by comparing the An information providing device having the above configuration.

2. A storage unit for storing time series data indicating a psychological state of a learner in a predetermined time unit; a data collection unit that collects one or more of data on each item related to the learner's learning behavior corresponding to the time-series data, data on each item related to educational services that change within the predetermined time unit, data on each item related to the environment, and data on each item related to the learner's behavior outside the predetermined time unit; an item extraction unit that extracts, based on a result of a multivariate analysis, data items that contribute to the level or level change of the time-series data for the predetermined time unit corresponding to a specific learner from the time-series data for the predetermined time unit stored in the storage unit, or data items that contribute to the level or level change of segmented time-series data, which is time-series data for each data segment within the predetermined time unit; The extracted item data is Data identified for the specific learner; Data identifying learners classified into a group other than the specific group into which the specific learner was classified, or a part of learners classified into a group other than the specific group; a feedback information generating unit for generating feedback information indicating a characteristic of the psychological state of the specific one learner by comparing the An information providing device having the above configuration.

3. A storage unit for storing time series data indicating a psychological state of a learner in a predetermined time unit; a data collection unit that collects one or more of data on each item related to the learner's learning behavior corresponding to the time-series data, data on each item related to educational services that change within the predetermined time unit, data on each item related to the environment, and data on each item related to the learner's behavior outside the predetermined time unit; an item extraction unit that extracts, based on the results of multivariate analysis, data items that contribute to the level or level change of the time series data of the predetermined time unit corresponding to a specific instructor from the time series data of the predetermined time unit stored in the storage unit, or data items that contribute to the level or level change of the section time series data, which is the time series data of each data section within the predetermined time unit; Data having an average value or a predetermined frequency of occurrence of the extracted item data, Data having an average value or a predetermined frequency of occurrence calculated for the specific instructor; Data having an average value or a predetermined frequency of occurrence calculated for a class teacher other than the specific class teacher in the specific group into which the specific class teacher is classified, or for a part of class teachers other than the specific class teacher in the specific group; a feedback information generating unit for generating feedback information for improving the psychological state of a learner attending a class taught by the particular one instructor by comparing the feedback information with the specific one instructor's feedback information; An information providing device having the above configuration.

4. A storage unit for storing time series data indicating a psychological state of a learner in a predetermined time unit; a data collection unit that collects one or more of data on each item related to the learner's learning behavior corresponding to the time-series data, data on each item related to educational services that change within the predetermined time unit, data on each item related to the environment, and data on each item related to the learner's behavior outside the predetermined time unit; an item extraction unit that extracts, based on the results of multivariate analysis, data items that contribute to the level or level change of the time series data of the predetermined time unit corresponding to a specific instructor from the time series data of the predetermined time unit stored in the storage unit, or data items that contribute to the level or level change of the section time series data, which is the time series data of each data section within the predetermined time unit; Data having an average value or a predetermined frequency of occurrence of the extracted item data, Data having an average value or a predetermined frequency of occurrence calculated for the specific instructor; Data having an average value or a predetermined frequency of occurrence calculated for instructors classified into a group other than the specific group into which the specific instructor was classified or for a portion of instructors classified into groups other than the specific group; a feedback information generating unit for generating feedback information for improving the psychological state of a learner attending a class taught by the particular one instructor by comparing the feedback information with the specific one instructor's feedback information; An information providing device having the above configuration.

5. The predetermined time unit is the time range of one lesson or the time range of a test for one subject.

5. An information providing device according to claim 1.

6. the data collection unit collects face image data and vital data of the learner at the predetermined time intervals; The information providing device includes: a data generating unit that generates time-series data indicating the mental state of the learner based on the face image data and the vital data collected by the data collecting unit; The information providing device according to claim 1 , further comprising:

7. storing time series data in a storage unit, the time series data being indicative of a psychological state of a learner, in a predetermined time unit; collecting one or more of data items related to the learner's learning behavior corresponding to the time series data, data items related to educational services that change within the predetermined time unit, data items related to the environment, and data items related to the learner's behavior outside the predetermined time unit; extracting, based on a result of multivariate analysis, from the time-series data for the predetermined time unit stored in the storage unit, data items that contribute to the level or level change of the time-series data for the predetermined time unit corresponding to a specific learner, or data items that contribute to the level or level change of segmented time-series data, which is time-series data for each data segment within the predetermined time unit; The extracted item data is Data identified for the specific learner; Data identifying a learner other than the specific learner or a part of learners other than the specific learner in a specific group into which the specific learner is classified; generating feedback information indicative of a characteristic of the psychological state of the particular one of the learners by comparing the A method for providing information executed by a computer.

8. A step of storing time series data indicating a psychological state of a learner in a storage unit, the time series data being in a predetermined time unit; collecting one or more of data items related to the learner's learning behavior corresponding to the time series data, data items related to educational services that change within the predetermined time unit, data items related to the environment, and data items related to the learner's behavior outside the predetermined time unit; extracting, based on a result of multivariate analysis, from the time-series data for the predetermined time unit stored in the storage unit, data items that contribute to the level or level change of the time-series data for the predetermined time unit corresponding to a specific learner, or data items that contribute to the level or level change of segmented time-series data, which is time-series data for each data segment within the predetermined time unit; The extracted item data is Data identified for the specific learner; Data identifying learners classified into a group other than the specific group into which the specific learner was classified, or a part of learners classified into a group other than the specific group; generating feedback information indicative of a characteristic of the psychological state of the particular one of the learners by comparing the A method for providing information executed by a computer.

9. storing time series data in a storage unit, the time series data being indicative of a psychological state of a learner, in a predetermined time unit; collecting one or more of data items related to the learner's learning behavior corresponding to the time series data, data items related to educational services that change within the predetermined time unit, data items related to the environment, and data items related to the learner's behavior outside the predetermined time unit; extracting, based on the results of multivariate analysis, data items that contribute to the level or level change of the time series data of the predetermined time unit corresponding to a specific instructor from the time series data of the predetermined time unit stored in the storage unit, or data items that contribute to the level or level change of the section time series data, which is the time series data of each data section within the predetermined time unit; Data having an average value or a predetermined frequency of occurrence of the extracted item data, Data having an average value or a predetermined frequency of occurrence calculated for the specific instructor; Data having an average value or a predetermined frequency of occurrence calculated for instructors other than the specific instructor in the specific group into which the specific instructor is classified, or for a part of instructors other than the specific instructor in the specific group; and generating feedback information for improving the psychological state of the learners attending the class taught by the particular one instructor by comparing the above-mentioned feedback information with the above-mentioned one instructor. An information providing program for causing a computer to execute the above.

10. A step of storing time series data indicating a psychological state of a learner in a storage unit, the time series data being in a predetermined time unit; collecting one or more of data items related to the learner's learning behavior corresponding to the time series data, data items related to educational services that change within the predetermined time unit, data items related to the environment, and data items related to the learner's behavior outside the predetermined time unit; extracting, based on the results of multivariate analysis, data items that contribute to the level or level change of the time series data of the predetermined time unit corresponding to a specific instructor from the time series data of the predetermined time unit stored in the storage unit, or data items that contribute to the level or level change of the section time series data, which is the time series data of each data section within the predetermined time unit; Data having an average value or a predetermined frequency of occurrence of the extracted item data, Data having an average value or a predetermined frequency of occurrence calculated for the specific instructor; Data having an average value or a predetermined frequency of occurrence calculated for instructors classified into a group other than the specific group into which the specific instructor was classified or for a portion of instructors classified into groups other than the specific group; and generating feedback information for improving the psychological state of the learners attending the class taught by the particular one instructor by comparing the above-mentioned feedback information with the above-mentioned one instructor. An information providing program for causing a computer to execute the above.