JP2020166481A - Information processing device - Google Patents

Abstract

To provide a technique for encouraging a participant of a course to take on a desirable behavior to pass a test.SOLUTION: An information processing device comprises a specification unit for specifying a target lesson for an object person among a plurality of lessons constituting a course related to a test, an acquisition unit for acquiring information which indicates a learning effect by taking the target lesson, and an output unit for outputting the information which indicates the learning effect to a terminal of the object person.SELECTED DRAWING: Figure 3

Description

Embodiments of the present invention relate to information processing devices and terminals.

In recent years, services that deliver courses such as qualification exams via the Internet have become widespread. In general, students make their own study schedule, such as weekly study hours and how to proceed for each theme, with the aim of passing the qualification test.

Services that deliver advice to students are widespread in order to reduce the work of such students and lead them to pass the qualification exam. For example, Patent Document 1 discloses a technique for giving advice to increase the learning pace when the learning pace is behind the attendance plan.

Japanese Unexamined Patent Publication No. 2004-259140

The advice disclosed in Patent Document 1 relates to learning time. Therefore, the student can grasp the delay in the learning pace, but cannot grasp the effective learning method for passing the qualification test.

The learning themes of the qualification exam are diverse. The learning themes that make it easy to increase the score and the learning themes that make it difficult to increase the score differ depending on the student. Therefore, it is difficult for students to pass the qualification test unless they proceed with learning effectively even if they increase their study time. It is not desirable for students to spend the same amount of time on all learning themes in order to pass the qualification exam.

In order to solve the above problems, we will provide the technology to encourage the students of the course to take desirable actions to pass the examination.

According to the embodiment, the information processing apparatus acquires a specific part that identifies the lesson to be taken by the subject among a plurality of lessons constituting the course related to the examination, and information indicating the learning effect of taking the lesson to be taken. It includes an acquisition unit and an output unit that outputs information indicating the learning effect to the terminal of the target person.

According to the embodiment, the information processing apparatus and the terminal can urge the subject to take a desirable action to pass the test by displaying the learning effect on the display unit.

FIG. 1 is a block diagram showing a configuration example of a course distribution system and a terminal according to an embodiment. FIG. 2 is a diagram showing a configuration example of a plurality of lessons constituting the target course according to the embodiment. FIG. 3 is a diagram showing a display example of the first learning effect for the target lecture according to the embodiment. FIG. 4 is a diagram illustrating the relationship between the score of the exercise according to the embodiment and the predicted score of the target test. FIG. 5 is a diagram showing a display example of the first learning effect for the target exercise according to the embodiment. FIG. 6 is a sequence diagram showing an operation example related to the score prediction model according to the embodiment. FIG. 7 is a sequence diagram showing an operation example related to the learning effect according to the embodiment.

Hereinafter, embodiments will be described in detail with reference to the drawings.
FIG. 1 is a block diagram showing a configuration example of the course distribution system 1 and the terminal 2.

The course distribution system 1 will be described.
The course distribution system 1 is a system that distributes the content of the course to each student who has registered to take the test course via a network such as the Internet. Here, the course distribution system 1 distributes a course related to a specific test to a terminal 2 owned by a specific student. In the following, a specific student who owns the terminal 2 is also referred to as a target person. A particular test is also referred to as a target test. Courses related to target exams are also called target courses.

The test may be a test whose final result is pass / fail, or a test whose final result is a score, and is not limited. For example, a pass / fail test is a qualification test. The qualification examination may be a national qualification examination or a private qualification examination. For example, a test whose final result is a score is a language test.

Each exam course consists of multiple lessons. Each lesson is a lecture or exercise, respectively. For example, a lecture is a form of attendance by watching video or audio. Lectures may be in the form of text-based learning on the web. Lectures may be in the form of a combination of video or audio viewing and text learning on the web. Exercises are a form of attendance by answering questions on the web. The exercise may partially include a lecture. Each lesson is a learning unit composed of a set of contents such as a theme. Each lesson is structured so that the time taken by the student for one lesson is relatively short. As a result, the target person can efficiently take each lesson by using the terminal 2 and utilizing the gap time.

The course distribution system 1 includes a server 3, a server 4, and a server 5.
The server 3, the server 4, and the server 5 are connected to each other so as to be able to communicate with each other via a network. The server 3, the server 4, and the server 5 will be described later.

The terminal 2 will be described.
The terminal 2 is a device used by the target person to attend the target course. For example, the terminal 2 is a PC (Personal Computer), but may be a smartphone, a tablet, or the like, and is not limited thereto.

The terminal 2 includes a processor 21, a ROM (Read Only Memory) 22, a RAM (Random Access Memory) 23, a storage device 24, a communication interface 25, an input device 26, and a display device 27. The processor 21, ROM 22, RAM 23, storage device 24, communication interface 25, input device 26, and display device 27 are connected to each other via a data bus or the like. In FIG. 1, the communication interface is described as "communication I / F".

The processor 21 controls the operation of the entire terminal 2. For example, the processor 21 is a CPU (Central Processing Unit), but is not limited thereto. The processor 21 may be composed of a circuit including a field programmable gate array (Field Programmable Gate Array) and the like. The processor 21 expands the program stored in advance in the ROM 22 or the storage device 24 into the RAM 23. The processor 21 executes various operations by executing the program expanded in the RAM 23.

The ROM 22 is a non-volatile memory that stores a control program, control data, and the like in advance.

The RAM 23 is a volatile memory that temporarily stores data and the like being processed by the processor 31.

The storage device 24 is a device composed of a non-volatile memory for storing data, programs, and the like. For example, the storage device 24 is composed of an HDD (Hard Disk Drive), an SSD (Solid State Drive), or the like, but is not limited thereto.

The storage device 24 stores a program executed by the processor 21. The program may be downloaded to the terminal 2 via the network. The program may be one stored in a computer-readable storage medium and installed in the terminal 2.

The communication interface 25 is an interface for communicating by wire or wirelessly. For example, the communication interface 25 includes interfaces corresponding to various wired or wireless standards. The communication interface 25 enables communication between the terminal 2 and another device.

The input device 26 is a device that receives an instruction based on an operation by the target person. For example, the input device 26 is, but is not limited to, a keyboard, a touch pad, and the like. For example, the input device 26 receives an instruction to start a target lesson based on an operation by the target person. The target lesson is a lesson to be taken by the target person among a plurality of lessons constituting the target course. The target lesson is also called the target lesson. Here, an example in which the target lesson is a non-taken lesson will be described. If the target lesson is a lecture, not attending means not watching. If the target lesson is an exercise, not taking it means not answering the questions provided in the exercise. In the following, if the target lesson is a lecture, this lecture is also referred to as the target lecture. If the target lesson is an exercise, this exercise is also referred to as the target exercise.

The display device 27 is a device that displays various screens. For example, the display device 27 is a liquid crystal display, but is not limited thereto. The display device 27 is an example of a display unit.

The hardware configuration of the terminal 2 is not limited to the above configuration. The terminal 2 makes it possible to omit or change the above-mentioned components and add new components as appropriate.

Each part mounted on the processor 21 will be described.
The processor 21 implements the acquisition unit 211 and the display control unit 212 by executing the program.
The acquisition unit 211 acquires various information from the server 4. For example, the acquisition unit 211 acquires information indicating the learning effect of taking the target lesson from the server 4. The learning effect is an effect obtained by taking the target lesson by the target person. An example of the learning effect will be described later. In the following, the information indicating the learning effect is also referred to as the learning effect information.
The display control unit 212 causes the display device 27 to display various information. For example, the display control unit 212 causes the display device 27 to display the learning effect.

The server 3 will be described.
The server 3 is a device used for managing data related to a plurality of participants in each examination course. In the following, data on a plurality of students will also be referred to as a plurality of student data. Here, a plurality of student data for the target course will be described as an example. An example of multiple student data for the target course will be described later.

The server 3 includes a processor 31, a ROM 32, a RAM 33, a storage device 34, and a communication interface 35. The processor 31, ROM 32, RAM 33, storage device 34, and communication interface 35 are connected to each other via a data bus or the like.
The processor 31 controls the operation of the entire server 3. The processor 31 is composed of the same hardware as the processor 21 described above.
The ROM 32 is composed of the same hardware as the ROM 22 described above.
The RAM 33 is composed of the same hardware as the RAM 23 described above.
The storage device 34 is composed of the same hardware as the storage device 24 described above. The storage device 34 stores a plurality of student data for the target course. The storage device 34 stores a program executed by the processor 31. The program may be downloaded to the server 3 via the network. The program may be one stored in a computer-readable storage medium and installed on the server 3.
The communication interface 35 is composed of the same hardware as the communication interface 25 described above.
The hardware configuration of the server 3 is not limited to the above configuration. The server 3 makes it possible to omit or change the above-mentioned components and add new components as appropriate.

A plurality of student data for the target course stored in the storage device 34 will be described.
The plurality of student data includes not only data on those who have completed the target course but also data on those who are taking the target course. For example, the data on the students who have completed the target course may include not only the data on the persons who have taken all the lessons constituting the target course but also the data on the persons who stopped taking the lessons in the middle of the target course. .. The plurality of student data includes data on the target person. In the following, the data regarding the target person is also referred to as the target person data. Multiple student data will be updated according to the progress of the students currently taking the course and the addition of new students.

The plurality of student data includes data on the attendance history for each student.
The attendance history is a log that records the learning behavior of each lesson in which the student took at least a part of the plurality of lessons constituting the target course. The attendance history includes not only the lessons that the student has completed, but also the learning behavior of the lessons that the student has taken halfway. In the following, lessons that students have completed are also referred to as completed lessons. Lessons that students take halfway are also called mid-course lessons. Lessons that students have not taken are also called untaken lessons.

Explain the case where the lesson is a lecture. For example, the attendance history includes the progress of the lecture (either attended, in the middle of attending, or not attending), the playback time of video or audio, and the learning time using text (lecture usage time) for each lesson. Includes elements of. For example, if the lecture is learning using textbooks, the progress of the lecture may be completed in response to the input of the completion of the lecture by the student. The server 3 may automatically record the learning time of the lecture, but may collect it from the terminal in response to the input by the student on the terminal. When inputting the study time of a lecture on the terminal, the student may input the study time of the text delivered in the course, or study the commercial text (for example, the part of the commercial text related to each lecture). You may enter the time.

Explain when the lesson is an exercise. For example, the attendance history is an element such as the progress of the exercise (either completed, in the middle of attending, or not attending), the score or correct answer rate of the exercise, and the learning time of the exercise (use time of the exercise) for each lesson. including. When a student takes the same exercise multiple times, the attendance history includes the score or correct answer rate of the exercise associated with the number of attendances (for example, the first attendance, the second attendance, etc.). The server 3 may automatically record the learning time of the exercise in the same manner as the learning time of the lecture described above, but may collect it from the terminal in response to the input by the student on the terminal. .. When the student inputs the learning time of the exercise on the terminal, the learning time of the exercise delivered in the course as well as the problem recorded in the commercial text (for example, commercially available) is similar to the learning time of the lecture described above. You may enter the study time of the part of the text related to each exercise).

The plurality of student data includes data on the score of the target test for the students who took the target test.
The score of the target test is the result of the student actually taking the target test. The score of the target test may be the result of the mock test instead of the result of the main test. For example, a practice test includes a test held by an external test institution. In this example, the results of the mock exam are reported by the students. For example, a mock exam includes a web-based exam included in the course. In this example, the results of the mock exam can be included in the attendance history.

The plurality of student data may include at least one of the data related to the student's experience and the data related to the student's attributes for each student.

Participant's experience includes the experience of actually taking the target exam in the past before the student took the target course. For example, the test experience includes factors such as the number of times the target test is taken and the result of the target test. For example, the past results of the target test may be formal results notified to the students by the test management organization, or may be the results of the students' self-scoring. Participant experience includes past learning experience with exams that differ from the target exam. For example, the learning experience includes factors such as the number of times a test is taken that is different from the target test.

The attributes of the students are unique to the students. For example, student attributes include factors such as age, gender, occupation and industry.

The plurality of student data is collected by the server 4 and stored in the server 3. For example, the server 4 measures the data related to the lesson attendance history for each student according to the distribution of the content of each lesson, and stores the data in the server 3. For example, the server 4 collects data on the student's attributes and the score of the target test from the terminal in response to the input by each student on the terminal, and stores the data on the server 3.

The server 4 will be described.
The server 4 is a device used for processing a request from a terminal 2, storing data in a server 3, processing a request in a server 5, and the like.

The server 4 includes a processor 41, a ROM 42, a RAM 43, a storage device 44, and a communication interface 45. The processor 41, ROM 42, RAM 43, storage device 44, and communication interface 45 are connected to each other via a data bus or the like.
The processor 41 controls the operation of the entire server 4. The processor 41 is composed of the same hardware as the processor 21 described above. The processor 41 implements each part described later that executes various operations by executing a program expanded in the RAM 43.
The ROM 42 is composed of the same hardware as the ROM 22 described above.
The RAM 43 is composed of the same hardware as the RAM 23 described above.
The storage device 44 is composed of the same hardware as the storage device 24 described above. The storage device 44 stores a program executed by the processor 41. The program may be downloaded to the server 4 via the network. The program may be an installed program stored in a computer-readable storage medium.
The communication interface 45 is composed of the same hardware as the communication interface 25 described above.
The hardware configuration of the server 4 is not limited to the above configuration. The server 4 makes it possible to omit or change the above-mentioned components and add new components as appropriate.

Each part mounted on the processor 41 will be described.
The processor 41 implements the start request acquisition unit 411, the target lesson identification unit 412, the prediction request output unit 413, the prediction information acquisition unit 414, and the output unit 415 by executing the program.

The start request acquisition unit 411 acquires the start request of the target lesson from the terminal 2 via the communication interface 45. For example, a request to start a target lesson includes information that identifies the target person and information that identifies the target lesson.

The target lesson specifying unit 412 specifies the target lesson as illustrated below. For example, the target lesson specifying unit 412 identifies the target lesson by referring to the information for specifying the target lesson included in the start request of the target lesson.

The prediction request output unit 413 outputs a prediction request for the learning effect of the target test to the server 5 via the communication interface 45. The learning effect prediction request includes information that identifies the target person and information that identifies the target lesson.

The prediction information acquisition unit 414 acquires learning effect information from the server 5 via the communication interface 45. The learning effect information is transmitted from the server 5 to the server 4 as a response to the learning effect prediction request.

The output unit 415 outputs various information and instructions to the terminal 2.
For example, the output unit 415 outputs the learning effect information to the terminal 2 via the communication interface 45. The learning effect information includes information about a message informing the subject of the learning effect.

For example, the output unit 415 outputs an instruction to the terminal 2 to display the learning effect at the start or completion of the target lesson.
An instruction for displaying the learning effect on the terminal 2 will be described.
When the target lesson is a lecture, for example, when the target lesson starts, it is the timing to display the start screen of the target lecture on the terminal 2. For example, when the target lesson is completed, it is the timing to display the end screen of the target lecture on the terminal 2. When the target lesson is an exercise, for example, at the start of the target lesson, it is the timing to display the initial screen provided by the target exercise on the terminal 2. When the target lesson is completed, it is the timing to display the screen of the score or the correct answer rate obtained by the target person in the question provided in the target exercise on the terminal 2.

For example, the output unit 415 outputs information indicating a predetermined attendance standard, which will be described later, to the terminal 2. The attendance standard can also be said to be the attendance status.
Explain the prescribed attendance criteria.
If the target lesson is a lecture, the prescribed attendance criterion is that the target person completes the viewing of the target lecture. When the target lesson is an exercise, the predetermined attendance standard is that the target person obtains a first score (predetermined score) or higher or a first correct answer rate (predetermined correct answer rate) or higher in the exercise. An example of setting the first score or the first correct answer rate will be described later.

A case where the learning effect is displayed on the terminal 2 at the start of the target lesson will be described.
For example, the output unit 415 outputs the learning effect information and the instruction to display the learning effect on the terminal 2 at the start of the target lesson to the terminal 2 together with the content of the target lesson. The output unit 415 may output information indicating a predetermined attendance standard to the terminal 2. As a result, the display control unit 212 of the terminal 2 causes the display device 27 to display the learning effect together with the content of the target lesson at the timing when the student starts the target lesson. Further, the display control unit 212 of the terminal 2 causes the display device 27 to display a predetermined attendance standard in association with the learning effect at the same timing.

A case where the learning effect is displayed on the terminal 2 when the target lesson is completed will be described.
For example, the output unit 415 delivers the learning effect information and an instruction to display the learning effect on the terminal 2 at the completion of the target lesson according to the completion of the target lesson by the student after delivering the content of the target lesson to the terminal 2. Output to 2. Instead of this, the output unit 415 may output the learning effect information and the instruction to display the learning effect on the terminal 2 to the terminal 2 in advance together with the content of the target lesson. As a result, the display control unit 212 of the terminal 2 causes the display device 27 to display the learning effect together with the contents of the target lesson at the timing when the student completes the target lesson. Further, the display control unit 212 of the terminal 2 causes the display device 27 to display a predetermined attendance standard in association with the learning effect at the same timing.

The server 5 will be described.
The server 5 is a device used for generating a score prediction model for a target test and predicting a learning effect using the score prediction model. The score prediction model is a trained model for predicting the score that the subject can obtain when the subject actually takes the target test, at least based on the attendance history of the subject. In the following, the score of the target test predicted by the trained model is also referred to as the predicted score.

The server 5 includes a processor 51, a ROM 52, a RAM 53, a storage device 54, and a communication interface 55. The processor 51, ROM 52, RAM 53, storage device 54, and communication interface 55 are connected to each other via a data bus or the like.
The processor 51 controls the operation of the entire server 5. The processor 51 is composed of the same hardware as the processor 21 described above. The processor 51 implements each part described later that executes various operations by executing a program expanded in the RAM 53.
The ROM 52 is composed of the same hardware as the ROM 22 described above.
The RAM 53 is composed of the same hardware as the RAM 23 described above.
The storage device 54 is composed of the same hardware as the storage device 24 described above. The storage device 54 stores the score prediction model. The storage device 54 stores a program executed by the processor 51. The program may be downloaded to the server 5 via the network. The program may be one stored in a computer-readable storage medium and installed on the server 5.
The communication interface 55 is composed of the same hardware as the communication interface 25 described above.
The hardware configuration of the server 5 is not limited to the above configuration. The server 5 makes it possible to omit or change the above-mentioned components and add new components as appropriate.

Each part mounted on the processor 51 will be described.
By executing the program, the processor 51 executes the teacher data request unit 511, the teacher data acquisition unit 512, the model generation unit 513, the model storage unit 514, the prediction request acquisition unit 515, the target data request output unit 516, and the target data acquisition unit. 517, a prediction unit 518 and a prediction information output unit 519 are implemented.

The teacher data request unit 511 outputs a request for teacher data to the server 3 via the communication interface 55. For example, the teacher data request unit 511 outputs a teacher data request to the server 3 when the server 5 generates the score prediction model and when the score prediction model is updated. Requesting teacher data includes requesting multiple student data for the subject course. Requests for multiple student data for a subject course include requests for data on attendance history and data on subject exam scores. A plurality of student data requests for a subject course may include at least one request of data on the student's experience and data on the student's attributes.

The teacher data acquisition unit 512 acquires teacher data from the server 3 via the communication interface 55. The teacher data is transmitted from the server 3 to the server 5 in response to the request for the teacher data. The teacher data includes data on the attendance history of each of the plurality of students and data on the scores of the target examinations. The teacher data may include at least one of the data on the student's experience and the data on the student's attributes for each of the plurality of students.

As illustrated below, the model generation unit 513 generates a score prediction model based on at least the attendance history of a plurality of students included in the teacher data and the scores of the target test by the plurality of students. Since the plurality of student data are updated as described above, the model generation unit 513 generates a score prediction model based on the updated teacher data. It should be noted that generating the score prediction model includes updating the score prediction model. For example, the model generation unit 513 may generate a score prediction model in a nighttime batch, or may generate a score prediction model in real time at the timing of predicting the learning effect.

The model generation unit 513 uses the score of the target test as an objective variable for generating a score prediction model. The model generation unit 513 uses each element for each lesson included in the attendance history as a feature amount for generating a score prediction model. There is a certain correlation between each element of each lesson included in the course history and the score of the target test.

For example, the feature quantity is the progress of the lecture included in the attendance history. If the lecture has been taken, the learning time of the student may tend to be longer than if the lecture has not been taken. As the learning time increases, the score of the target test may tend to increase. For example, the feature amount is the playback time of a moving image or audio included in the attendance history. As the video or audio playback time increases, the student's learning time can tend to increase. As the learning time increases, the score of the target test may tend to increase. For example, the feature quantity is the usage time of the lecture included in the attendance history. The longer the lecture is used, the longer the student's learning time can tend to be. As the learning time increases, the score of the target test may tend to increase. For example, the feature quantity is the progress of the exercise included in the attendance history. Students who have taken the exercises may tend to spend more time studying than those who have not taken the exercises. As the learning time increases, the score of the target test may tend to increase. For example, the feature quantity is the score or correct answer rate of the exercise included in the attendance history. Exercise questions consist of past questions and questions that are likely to appear in the target exam. As the score or correct answer rate of the exercise increases, the students may be more likely to get a high score in the target test as well. Therefore, as the score of the exercise or the percentage of correct answers increases, the score of the target test may tend to increase. For example, the feature amount is the usage time of the exercise included in the attendance history. As the usage time of the exercise increases, the learning time of the students may tend to increase. As the learning time increases, the score of the target test may tend to increase.

The model generation unit 513 may use each element constituting the student's experience as a feature amount for generating a score prediction model. There is a certain correlation between each element that constitutes the student's experience and the score of the target examination. For example, the feature quantity is the number of times the subject test is taken, which is included in the experience of the student. As the number of times a subject exam is taken increases, students may tend to become accustomed to taking the subject exam. As students become accustomed to taking the target exam, the score of the target exam can tend to increase. For example, the feature quantity is the past result of the target test included in the experience of the student. The score of the target test may tend to be higher than the past results of the target test. For example, the feature quantity is the number of times a test is taken that is different from the target test included in the student's experience. As the number of times an exam is taken increases, students may tend to become accustomed to taking the exam. As students become accustomed to taking the exam, the score of the target exam may tend to increase.

The model generation unit 513 may use each element constituting the attribute of the student as a feature amount for generating the score prediction model. There is a certain correlation between each element that constitutes the attributes of the students and the score of the target test. For example, the feature quantity is the age included in the attributes of the students. As we get older, our memory tends to decline. Therefore, as the age increases, the score of the target test may tend to decrease. For example, the feature quantity is the gender included in the attributes of the students. The exam may be for men or for women. Therefore, the scores of the target tests may tend to differ depending on the gender. For example, the feature quantity is the occupation and industry included in the attributes of the student. Exams are highly relevant for each occupation and industry. For example, people in ICT-related professions and industries may tend to be better suited for information processing-related exams. Therefore, the scores of the target tests may tend to differ depending on the occupation and industry.

The model generation unit 513 estimates the relationship between each feature amount with respect to the objective variable by machine learning, and generates a score prediction model. The score prediction model uses various variables corresponding to each feature amount in order to obtain the predicted score. For example, the score prediction model uses whether or not each lecture has been taken as a variable. For example, the score prediction model uses the score or correct answer rate of each exercise as a variable.

For example, machine learning is supervised learning. Machine learning may generate a predictive model by a regression method such as linear regression and decision tree. The regression method is not limited to these. Machine learning may generate predictive models by classification methods such as neural networks, random forests, support vector machines and deep learning. The classification method is not limited to these.

Although the model generation unit 513 has described an example of generating a score prediction model using a plurality of student data by machine learning, the score prediction model may be generated without using machine learning. For example, the model generation unit 513 selects any one student data having a correlation with the attendance history of the target person data from a plurality of student data, and creates a score prediction model based on the selected student data. It may be generated.

The model storage unit 514 stores information about the score prediction model generated by the model generation unit 513 in the storage device 54.

The prediction request acquisition unit 515 acquires a prediction request for the learning effect from the server 4 via the communication interface 55. The prediction request acquisition unit 515 acquires information for identifying the target person and information for specifying the target lesson included in the prediction request for the learning effect.

The target data request output unit 516 outputs a data request regarding the target person to the server 3 via the communication interface 55, as illustrated below. In the following, the data regarding the target person is also referred to as the target person data. For example, the target data request output unit 516 refers to the information that identifies the target person included in the learning effect prediction request. The target data request output unit 516 identifies the target person and outputs the target person data request to the server 3. The request for subject data includes information that identifies the subject.

The target data acquisition unit 517 acquires the target person data from the server 3 via the communication interface 55. The target person data includes data related to the attendance history of the target person. The subject data may include at least one of data on the student's experience with respect to the subject and data on the attributes of the student. The target person data is transmitted from the server 3 to the server 5 as a response to the request for the target person data.

The prediction unit 518 predicts the learning effect as illustrated below.
The learning effect includes any one of the first to fifth learning effects exemplified below.

The first learning effect is the expected increase score of the target test. The second learning effect is the predicted score of the target test when the predetermined attendance criteria are met. In the following, the predicted score of the target test when the predetermined attendance criteria are satisfied is also referred to as the first predicted score. The third learning effect is the first predicted score and the predicted score of the target test when the predetermined attendance criteria are not met. In the following, the predicted score of the target test when the prescribed attendance criteria are not met is also referred to as the second predicted score. The fourth learning effect is the score up to the target point when the predetermined attendance criteria are satisfied. The fifth learning effect is the possibility of reaching the target point when the predetermined attendance criteria are met.

The prediction of the first learning effect will be described. The expected increase score is the increase in the score of the target test that is expected to increase as the subject takes the target lesson.
First, the prediction unit 518 predicts the first predicted score by using the score prediction model. Next, the prediction unit 518 predicts the second predicted score by using the score prediction model. Next, the prediction unit 518 obtains the difference between the first prediction score and the second prediction score. The difference corresponds to the expected rise score. As a result, the prediction unit 518 can acquire information indicating the first learning effect.

The second prediction of the learning effect will be described. The prediction unit 518 predicts the first predicted score by using the score prediction model. As a result, the prediction unit 518 can acquire information indicating the second learning effect.

The prediction of the third learning effect will be described. First, the prediction unit 518 predicts the first predicted score by using the score prediction model. Next, the prediction unit 518 predicts the second predicted score by using the score prediction model. As a result, the prediction unit 518 can acquire information indicating the third learning effect.

The fourth prediction of the learning effect will be described. The target score used in the fourth learning effect is a score for passing the target test. For example, if the passing score of the target test is fixed, the target score may be a fixed passing score or a score higher than the fixed passing score. For example, if the passing score of the target test fluctuates, the target score may be the average score of a plurality of past passing scores, or may be a score higher than the average score. First, the prediction unit 518 predicts the first predicted score by using the score prediction model. Next, the prediction unit 518 predicts the first predicted score by using the score prediction model. Next, the prediction unit 518 obtains the difference from the first prediction score to the target point. The difference corresponds to the score up to the target point. As a result, the prediction unit 518 can acquire information indicating the fourth learning effect.

The fifth prediction of the learning effect will be described. The target point used in the fifth learning effect may be the same as the target point used in the fourth learning effect. First, the prediction unit 518 predicts the first predicted score by using the score prediction model. Next, the prediction unit 518 predicts the possibility of reaching the target point based on the first predicted score and the target point. In one example, the likelihood of reaching a target point is the pass rate. The pass rate is the ratio of the first predicted score to the target score. In this example, the prediction unit 518 obtains the ratio of the first predicted score to the target point. In another example, the likelihood of reaching the target point is a pass decision. The pass judgment is a rank selected from a plurality of ranks. For example, the rank of a plurality of stages is set according to the deviation of the first predicted score from the target point or the ratio of the first predicted score to the target point. For example, the rank of a plurality of stages includes, but is not limited to, a rank of four stages of A, B, C, and D. In this example, the prediction unit 518 selects a rank corresponding to the difference from the first prediction score to the target point from the ranks of a plurality of stages. Instead, the prediction unit 518 may select a rank corresponding to the ratio of the first predicted score to the target point from the ranks of a plurality of stages. As a result, the prediction unit 518 can acquire information indicating the fifth learning effect.

The prediction information output unit 519 outputs the learning effect information acquired by the prediction unit 518 to the server 4 via the communication interface 55. The learning effect information is transmitted from the server 5 to the server 4 as a response to the learning effect prediction request.

An example of the composition of a plurality of lessons constituting the target course will be described.
FIG. 2 is a diagram showing a configuration example of a plurality of lessons constituting the target course.
The target course consists of x (2 or more) lessons. The x lessons consist of y (1 or more) lectures and y exercises. The number of lectures may differ from the number of exercises. Lectures and exercises are arranged alternately, but are not limited to this. Two or more lectures may be arranged in succession. Two or more exercises may be arranged in succession.

Prediction examples and display examples of learning effects will be explained separately for target lectures and target exercises.

An example of the target lecture will be explained.
Here, it is assumed that lessons 1 to 4 shown in FIG. 2 are already viewed lessons. Lessons 5 to x are assumed to be unviewed lessons. Lecture 3 of Lesson 5 shall be the target lecture.

First, an example of predicting the first learning effect in the target lecture will be described.
The prediction unit 518 predicts the first predicted score when the subject completes viewing of the lecture 3 which is the target lecture by using the score prediction model. The case where the target person completes the viewing of the lecture 3 which is the target lecture is an example of the case where the predetermined attendance criteria are satisfied. Here, the prediction unit 518 applies the data of the attendance history of the target person to the variables of the score prediction model for the lessons 1 to 4 which have been taken. The prediction unit 518 applies data indicating that the lecture has been taken to the variables of the score prediction model for the lecture 3 which is the target lecture. The prediction unit 518 applies empty data to the variables of the score prediction model of lessons 6 to x excluding the target lectures among the lessons not taken. Instead, the prediction unit 518 may apply default data to the variables of the score prediction model of lessons 6 to x excluding the target lecture among the lessons not taken. For example, if the lesson not taken is a lecture, the default data includes data indicating that the lesson has been taken. For example, if the lesson not taken is an exercise, the default data includes data indicating that the exercise score or correct answer rate is 0. Alternatively, if the lesson not taken is an exercise, the default data may include data that indicates the average score or correct answer rate of the exercise when the first attendance of multiple students. Good. The prediction unit 518 applies the above-mentioned data to the variables of the score prediction model and predicts the first prediction score.

The prediction unit 518 uses the score prediction model to predict the second predicted score when the subject is not watching the target lecture. If the target person does not watch the target lecture, this is an example of the case where the prescribed attendance criteria are not met. Here, the prediction unit 518 applies the data of the attendance history of the target person to the variables of the score prediction model for the lessons 1 to 4 which have been taken. The prediction unit 518 applies data indicating not attending to the variables of the score prediction model for the lecture 3 which is the target lecture. The prediction unit 518 applies empty data to the variables of the score prediction model for lessons 6 to x excluding the target lectures among the lessons not taken. Instead, the prediction unit 518 may apply the above-mentioned default data to the variables of the score prediction model of lessons 6 to x excluding the target lecture among the lessons not taken. The prediction unit 518 applies the above-mentioned data to the variables of the score prediction model and predicts the second prediction score.

It is preferable that the prediction unit 518 is the same except for the data applied to the variables of the score prediction model for the target lecture when predicting the first prediction score and the second prediction score. The reason is that by changing only the attendance criteria of the target lecture, the accuracy of the expected increase score brought about by attending the target lecture is improved.

Next, the prediction unit 518 obtains an ascending prediction score, which is the difference between the first prediction score and the second prediction score, and acquires information indicating the first learning effect. Since the data applied to the variables of the score prediction model is different for each student, the ascending prediction score is different for each student even in the same target lecture. Similarly, the expected increase score will differ from lecture to lecture even for the same student.

The second to fifth learning effect prediction examples in the target lecture are the same as the first learning effect prediction example described above. For example, the prediction unit 518 predicts the first prediction score as described above, and acquires information indicating the second learning effect. For example, the prediction unit 518 predicts the first predicted score and the second predicted score as described above, and acquires information indicating the third learning effect. For example, the prediction unit 518 predicts the first predicted score as described above, obtains the difference from the first predicted score to the target point, and acquires information indicating the fourth learning effect. For example, the prediction unit 518 predicts the first predicted score as described above, obtains the possibility of reaching the target point based on the first predicted score and the target point, and acquires information indicating the fifth learning effect. To do.

Next, a display example of the first learning effect for the target lecture will be described.
FIG. 3 is a diagram showing a display example of the first learning effect for the target lecture displayed on the display device 27.

The display control unit 212 causes the display device 27 to display the expected increase score (for example, 8 points) together with the content of the target lecture at the timing when the student starts the target lesson. The display control unit 212 may display a predetermined attendance standard on the display device 27 in association with the increase prediction score. For example, the display control unit 212 causes the display device 27 to display a message indicating that the ascending predicted score is related to the completion of attending the target lecture in association with the ascending predicted score. The display mode such as the position, size, and display method of the message may be any one that attracts the attention of the target person, and is not limited. The subject can easily grasp that the expected increase score can be obtained by taking the target lecture.

Instead of, or together with this, the display control unit 212 causes the display device 27 to display the expected increase score together with the content of the target lecture at the timing when the student completes the target lesson. The display control unit 212 may display a predetermined attendance standard on the display device 27 in association with the increase prediction score. For example, the display control unit 212 similarly causes the display device 27 to display a message indicating that the ascending predicted score is related to the completion of attending the target lecture in association with the ascending predicted score. The subject can easily grasp that he / she was able to obtain the expected increase score by attending the target lecture.

The display examples of the second to fifth learning effects are the same as the display examples of the first learning effect described above. When the acquisition unit 211 acquires learning effect information including two or more of the first to fifth learning effects, the display control unit 212 displays two or more of the first to fifth learning effects. It may be displayed on the device 27.

An example of the target exercise will be described.
Here, it is assumed that lessons 1 to 5 shown in FIG. 2 are already viewed lessons. Lessons 6 to x are assumed to be unviewed lessons. Exercise 3 of Lesson 6 shall be the target exercise.

First, an example of predicting the first learning effect in the target exercise will be described.
The prediction unit 518 predicts the first predicted score when the subject obtains the first score or more or the first correct answer rate or more in the exercise 3 which is the target exercise by using the score prediction model. The case where the subject obtains the first score or more or the first correct answer rate or more in the exercise 3 which is the target exercise is an example of the case where the predetermined attendance criteria are satisfied. Here, the prediction unit 518 applies the data of the attendance history of the target person to the variables of the score prediction model for the lessons 1 to 5 that have already been taken. The prediction unit 518 applies data indicating the first score or the first correct answer rate to the variables of the score prediction model for the exercise 3 which is the target exercise. The prediction unit 518 applies empty data to the variables of the score prediction model of lessons 7 to x excluding the target exercises among the lessons not taken. Instead, the prediction unit 518 may apply the above-mentioned default data to the variables of the score prediction model of lessons 7 to x excluding the target exercise among the lessons not taken. The prediction unit 518 applies the above-mentioned data to the variables of the score prediction model and predicts the first prediction score.

The prediction unit 518 uses the score prediction model to predict the second predicted score when the subject obtains a second score or more or a second correct answer rate or more in the target exercise 3, exercise 3. When the subject obtains a second score or higher or a second correct answer rate or higher in Exercise 3, which is the target exercise, it is an example of a case where the prescribed attendance criteria are not met. The second score is a score lower than the first score. The second correct answer rate is a lower correct answer rate than the first correct answer rate. The second score or the second correct answer rate may be 0, or may be the average value of the scores or correct answer rates of a plurality of students at the time of the first attendance. Here, the prediction unit 518 applies the data of the attendance history of the target person to the variables of the score prediction model for the lessons 1 to 5 that have already been taken. The prediction unit 518 applies data indicating the second score or the second correct answer rate to the variables of the score prediction model for the exercise 3 which is the target exercise. The prediction unit 518 applies empty data to the variables of the score prediction model of lessons 7 to x excluding the target exercises among the lessons not taken. Instead, the prediction unit 518 may apply the above-mentioned default data to the variables of the score prediction model of lessons 7 to x excluding the target exercise among the lessons not taken. It is preferable that the prediction unit 518 is the same except for the data applied to the variables of the score prediction model for the target exercise when predicting the first prediction score and the second prediction score. The prediction unit 518 applies the above-mentioned data to the variables of the score prediction model and predicts the second prediction score.

The prediction unit 518 obtains an ascending prediction score, which is the difference between the first prediction score and the second prediction score, and acquires information indicating the first learning effect. Since the data applied to the variables of the score prediction model is different for each student, the ascending prediction score is different for each student even in the same target exercise. Similarly, the expected increase score will vary from exercise to exercise, even for the same student.

The second to fifth learning effect prediction examples in the target exercise are the same as the first learning effect prediction examples described above. For example, the prediction unit 518 predicts the first prediction score as described above, and acquires information indicating the second learning effect. For example, the prediction unit 518 predicts the first predicted score and the second predicted score as described above, and acquires information indicating the third learning effect. For example, the prediction unit 518 predicts the first predicted score as described above, obtains the difference from the first predicted score to the target point, and acquires information indicating the fourth learning effect. For example, the prediction unit 518 predicts the first predicted score as described above, obtains the possibility of reaching the target point based on the first predicted score and the target point, and acquires information indicating the fifth learning effect. To do.

Here, an example of setting the first score or the first correct answer rate will be described by taking Exercise 3 as an example. The maximum score for Exercise 3 shall be 100 points.
The prediction unit 518 predicts a plurality of predicted scores in the range of 0 to 100 points. For example, the prediction unit 518 predicts the predicted score at a predetermined score interval (for example, 5 points).

FIG. 4 is a diagram illustrating the relationship between the score of Exercise 3 and the predicted score.
As shown in FIG. 4, the growth rate of the predicted score decreases as the score of Exercise 3 increases. For example, the growth rate is the increase in the predicted score with respect to a predetermined score interval. The growth rate is related to the slope of the graph of the relationship between the score of Exercise 3 and the predicted score. When the score of Exercise 3 is in the range of 80 to 100 points, the predicted score is almost the same. Therefore, the prediction unit 518 sets a score (for example, 80 points) at which the growth rate of the predicted score predicted according to the score of the target exercise does not satisfy the predetermined growth rate as the first score. That is, the first score is a score that considers the balance between the predicted score expected by attending the target exercise and the maintenance of the motivation of the target person. Similarly, the prediction unit 518 sets the correct answer rate at which the growth rate of the predicted score of the target test predicted according to the correct answer rate of the exercise does not satisfy the predetermined growth rate as the first correct answer rate. The relationship between the exercise score and the predicted score differs for each student. Therefore, the first score or the first correct answer rate differs for each student even in the same target exercise. The relationship between the exercise score and the predicted score is different for each exercise. Therefore, the first score or the first correct answer rate differs for each exercise even for the same student. The first score or the first correct answer rate may be determined based on the elongation rate as described above, but may be a preset fixed value (for example, 60 points or 60%). .. The fixed values may be common or different for each exercise. The fixed value may be arbitrarily adjustable.

The prediction unit 518 has described an example of predicting one first predicted score when a predetermined attendance standard is satisfied, but the present invention is not limited to this. The prediction unit 518 may predict a plurality of first prediction scores as illustrated below. In this example, the predetermined attendance criteria include a plurality of attendance criteria according to a plurality of first scores or a plurality of first correct answer rates. For example, the plurality of first scores or the plurality of first correct answer rates may or may not be equal intervals such as scores at 5-point intervals or correct answer rates at 5% intervals. The plurality of first scores or the plurality of first correct answer rates include the score or the correct answer rate in which the growth rate of the predicted score of the target test predicted according to the score or the correct answer rate of the exercise does not satisfy the predetermined growth rate. You may be.

The prediction unit 518 predicts a plurality of first predicted scores according to a plurality of first scores or a plurality of first correct answer rates. The prediction unit 518 obtains the difference between the plurality of first predicted scores and the second predicted score, and predicts a plurality of predicted ascending scores for each of the plurality of attendance criteria. The prediction unit 518 acquires information indicating the first learning effect including the increase prediction score for each of the plurality of attendance criteria. In addition, the prediction unit 518 can acquire the second to fifth learning effects including the values for each of the plurality of attendance criteria as well as the first learning effect.

Next, a display example of the first learning effect for the target exercise will be described.
FIG. 5 is a diagram showing a display example of the first learning effect for the target exercise displayed on the display device 27.

The display control unit 212 displays the expected increase score (for example, 10 points) together with the content of the target exercise at the timing when the student starts the target lesson. The display control unit 212 may display a predetermined attendance standard on the display device 27 in association with the increase prediction score. For example, the display control unit 212 causes the display device 27 to display a message indicating that the ascending predicted score is related to the acquisition of the first score (for example, 80 points) or more in association with the ascending predicted score. The display control unit 212 may display a message about the first correct answer rate on the display device 27 instead of the first score. The display mode such as the position, size, and output of the message may be any one that attracts the attention of the target person, and is not limited. The subject can easily grasp the score or correct answer rate of the target exercise, which is the target for obtaining the expected increase score, and can approach the target exercise.

The display control unit 212 may display the graph showing the relationship between the score of the target exercise or the correct answer rate and the predicted score together with the predicted ascending score on the display device 27. In FIG. 5, the second predicted score is an example in which the second score is set to 0. In this example, the prediction information acquisition unit 414 of the server 4 acquires information indicating a plurality of scores of the exercise 3 or a plurality of prediction scores of the correct answer rate from the server 5. The output unit 415 of the server 4 outputs the information indicating the plurality of scores of the exercise 3 or the predicted scores of the plurality of correct answer rates to the terminal 2 together with the learning effect information including the first learning effect. The acquisition unit 211 of the terminal 2 acquires from the server 4 information indicating a plurality of scores of the exercise 3 or a plurality of predicted scores of the correct answer rate together with the learning effect information including the first learning effect. The subject can easily grasp the transition of the expected score according to the score of the target exercise or the correct answer rate.

When the prediction unit 518 of the server 5 predicts the first score or a plurality of ascending prediction scores according to a plurality of attendance criteria having different correct answer rates, the display control unit 212 determines the plurality of first scores. Alternatively, a plurality of predicted increase scores may be displayed on the display device 27 in association with each of the plurality of first correct answer rates. In this example, the prediction information acquisition unit 414 of the server 4 acquires the learning effect information including the first learning effect from the server 5. The first learning effect includes a plurality of first predicted scores for each of the plurality of attendance criteria. The output unit 415 of the server 4 outputs the learning effect information including the first learning effect to the terminal 2. The acquisition unit 211 of the terminal 2 acquires the learning effect information including the first learning effect from the server 4. The subject can easily grasp the score of the target exercise or the expected increase score according to the correct answer rate.

Instead of, or together with this, the display control unit 212 causes the display device 27 to display the expected increase score together with the content of the target exercise at the timing when the student completes the target lesson. The display control unit 212 may display a predetermined attendance standard on the display device 27 in association with the increase prediction score. For example, the display control unit 212 causes the display device 27 to display a message indicating that the ascending predicted score is related to the acquisition of the first score or more in association with the ascending predicted score. The display control unit 212 may display a message about the first correct answer rate on the display device 27 instead of the first score. The subject can easily grasp the score or correct answer rate of the target exercise, which is the target for obtaining the expected increase score, and can review the target exercise.

As described above, the display control unit 212 may display the graph showing the relationship between the score or the correct answer rate of the target exercise and the predicted score together with the ascending predicted score on the display device 27. As described above, the display control unit 212 may display a plurality of predicted ascending scores on the display device 27 in association with each of the plurality of first scores or the plurality of first correct answer rates. The display control unit 212 predicts an increase corresponding to the highest first score or first correct answer rate that exceeds the target person's score or correct answer rate among the plurality of first scores or the plurality of first correct answer rates. The score may be displayed on the display device 27. The subject can easily grasp the score of the target exercise that he / she has acquired or the expected increase score obtained by the correct answer rate.

The display examples of the second to fifth learning effects are the same as the display examples of the first learning effect described above. When the acquisition unit 211 acquires learning effect information including two or more of the first to fifth learning effects, the display control unit 212 displays two or more of the first to fifth learning effects. It may be displayed on the device 27.

An operation example related to the above-mentioned score prediction model will be described.
FIG. 6 is a sequence diagram showing an operation example related to the score prediction model. In addition, one or more operations shown in FIG. 6 may be changed or omitted as appropriate.

The teacher data request unit 511 outputs a request for teacher data to the server 3 via the communication interface 45 (step S101). The processor 31 acquires a request for teacher data from the server 5 via the communication interface 35 (step S102). The processor 31 extracts the teacher data from the storage device 34 and outputs the teacher data to the server 5 via the communication interface 35 (step S103). The teacher data acquisition unit 512 acquires teacher data from the server 3 via the communication interface 55 (step S104). The model generation unit 513 generates a score prediction model based on the teacher data (step S105). The model storage unit 514 stores the score prediction model in the storage device 54 (step S106).

An operation example related to the above-mentioned learning effect will be described.
FIG. 7 is a sequence diagram showing an operation example related to the learning effect. In addition, one or more operations shown in FIG. 7 may be changed or omitted as appropriate.

The processor 21 outputs a start request of the target lesson to the server 4 via the communication interface 25 in response to the input of the start of the target lesson by the target person in the input device 26 (step S201). The start request acquisition unit 411 acquires the start request of the target lesson from the terminal 2 via the communication interface 45 (step S202). The target lesson specifying unit 412 specifies the target lesson with reference to the target lesson start request (step S203). The prediction request output unit 413 outputs a prediction request for the learning effect to the server 5 via the communication interface 45 (step S204).

The prediction request acquisition unit 515 acquires a prediction request for the learning effect from the server 4 via the communication interface 55 (step S205). The target data request output unit 516 outputs a request for target person data to the server 3 via the communication interface 55 (step S206).

The processor 31 acquires a request for target person data from the server 5 via the communication interface 35 (step S207). The processor 31 extracts the target person data from the storage device 34 and outputs the target person data to the server 5 via the communication interface 35 (step S208).

The target data acquisition unit 517 acquires the target person data from the server 3 via the communication interface 55 (step S209). The prediction unit 518 predicts the learning effect by using the score prediction model (step S210). The prediction information output unit 519 outputs the learning effect information to the server 4 via the communication interface 55 (step S211).

The prediction information acquisition unit 414 acquires learning effect information from the server 5 via the communication interface 45 (step S212). The output unit 415 outputs the learning effect information to the terminal 2 via the communication interface 45 (step S213). The acquisition unit 211 acquires the learning effect information from the server 4 via the communication interface 25 (step S214). The display control unit 212 causes the display device 27 to display the learning effect (step S215).

The output unit 415 may switch the necessity of outputting the learning effect information according to the learning effect. In this example, the output unit 415 compares the learning effect with a predetermined reference. The predetermined standard is a standard for determining the magnitude of the learning effect, and the predetermined standard can be arbitrarily changed. The output unit 415 switches the necessity of outputting the learning effect information according to the comparison result. For example, in the first learning effect, the output unit 415 compares the ascending prediction score with a predetermined criterion. When the rise prediction score is equal to or higher than a predetermined reference, the output unit 415 determines that it is necessary to display the rise prediction score, and outputs the learning effect information to the terminal 2. When the ascending predicted score is less than a predetermined reference, the output unit 415 determines that the display of the ascending predicted score is unnecessary, and omits the output of the learning effect information. The output unit 415 may compare the first predicted score with a predetermined reference for the second learning effect. The output unit 415 may compare at least one of the first predicted score and the second predicted score with a predetermined criterion for the second learning effect. The output unit 415 may compare the score up to the target point with a predetermined reference for the third learning effect. The output unit 415 may compare the possibility of reaching the target point with a predetermined reference.

According to the embodiment, the server 4 can output the learning effect information to the terminal 2. The terminal 2 can display the learning effect on the display device 27. The learning effect differs from lesson to lesson, but the subject can be aware of the learning effect from lesson to lesson. By being aware of the learning effect, the subject can work on the target lesson with different strengths according to the learning effect. For example, the subject can avoid spending too much time on lessons that contribute less to the score of the subject exam. Therefore, the target person can take each lesson efficiently. In addition, since the subject can be aware of the learning effect as a short-term goal for each lesson, the motivation to take each lesson can be maintained. In this way, the server 4 and the terminal 2 can urge the subject to take desirable actions to pass the subject test.

According to the embodiment, the learning effect includes at least one of the above-mentioned first to fifth learning effects. The subject can grasp the learning effect of the target lesson by various indexes.

According to the embodiment, the ascending predicted score is the difference between the first predicted score and the second predicted score. As a result, the server 4 can acquire a highly accurate ascending prediction score related to the target lesson.

According to the embodiment, the predetermined attendance criterion is that the subject completes the viewing of the subject lecture. According to this example, the server 4 can acquire a highly accurate increase prediction score related to the completion of viewing of the target lecture. According to the embodiment, the predetermined attendance standard is that the subject obtains the first score or more or the first correct answer rate or more in the target exercise. According to this example, the server 4 can acquire a highly accurate increase prediction score related to the acquisition of the first score or more or the first correct answer rate or more of the target exercise.

According to the embodiment, the first predicted score and the ascending predicted score are predicted for each of a plurality of attendance criteria. The subject can easily grasp the learning effect according to the score or the correct answer rate of the target exercise.

According to the embodiment, the first score or the first correct answer rate is a score or a correct answer rate in which the growth rate of the predicted score predicted according to the score or the correct answer rate of the target exercise does not satisfy the predetermined growth rate. .. Alternatively, the plurality of first scores or the plurality of first correct answer rates include the score or the correct answer rate in which the growth rate of the predicted score predicted according to the score or the correct answer rate of the exercise does not satisfy the predetermined growth rate. For example, if the score of the target exercise is perfect, the learning effect is expected to be maximum. However, if the subject aims to get a perfect score in all the exercises, the subject's motivation will be reduced. On the other hand, even if the score of the target exercise is not perfect, the learning effect may be almost the same as the case where the score of the target exercise is perfect. The target person can take each exercise without difficulty by aiming for the first score in consideration of the balance between the learning effect point expected by taking the target exercise and the maintenance of the motivation of the target person.

According to the embodiment, the learning effect is predicted at least based on the attendance history of the subject and the different attendance criteria of the subject lesson. As described above, the attendance history of the subject and the attendance criteria of the subject lesson are used to predict the learning effect by the score prediction model. As a result, the server 4 can acquire a highly accurate ascending prediction score. According to the embodiment, the learning effect is predicted at least based on the attendance history of the subject, the different attendance criteria of the subject lesson, the attendance history of the plurality of students, and the scores of the target test by the plurality of students. The attendance history of a plurality of students and the scores of the target test by the plurality of students are used to generate a score prediction model as described above. As a result, the server 4 can acquire a highly accurate ascending prediction score.

According to the embodiment, the server 4 outputs an instruction to the terminal 2 to display the learning effect at the start or completion of the target lesson. As a result, the target person can be aware of how much the target lesson contributes to the score of the target test before taking the target lesson. The target person can work on the target lesson with different strengths according to the learning effect, and can increase the motivation to take the target lecture. After taking the target lesson, the target person can be aware of how much the target lesson contributes to the score of the target test. The target person can work on the review of the target lesson by adjusting the strength according to the learning effect.

According to the embodiment, the server 4 outputs information indicating a predetermined attendance standard to the terminal 2. As a result, the target person can grasp the predetermined learning standard displayed on the display device 27 and take the target lesson while being aware that the predetermined learning standard is satisfied in order to obtain the learning effect.

According to the embodiment, the server 4 switches the necessity of outputting the learning effect information according to the learning effect. As a result, the server 4 can notify the target person of the magnitude of the effect obtained in the target lesson according to the display or non-display of the learning effect. The target person can take each lesson efficiently by setting the strength of the approach to each lesson according to the display or non-display of the learning effect.

Although the example in which the server 4 is a device separate from the server 2 and the server 5 has been described, the present invention is not limited to this. The server 4 may be a device integrated with at least one of the server 2 and the server 5. When the server 4 is composed of a device integrated with the server 2, the server 4 stores data related to the participants of the target course. When the server 4 is composed of a device integrated with the server 5, the processor 41 of the server 4 appropriately implements each part described with reference to the processor 51.

In short, the present invention is not limited to the present embodiment as it is, and at the implementation stage, the constituent elements can be modified and embodied without departing from the gist thereof. In addition, various inventions can be formed by an appropriate combination of a plurality of constituent elements disclosed in the present embodiment. For example, some components may be deleted from all the components shown in the present embodiment. Furthermore, the components of different embodiments may be combined as appropriate.

1 ... Course distribution system, 2 ... Terminal, 3 ... Server, 4 ... Server, 5 ... Server, 21 ... Processor, 22 ... ROM, 23 ... RAM, 24 ... Storage device, 25 ... Communication interface, 26 ... Input device, 27 ... Display device, 31 ... Processor, 32 ... ROM, 33 ... RAM, 34 ... Storage device, 35 ... Communication interface, 41 ... Processor, 42 ... ROM, 43 ... RAM, 44 ... Storage device, 45 ... Communication interface, 51 ... Processor, 52 ... ROM, 53 ... RAM, 54 ... Storage device, 55 ... Communication interface, 211 ... Acquisition unit, 212 ... Display control unit, 411 ... Start request acquisition unit, 412 ... Target lesson identification unit, 413 ... Prediction request output Unit, 414 ... Prediction information acquisition unit, 415 ... Output unit, 511 ... Teacher data request unit, 512 ... Teacher data acquisition unit, 513 ... Model generation unit, 514 ... Model storage unit, 515 ... Prediction request acquisition unit, 516 ... Target Data request output unit, 517 ... Target data acquisition unit, 518 ... Prediction unit, 519 ... Prediction information output unit.

In short, the present invention is not limited to the present embodiment as it is, and at the implementation stage, the components can be modified and embodied without departing from the gist thereof. In addition, various inventions can be formed by an appropriate combination of a plurality of constituent elements disclosed in the present embodiment. For example, some components may be deleted from all the components shown in the present embodiment. Furthermore, the components of different embodiments may be combined as appropriate.
Hereinafter, the description of the scope of claims at the time of filing the application of the present application will be added.
[C1]
Of the multiple lessons that make up the course related to the exam, a specific part that identifies the lesson to be taken by the target person, and
The acquisition department that acquires information showing the learning effect of taking the lessons to be taken, and
An output unit that outputs information indicating the learning effect to the target person's terminal, and
Information processing device equipped with.
[C2]
Of the multiple lessons that make up the course related to the exam, the acquisition department that acquires information showing the learning effect of taking the target lesson, and the acquisition department
A display control unit that displays the learning effect on the display unit,
A terminal equipped with.
[C3]
The learning effect is the ascending predicted score of the test, the first predicted score of the test when the predetermined attendance standard is satisfied, the first predicted score, and the first of the test when the predetermined attendance standard is not satisfied. Includes at least one of the predicted score of 2, the score up to the target point when the predetermined attendance standard is met, and the possibility of reaching the target point when the predetermined attendance standard is met, [ The information processing device according to [C1] or the terminal according to [C2].
[C4]
The information processing apparatus according to [C3] or the terminal according to [C3], wherein the increase prediction score is a difference between the first prediction score and the second prediction score.
[C5]
The information processing device according to [C3] or the terminal according to [C3], wherein the score up to the target point is the difference from the first predicted score to the target point.
[C6]
The information processing apparatus according to [C3] or the terminal according to [C3], which is predicted based on the first predicted score and the target point, the possibility of reaching the target point.
[C7]
The information processing apparatus according to any one of [C3] to [C6], wherein when the lesson to be taken is a lecture, the predetermined attendance standard is that the subject completes viewing of the lecture. Alternatively, the terminal according to any one of [C3] to [C6].
[C8]
When the lesson to be taken is an exercise, the predetermined attendance standard is that the subject obtains a predetermined score or more or a predetermined correct answer rate or more in the exercise, whichever is [C3] to [C6]. The information processing apparatus according to item 1 or the terminal according to any one of [C3] to [C6].
[C9]
The predetermined score or the predetermined correct answer rate is a score or a correct answer rate in which the growth rate of the predicted score of the test predicted according to the score or the correct answer rate of the exercise does not satisfy the predetermined growth rate, [C8. ] The information processing apparatus described in [C8] or the terminal described in [C8].
[C10]
The predetermined attendance criteria include a plurality of attendance criteria according to a plurality of scores or a plurality of correct answer rates.
The first predicted score is the information processing device according to [C8] or the terminal according to [C8], which is predicted for each of the plurality of attendance criteria.
[C11]
The predetermined attendance criteria include a plurality of attendance criteria according to a plurality of scores or a plurality of correct answer rates.
The information processing device according to [C8] or the terminal according to [C8], which is predicted for each of the plurality of attendance criteria, the increase prediction score.
[C12]
The plurality of scores or the plurality of correct answer rates include a score or a correct answer rate in which the growth rate of the predicted score of the test predicted according to the score or the correct answer rate of the exercise does not satisfy the predetermined growth rate [C10. ] Or the information processing apparatus according to [C11] or the terminal according to [C10] or [C11].
[C13]
The information processing device according to [C1] or the terminal according to [C2], which is predicted based on at least the attendance history of the subject and the different attendance criteria of the subject lesson.
[C14]
The learning effect is predicted based on at least the attendance history of the subject, different attendance criteria of the subject lesson, the attendance history of a plurality of participants, and the score of the examination by the plurality of participants [C1]. The information processing apparatus according to the above or the terminal according to [C2].
[C15]
The information processing device according to [C1], wherein the output unit outputs an instruction to display the learning effect on the terminal at the start or completion of the lesson to be taken.
[C16]
The information processing device according to any one of [C3] to [C6], wherein the output unit outputs information indicating the predetermined attendance standard to the terminal.
[C17]
The information processing apparatus according to [C1], wherein the output unit switches the necessity of outputting information indicating the learning effect according to the learning effect.

Claims (17)

Of the multiple lessons that make up the course related to the exam, a specific part that identifies the lesson for the target person and
The acquisition department that acquires information showing the learning effect of taking the lessons to be taken, and
An output unit that outputs information indicating the learning effect to the target person's terminal, and
Information processing device equipped with. Of the multiple lessons that make up the course related to the exam, the acquisition department that acquires information showing the learning effect of taking the target lesson, and the acquisition department
A display control unit that displays the learning effect on the display unit,
A terminal equipped with. The learning effect is the ascending predicted score of the test, the first predicted score of the test when the predetermined attendance standard is satisfied, the first predicted score, and the first of the test when the predetermined attendance standard is not satisfied. Claims including at least one of the predicted score of 2, the score up to the target point when the predetermined attendance standard is met, and the possibility of reaching the target point when the predetermined attendance standard is met. The information processing device according to item 1 or the terminal according to claim 2. The information processing apparatus according to claim 3 or the terminal according to claim 3, wherein the increase prediction score is a difference between the first predicted score and the second predicted score. The information processing device according to claim 3 or the terminal according to claim 3, wherein the score up to the target point is a difference from the first predicted score to the target point. The information processing apparatus according to claim 3 or the terminal according to claim 3, wherein the possibility of reaching the target point is predicted based on the first predicted score and the target point. The information processing device or claim according to any one of claims 3 to 6, wherein when the lesson to be taken is a lecture, the predetermined attendance standard is that the subject completes viewing of the lecture. The terminal according to any one of items 3 to 6. When the lesson to be taken is an exercise, the predetermined attendance criterion is any one of claims 3 to 6, wherein the subject obtains a predetermined score or more or a predetermined correct answer rate or more in the exercise. The information processing apparatus according to the item or the terminal according to any one of claims 3 to 6. The predetermined score or the predetermined correct answer rate is a score or a correct answer rate in which the growth rate of the predicted score of the test predicted according to the score or the correct answer rate of the exercise does not satisfy the predetermined growth rate. The information processing apparatus according to 8 or the terminal according to claim 8. The predetermined attendance criteria include a plurality of attendance criteria according to a plurality of scores or a plurality of correct answer rates.
The information processing device according to claim 8 or the terminal according to claim 8, wherein the first predicted score is predicted for each of the plurality of attendance criteria. The predetermined attendance criteria include a plurality of attendance criteria according to a plurality of scores or a plurality of correct answer rates.
The information processing device according to claim 8 or the terminal according to claim 8, wherein the increase prediction score is predicted for each of the plurality of attendance criteria. The plurality of scores or the plurality of correct answer rates include a score or a correct answer rate in which the growth rate of the predicted score of the test predicted according to the score or the correct answer rate of the exercise does not satisfy a predetermined growth rate. The information processing apparatus according to 10 or 11, or the terminal according to claim 10 or 11. The information processing device according to claim 1 or the terminal according to claim 2, wherein the learning effect is predicted at least based on the attendance history of the subject and different attendance criteria of the lesson subject to attendance. The learning effect is predicted based on at least the attendance history of the subject, different attendance criteria of the subject lesson, the attendance history of a plurality of participants, and the score of the examination by the plurality of participants, claim 1. The information processing apparatus according to claim 2 or the terminal according to claim 2. The information processing device according to claim 1, wherein the output unit outputs an instruction to display the learning effect on the terminal at the start or completion of the lesson to be taken. The information processing device according to any one of claims 3 to 6, wherein the output unit outputs information indicating the predetermined attendance standard to the terminal. The information processing device according to claim 1, wherein the output unit switches the necessity of outputting information indicating the learning effect according to the learning effect.