JP2022094804A - Learning assistance device, learning assistance method and computer program - Google Patents

Abstract

To provide a device that assists in selecting a concurrently applied school in conjunction with a preferred school.SOLUTION: An information processing device comprises: a large-capacity storage device 4 for holding the learning level of a test-taking student and the analysis result of question tendency of an examination which the test-taking student aims to pass; a unit database management part 104 for acquiring a plurality of learning contents which the test-taking student can learn in an attempt to pass an examination; and a growth rate calculation unit 107 for estimating the growth rate S of the learning level on the basis of the analysis result on the assumption that the test-taking student has learned a prescribed amount regarding each of the plurality of acquired learning contents. A control unit 103 causes one or more learning contents the growth rate of which, the one obtained by the growth rate calculation unit, increases a prescribed value or more to be displayed by an output device 3 that is visible to the test-taking student.SELECTED DRAWING: Figure 4

Description

The present invention relates to a learning support technique for supporting learning in an examination at a university or the like.

It is common for learners such as examinees to select the school of their choice when they go on to school, and to study with the goal of passing the selected school. Therefore, it is desirable to carry out learning with excellent time efficiency, such as obtaining a passing score while suppressing the learning time by conducting learning corresponding to the examination subjects of the selected school of choice and the tendency of questions in each subject.

As a learning support technique for examinees, for example, an educational support method disclosed in Patent Document 1 is known. In this educational support method, a user terminal such as a tablet is used to support the user in learning or utilizing a plurality of knowledge connections. More specifically, in this educational support method, when there is a common feature between the knowledge newly learned by the user and the knowledge of at least one of the knowledge already learned by the user, the terminal of the user has a common feature. , Present that the common features exist. In Patent Document 1, when a connection occurs between the learned knowledge by presenting a connection between a plurality of knowledge having a common feature, the student can efficiently know the existence of such a connection, and also. You can learn how to use it.

Patent Document 2 discloses a technique for showing a learner a weakness field in detail and providing learning support for guiding the learner to repeat learning for the weakness field until the weakness is overcome. In Patent Document 2, the learner can easily recognize the learning item which is his / her weak point, and can guide the learner to repeat the learning until this weak point is overcome. Therefore, it can contribute to the improvement of the learner's academic ability.

Japanese Unexamined Patent Publication No. 2019-6878 Japanese Unexamined Patent Publication No. 2011-70021

The goal of learning is for the examinees to acquire the academic ability to get a passing score in order to pass the school of their choice. On the other hand, the time that can be spent studying for the exam is limited. Therefore, in order to acquire the academic ability to pass the desired school in a limited study time, it is required to present the learner with the learning content that the learner should preferentially study.

The present invention has been made in view of the above problems, and an object of the present invention is to provide a learning support technique capable of presenting learning contents to be preferentially learned by learners such as examinees.

The learning support device according to one aspect of the present invention is a holding means for holding the learning level of the examinee and the analysis result of the questioning tendency of the examination that the examinee aims to pass, and the examinee can learn toward passing the examination. An acquisition means for acquiring a plurality of learning contents, and an estimation means for estimating the growth rate of the learning level when it is assumed that the examinee has learned a predetermined amount for each of the acquired plurality of learning contents. It is characterized by having a presenting means for displaying one or more of the learning contents whose elongation width is larger than a predetermined value on a display device that can be visually recognized by the examinee.

According to the present invention, it is possible to present to the examinee information that the examinee should learn with true priority in order to achieve the goal.

Explanatory drawing of subjects and units. An explanatory diagram of the genre. Configuration diagram of the information processing device. Functional block diagram of the information processing device. (A) to (c) are explanatory views of data stored in a large-capacity storage device. A flowchart for calculating the priority of a unit to be learned with priority. An explanatory diagram of unit data including units that are not included in the exam subjects of the school of your choice.

Hereinafter, embodiments will be described in detail with reference to the drawings.
In the first embodiment, an example is shown in which a learner who wants to enter a university, that is, an examinee, is made to present learning contents to be learned to the examinee when he / she obtains academic ability to pass the desired school. In addition, as learning content for such examinees, the examination subjects of the school of choice will be classified and examples of subdivided items will be explained.
For example, suppose that there is Mathematics I as an entrance examination subject of the student's desired school. In this case, the contents to be learned in Mathematics I include numbers and formulas, quadratic functions, figures and metric. The contents of these entrance examination subjects are, for example, expansion of formulas in numbers and formulas, factorization of formulas, and so on. Quadratic functions can be subdivided into functions and graphs, graphs of quadratic functions, quadratic equations, quadratic inequalities, and the like.

In the following explanation, the unit is a unit in which the knowledge necessary for passing the entrance examination in the learning content is arranged in a predetermined acquisition order. More specifically, in this embodiment, the content of the subject to be studied may be classified according to the chapters of the textbook, and the subdivided one may be referred to as a "unit". An example of a unit in "mathematics" is shown in FIG. In the example of FIG. 1, the units are classified hierarchically such as a large unit, a medium unit, and a small unit. For example, in the large unit defined as "number and formula", medium units such as "calculation of formula", "real number", and "linear inequality" belong, and in the middle unit of "calculation of formula", It is classified so that the small units of "expansion of equations" and "factorization of equations" belong to it. The same is true for other units.

For items in the large unit, medium unit, and small unit, the code (combination of letters, numbers, and symbols) of the machine-readable data structure that can uniquely identify each item or its contents is assigned to the item. It is associated with one-to-one and set in a predetermined recording medium (data recording area). Searching, searching, sorting, etc. of the unit or its contents recorded on the recording medium are performed using these codes. Although an example of three layers is shown in FIG. 1, it may be four or more layers or two layers.

On the other hand, apart from the above units, common or similar knowledge across multiple units (if you complete one of the units, you can efficiently acquire it without having to take the unit), and it is a prerequisite for solving one problem. Classification from the viewpoint of one or more types of condition grasping methods (knowledge of subject grasping) and problem-solving procedures (solving methods) that should be learned during learning is more suitable for achieving the purpose of learning. It has been clarified from the past learning support results by the applicant of the present application that it is a necessary condition.

In the following explanation, those classified from such a viewpoint may be referred to as "genre" in particular. The genres are the analysis results of past national exam questions, mock exams (abbreviation of mock exam questions, the same applies hereinafter), individual entrance exam questions of many universities, and the causal relationship of the examinee's grades to the past course-specific completion exams. , At least one such as genre (1), (2) ... (n), preferably by machine learning (deep learning, which will be described later) in which the progress of learning of a plurality of examinees is input. , Multifaceted and multiple types are determined. The genre can be decided for each subject, but it may cross the boundaries of the subject, for example, "mathematics" and "physics".

An example of the contents of the genre (1) is shown in FIG. In the example of FIG. 2, for convenience of explanation, the genre (1) is classified into two layers such as a major classification and a minor classification. In the illustrated example, as the major classification of genre (1), "understanding the problem", "graph / graphic expression", "good formula transformation", and "steady processing" are the solution approaches (large), that is, the most. It is classified by machine learning as a high-level solution approach. Such a classification method is a method that cannot be derived by a "unit" or a simple classification method that simply groups it, and is premised on solving common or similar knowledge across multiple units, or one problem. The viewpoint is one or more types of condition grasping methods required as, and problem-solving procedures that should be learned during learning.
Although an example of two layers is shown in FIG. 2, it may be three or more layers.

In the first embodiment, regarding the problem to be presented to the examinee, which unit (large unit, medium unit, small unit in the example of FIG. 1) and which genre (solving approach (large) in the example of FIG. 2) are the problems. , Solving method approach (small)) is defined in advance. Then, for the contents of each of these definitions, the results of the past mock exams of the examinees and the results of the confirmation test in the class are recorded in association with each other. The association is performed using a large unit code, a medium unit code, a small unit code solution approach (large) code, and a solution approach (small) code.

As a result, for each unit or genre, it is possible to obtain the learning achievement level of the examinee, the expected score that can be obtained in the entrance examination, etc. from the learning progress status of the examinee or the past grades. In addition, not limited to units and genres, a wide variety of problems can be classified into several categories according to arbitrary criteria (for example, aptitude analysis criteria, similarity of questioning tendency with concurrent applicants) and the like. In the following explanation, these large units, medium units, small units, major classifications and minor classifications of genres, and individually defined standards may be referred to as "tags".

In the first embodiment, it is also assumed that information indicating how much the learner's learning level grows when a predetermined amount or a predetermined time is learned for each tag, for example, the examinee learns a predetermined amount. The growth width of the learning level in the case is estimated, and one or more learning contents whose growth width becomes larger than a predetermined value are displayed on a display device that can be visually recognized by the examinee. This process will be described in detail later.

(Device configuration)
Hereinafter, examples of embodiments of the learning support device to which the present invention is applied will be described. The learning support device can be realized by a collaboration between an information processing device having a display function and a communication function and a computer program.
FIG. 3 is a diagram showing a configuration example of an information processing device that functions as a learning support device. The information processing apparatus 1 is, for example, a database system having a plurality of types of databases (hereinafter referred to as "DB"), and is configured to output data searched from a large amount of data based on predetermined conditions. The information processing device 1 includes a CPU (Central Processing Unit) 10, a ROM (Read Only Memory) 11, a RAM (Random Access Memory) 12, and an I / O interface 13 as basic components. The CPU 10, ROM 11, RAM 12, and I / O interface 13 are communicably connected via the bus 14. An input device 2, an output device 3, and a large-capacity storage device 4 are connected to the I / O interface 13, and the learning level of a person who has passed the test is time-series for each learning content in the large-capacity storage device 4. It is held as a performance level that changes to.

The input device 2 is, for example, a keyboard, a mouse, a touch panel, or the like. The output device 3 is, for example, a display, a printer, or the like. The input device 2 functions as an acquisition means for acquiring a plurality of learning contents that the examinee can learn toward passing the examination. The input device 2 may be configured to be provided inside the information processing device 1 or may be an external device connected via a communication cable.
The large-capacity storage device 4 is a rewritable storage device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive). The large-capacity storage device 4 functions as a holding means for holding the learning level of the examinee and the analysis result of the questioning tendency of the examination that the examinee aims to pass. The large-capacity storage device 4 may be an online storage provided in the cloud. In the large-capacity storage device 4 of the first embodiment, a database is also constructed for the examinee to provide useful information for selecting a concurrent application school.

The CPU 10 controls the operation of the information processing apparatus 1 by executing a computer program stored in the ROM 11. The RAM 12 provides a work area for the CPU 10 to perform processing. The CPU 10 receives data and instructions input from the input device 2 via the I / O interface 13. The CPU 10 outputs information by the output device 3 via the I / O interface 13. The CPU 10 accesses (writes and reads) the large-capacity storage device 4 via the I / O interface 13.

FIG. 4 is a functional block diagram of the information processing apparatus 1. The information processing apparatus 1 operates as a plurality of functional blocks by, for example, the CPU 10 executing a computer program. In the following description, an example using a unit as a tag is mainly shown, but the tag can use a genre or other criteria.
The information processing device 1 operates as an input unit 101, an output unit 102, a control unit 103, a unit DB management unit 104, an examinee DB management unit 105, and an elongation width calculation unit 107. The unit DB 40, the examinee DB 41, and the test result DB 42 are constructed in the large-capacity storage device 4. 5 (a) to 5 (c) are explanatory views of data stored in the large-capacity storage device 4.

The control unit 103 controls the unit DB management unit 104, the examinee DB management unit 105, and the elongation width calculation unit 107. By controlling these, the control unit 103 extracts information useful for calculating the learning content that the examinee should preferentially learn based on the unit DB 40, the examinee DB 41, and the test result DB 42. The learning content is classified by tags, and for each tag, a value indicating how much the learning level grows when a predetermined amount or a predetermined time is learned is appropriately calculated and can be updated freely. Recorded in association with.

The input unit 101 receives data and instructions input from the input device 2 and transmits them to the control unit 103. The output unit 102 outputs information from the output device 3 under the control of the control unit 103. In the first embodiment, the output unit 102 outputs information useful for selecting a unit to be learned by the examinee as an image from the output device 3. The unit DB management unit 104 creates, deletes, updates, reads, and the like the unit data stored in the unit DB 40 based on the control of the control unit 103.

FIG. 5A is an explanatory diagram of unit data. The unit data is stored with a unit ID that identifies the unit. In addition, the unit data is saved in association with the subject to which the unit belongs, such as mathematics and national language. In general, examinees often choose the subject they want to study first and then study. In this way, the subject ID and the unit ID are associated with each other, and after the examinee first selects the subject to be studied, the unit selected from the units belonging to the subject ID to be studied is presented to the examinee in that subject. Only the unit to which it belongs can be presented to the examinee.

Of course, not limited to such an embodiment, the unit ID may be stored in the unit data without being associated with the subject, and the unit to be studied may be presented to the examinee regardless of the subject. FIG. 5A shows a subject ID (S), a unit name, and a unit ID when the subject is mathematics (particularly the number I). As the unit, the calculation of the formula, the real number, the linear equation, the linear inequality, and the set and the logic included in the learning content "number and formula" are shown, and the unit IDs are S1, S2, respectively. S3, S4 and S5 are given.
Note that FIG. 5A exemplifies a part of the unit data, and the actual unit data includes quadratic functions, figures and metric, etc., for the subject of the desired school (mathematics in this example). It may include the unit ID and the like of the associated unit.

For example, each information is input from the input device 2 and the unit data is created by the unit DB management unit 104 in response to an instruction from the control unit 103 based on this information. When there is a change in the content of the unit data, the changed content is input from the input device 2. The unit DB management unit 104 updates the unit data according to the change contents according to the instruction of the control unit 103.

The unit DB management unit 104 also refers to the unit DB 40 under the control of the control unit 103, and reads out necessary information. By doing so, for example, when the examinee changes the school of choice and the subjects or units required for the entrance examination of the school of choice are changed, or the course of study is changed and new subjects or units are introduced or excluded. Even in that case, the content of the unit data can be changed accordingly.
In addition, by storing only the subjects and units belonging to the examination subject of the school of choice of the examinee in the unit data, it is possible to prevent the examinee from presenting the subjects and units unnecessary for the examination.

The examinee DB management unit 105 creates, deletes, updates, reads, and the like the examinee data stored in the examinee DB 41 based on the control of the control unit 103. An example of the contents of such examinee data is shown in FIG. 5 (b). For example, the examinee data is stored (associated) with an "examinee ID" that identifies the examinee for each "examinee name". The examinee data includes items such as "student's current school name", "school ID", "desired school information", and "result history".

In this example, the learning data includes the "student's school name", but may be configured to be identified by the "school ID". In this case, the "student's school name" is indispensable information. do not have.
The "school information" includes a "school ID", a "faculty ID", and a "department ID" representing the school, faculty, and department of the examinee at that time. The "result history" is a history of grades including the results (scores, deviation values, etc.) of the mock examination of the examinee.

Each information is input from the input device 2, for example, and the examinee data is created by the examinee DB management unit 105 in response to an instruction from the control unit 103 based on this information. If there is a change in the content of the examinee data, the changed content is input from the input device 2. The examinee DB management unit 105 changes the examinee data according to the change contents according to the instruction of the control unit 103.
Further, the examinee DB management unit 105 refers to the examinee DB 41 under the control of the control unit 103, and reads out necessary information.

Under the control of the control unit 103, the examinee DB management unit 105 generates test result data based on the results of the examinee's past mock exams and stores the test result data in the test result DB 42. In the test result data, the mock test taken by the examinee and the score for each unit are stored. In this example, the test result is generated based on the result of the past mock test, but the test result data may be generated not only for the past mock test but also for any test such as the past question and the confirmation test. ..
FIG. 5C is an explanatory diagram of test result data. The test result data is generated by extracting the examinee name, the examinee ID, the school ID, and the grade history from the examinee data. In the example of FIG. 5C, the "examinee name (examination Taro)", "examinee ID (J1)", and "unit name" (calculation of formula, real number, linear equation) in the first mock examination (mock examination ID: M1) 1, linear inequalities and sets and logic), "unit ID" is shown. In addition, the "score rate (%)", "A-line score rate", and "score allocation" of the examinees are shown for each "unit". The "score rate (%)" of the examinee is the value (%) obtained by dividing the score obtained by the examinee by the score. The "A-line score rate" is a score rate at which the possibility of passing the desired school is equal to or higher than a predetermined value ("A-line"). In the first embodiment, for convenience, the score rate at which the pass probability of the desired school is 80% or more is defined as the A-line score rate. The probability of passing the score corresponding to the A-line score rate is not limited to 80%, and other numerical values or judgment criteria may be used. For example, the A-line score rate may be a score rate based on another criterion in which the pass probability is equal to or higher than a predetermined value.

The growth width calculation unit 107 estimates the growth width of the learning level by referring to the test result data stored in the test result DB and calculating the growth width of the learning level of the examinee for each unit by the machine learning method. The growth width here refers to the growth width of the learning level when it is assumed that the examinee who is an examinee has learned a predetermined amount. In the first embodiment, for convenience, the increase in the score rate predicted when one exercise is performed with a predetermined learning amount unit as one exercise is defined as the increase in the learning level. More specifically, one exercise can be one learning or one question learning. Therefore, the elongation width is expressed as follows, for example.

Growth width (x) = predicted increase score rate in one exercise Here, the predicted increase score rate in one exercise can be obtained by any method, but in the first embodiment, the following three Items A to C were used as parameters.
A Number of past exercises B Average score rate of the last few times in A's past exercises, for example, 3 times C Score rate in the completion judgment test

For example, with regard to A, if the number of exercises in the past questions is large, it is considered that the examinee will have an understanding and memory of the unit and the score will be stable, which may be a factor to reduce the growth rate. For example, for B, if the average score rate is high, for example, if the score is 95 out of 100, the predicted score will not increase beyond 5 even if the understanding of the unit is further deepened. On the other hand, in the case of 30 points out of 100 points, there is room for another 70 points to increase as the understanding of the unit deepens. Therefore, the value of B is set so that the value increases as the average score rate of the unit decreases.
Similar to B, C is set so that the value becomes larger when the score rate in the completion judgment test is low. In addition, the completion judgment test is a test that is conducted after learning all the contents of the unit, and since it is conducted at a difficulty level closer to the entrance exam than the past question exercise, it is more stretchable than the past question exercise test. It is preferable to increase the effect.

From these facts, assuming that the elongation width is S, S can be a value calculated by using at least one of the parameters A to C or any combination for each unit as the estimated value. For example, the added value or the average value of the values of A, B, and C, or the value obtained by calculating the weighted average value of A, B, and C can be used as the estimated value of the elongation width. The method for estimating the elongation width is not limited to this example, and any other method such as using the product of A, B, and C as the estimated value for the elongation width can be used.

The above-mentioned example is a simplified example. As another example, a calculation model in which hundreds of explanatory variables are set for A to B described above and the elongation width is obtained from these explanatory variables using artificial intelligence (AI) or machine learning method (deep learning) is used. It may be constructed to obtain the elongation width. For example, a machine learning method using a decision tree in the process of estimating the growth width of the learning level, such as the "ID3 algorithm", or a machine learning tool that executes this machine learning method can be used to obtain the growth width. For example, in a machine learning tool, a plurality of types of information may be input, and a decision tree generated in an estimation process may be randomly selected to derive an elongation width or the like.

Alternatively, "Extra Trees", which is an ensemble machine learning method based on decision trees, may be used. "Extra Trees" is a method in which the number of trees in a decision tree is set to a plurality, a random subset feature is used, and the tree is randomly divided. "Extra Trees" are generally suitable as a calculation model for calculating the growth width because they tend to have higher accuracy of test and learning scores than methods such as random forest.

A calculation model for obtaining the elongation width may be constructed by using a method other than the decision tree. For example, as shown below, it is also possible to use a combination of a plurality of methods including a decision tree and a regression method.

The control unit 103 specifies a unit to be preferentially learned by the examinee from the elongation width calculated for each unit in which the processing result obtained by the elongation width calculation unit 107 is acquired.
For example, the unit in which the elongation width is the largest, or the unit in which the elongation width is larger than a predetermined value is specified as a "unit to be learned preferentially". Further, the control unit 103 generates an image representing a unit to be preferentially learned. The control unit 103 visually outputs an image representing the unit to be preferentially learned from the output device 3 via the output unit 102. When the output device 3 is a display, the selection support image is displayed on the display. When the output device 3 is a printer, the selection support image is printed on paper. As a result, the unit to be studied with priority is presented to the examinee. The control unit 103 functions as a display control means for outputting an image from the output device 3.

(Determination method of the unit to be studied with priority)
Below, a detailed example of the unit determination method to be learned preferentially will be described. In the above description, the unit in which the elongation width is the largest or the unit in which the elongation width becomes larger than a predetermined value is specified as a "unit to be learned preferentially".
In the first embodiment, the priority is calculated for each unit so that the higher the increase in the score in the entrance examination at the desired school, the larger the priority value, and the unit with the higher priority is presented to the examinee. In the calculation of priority, in addition to the growth width S, the growth allowance and the question frequency are used.

As an example, the growth allowance uses a value obtained by correcting the score value according to the difference between the A-line score rate of the desired school and the current score rate. For example, the score is corrected so that the larger the difference between the A-line score rate of the desired school and the current score rate, the larger the value is, and the corrected value is used as the growth allowance.
Further, as an example of the question frequency (β), the question frequency of the unit at the desired school (for example, the question frequency in the last 10 years of the desired school) can be used. As the question frequency increases, the question frequency value increases.
The value obtained by correcting the growth width S by at least one of the growth allowance and the question frequency is used as the priority.
The growth allowance is not limited to this example, and the target score rate for passing the desired school, the score rate of the examinees, and the entrance examination of the desired school in any learning range such as learning contents and units. It can be determined by the frequency of appearance (or expected score) of the learning range in. The growth allowance is set so that the higher the score of the school of choice, the higher the growth allowance, and the lower the score rate of the examinees, the higher the growth allowance. By defining in this way, the learning range with a large growth margin represents the learning range in which there is room for increasing the score in the entrance examination of the desired school by increasing the score rate.

In this way, by using the obtained growth range, the priority of the unit to be studied preferentially in order to increase the growth range of the score in the entrance examination of the desired school is calculated. As a result, it is possible to show the examinee which unit should be prioritized in order to pass the desired school. For example, if the unit with the highest priority is the calculation of the equation, the unit with the second highest is the set, and the unit with the third highest is the linear inequality, the calculation of the first-order equation, the second-order set, the third-order inequality, etc. It is ranked in and displayed to the examinees. As a result, the examinee can easily grasp in what order he / she should study the unit in order to increase the increase in the score in the entrance examination at the school of his / her choice.

(Calculation of priority)
FIG. 6 is a flowchart showing a procedure for calculating the priority of the unit to be learned preferentially in the first embodiment. This process is started, for example, by the control unit 103 receiving an instruction to start the priority calculation process from the input device 2 via the input unit 101. The start instruction includes the examinee ID and the subject name for which the priority is requested.

The control unit 103 that has received the start instruction confirms the first choice school of the examinee corresponding to the examinee ID included in the start instruction (S101). The control unit 103 can confirm the first choice school (school ID, faculty ID, and department ID) of the examinee by referring to the examinee data of the examinee ID of the examinee DB 41 by the examinee DB management unit 105.

The control unit 103 that has confirmed the first school of choice extracts the subject ID, the unit name, and the unit ID corresponding to the subject name from the unit DB 40 through the unit DB management unit 104 (S102). After that, the control unit 103 extracts the grade history of the input subject obtained from the test result data of the examinee DB 41 and the test result DB 42 through the examinee DB management unit 105. As a result, it is possible to obtain the number of past question exercises related to the above-mentioned input subject, the average score rate of the last three times in the past question exercise, and the score rate in the completion judgment test.
The growth width calculation unit 107 obtains the growth width for each unit as described above from the number of past questions and the like and the A-line score rate of the examinee data stored in the examinee DB 41 (S103).

The control unit 103 obtains the priority by multiplying the growth width obtained for each unit by the above-mentioned growth allowance and question frequency (S104). The control unit 103 outputs the obtained priority to the output device 103 through the output unit 102 (S105). Preferably, the growth allowance is associated with the actual level stored in the large-capacity storage device 4 and output to the output device 103.

Through the above processing, it is possible to present to the examinee the priority of the unit to be studied preferentially according to the school of choice. From the priorities presented, candidates will be able to easily know which unit to study in order to improve their entrance exam score at the school of their choice. In addition, the units presented are narrowed down to the subjects included in the examination subjects of the school of choice. Candidates can study for the entrance examination of the school of their choice without having to reconfirm whether or not they are included in the examination subjects of the school of their choice. Therefore, the examinee can facilitate the unit to be studied preferentially in order to increase the score in the entrance examination at the desired school, and can efficiently study for the examination so as to increase the possibility of passing the desired school.

(Modification example)
In the above example, the priority was calculated without referring to the time when the examinee learns. However, it is also possible to calculate the growth rate after referring to the time when the examinee learns, or to calculate the priority by referring to the time when the examinee learns in the calculation of the priority.
The following is an example of calculating the growth rate with reference to the time when the examinee learns.
In the first embodiment, the elongation width is calculated as, for example, the added value of the above-mentioned parameters A, B and C.

Here, when the examination is near, it usually takes a long time after the completion judgment test exercises are performed, and the importance of the score rate in the past questions that are performed later than the number of exercises in the past questions. Is considered to be relatively high. Therefore, for example, the number of days remaining until the entrance examination of the desired school may be set as the parameter D, and the parameter D may be further added to the parameters A to C to calculate the growth rate for each unit. For example, the value obtained by adding or multiplying the value calculated from the parameters A to C by the parameter D may be used as the estimated value of the elongation width.
By calculating the growth rate with reference to the time of the examination in this way, it is possible to more accurately determine the unit to be studied with priority. At this time, the definition of D may be changed depending on the unit.

In the above example, the coefficient D is used to refer to the time of the examination in the calculation of the elongation width, but the time of the examination may be referred to in the calculation of the priority. In addition, even if the subject is not related to the entrance examination, it may be possible to increase the score of the subject related to the entrance examination by studying the subject related to the subject. For example, the examination range of the entrance examination of the desired school may be the number II, and the number III may not be included in the examination range. However, for example, the calculus taken in number III is a development of the calculus in number II, and when solving the problem of calculus in the range of number II, it is easier to solve the problem by using the knowledge of number III. In some cases it can be done.

In this way, by deliberately studying subjects that are not included in the examination subjects of the desired school, it may be possible to increase the score rate of the entrance examination at the desired school. An example of finding the growth rate and priority, including subjects that have not been completed, is shown.
FIG. 7 shows an example of unit data stored in the unit DB 40 in an example in which the number I and the number II are included but the number III is not included in the examination subject of the desired school. In the example shown in FIG. 5A, the number I is shown as an example, but in the example of FIG. 7, the number II (subject ID: T) and the number III are shown.

In the example of FIG. 5A, the unit data includes only the unit included in the examination subject (mathematics in this example) of the desired school. However, in the example of FIG. 7, although the number II is included in the examination subject of the desired school, the unit of the number III, which is not included in the examination subject of the desired school, "asymptote (unit ID: U5) is also included." ..

This is because the question of directly asking the asymptote of number III is not asked in the entrance examination of the school of choice, but by understanding the concept of the asymptote, you can understand the background of the question and correctly guess the limit of graphs and sequences. This is because it may be possible to create an answer after doing so. Therefore, although it is not an examination subject of the school of choice, the score rate can be increased as a result by learning the "asymptote" of the unit III, which is a unit particularly related to the examination subject, number II.
Based on the above, in the above-mentioned calculation of growth rate and priority, it is possible to include units that are not included in the examination subjects of the desired school but are closely related to the unit of the examination subjects of the desired school. , You can increase the score rate in the entrance examination of the school of your choice.

The learning support device according to one aspect of the present invention is a holding means for holding the learning level of the examinee and the analysis result of the questioning tendency of the examination that the examinee aims to pass, and the examinee can learn toward passing the examination. An acquisition means for acquiring a plurality of learning contents, and an estimation means for estimating the growth rate of the learning level when it is assumed that the examinee has learned a predetermined amount for each of the acquired plurality of learning contents based on the analysis result. It is characterized by having a control means for calculating the priority of the learning order for the plurality of learning contents based on the growth width and ranking the priority.

Growth width (x) = predicted increase score rate in one exercise Here, the predicted increase score rate in one exercise can be obtained by any method, but in the first embodiment, the following 3 Two items A to C were used as parameters.
A Number of past exercises B Average score rate of the last few times in A's past exercises, for example, 3 times C Score rate in the completion judgment test

Claims (11)

A holding means for holding the learning level of the examinee and the analysis result of the questioning tendency of the examination that the examinee aims to pass,
An acquisition means for acquiring a plurality of learning contents that the examinee can learn toward passing the examination, and
An estimation means for estimating the growth rate of the learning level based on the analysis result when it is assumed that the examinee has learned a predetermined amount for each of the acquired plurality of learning contents.
A presentation means for displaying one or more of the learning contents whose elongation width is larger than a predetermined value on a display device that can be visually recognized by the examinee.
A learning support device characterized by having. The estimation means is characterized in that a value at which the learning level changes when the learning content is learned at least once or one question is estimated as the elongation width.
The learning support device according to claim 1. The holding means holds the learning level of a person who has passed the test as a performance level that changes in time series for each learning content.
The presenting means sets the growth margin in the learning content as the actual level, which is determined based on the score rate of the examinee in the learning content and the expected score of the learning content in the examination in which the examinee aims to pass. It is characterized in that it is displayed on the display device in association with the above.
The learning support device according to claim 1 or 2. The presenting means is characterized in that the display device displays the growth allowance in the learning content and the appearance frequency in the test.
The learning support device according to claim 3. The learning content is composed of a unit in which the knowledge necessary for passing the examination is arranged in a predetermined acquisition order, and at least one genre in which the knowledge is arranged across a plurality of the units. , The learning level is associated with each unit and each genre.
The learning support device according to any one of claims 1 to 4. The estimation means is any one of claims 1 to 3, wherein the estimation means inputs a plurality of types of information and derives the elongation width by a machine learning tool that randomly selects a decision tree generated in the estimation process. The learning support device described in. The presenting means is characterized in that the learning content having a relatively high elongation width is preferentially presented.
The learning support device according to any one of claims 1 to 6. The presenting means is characterized in that the learning content having a higher growth allowance and the appearance frequency is preferentially presented.
The learning support device according to any one of claims 4 to 6. The presenting means is characterized in that the learning content having a higher priority is preferentially presented with the result of multiplying the elongation width, the elongation allowance, and the appearance frequency as a priority.
The learning support device according to any one of claims 4 to 8. Retaining the learning level of the examinee and the analysis result of the questioning tendency of the examination that the examinee aims to pass,
Acquiring a plurality of learning contents that the examinee can learn toward passing the examination,
Based on the analysis result, the growth range of the learning level when it is assumed that the examinee has learned a predetermined amount for each of the acquired plurality of learning contents is estimated.
A learning support method comprising displaying one or more of the learning contents whose elongation width is larger than a predetermined value on a display device that can be visually recognized by the examinee. A computer program for operating a computer as the learning support device according to any one of claims 1 to 9.

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