JP7534149B2 - Learning support system, information processing device, information processing method and program - Google Patents

Description

The present invention relates to a learning support system, an information processing device, an information processing method, and a program.

A system is disclosed that provides learners with unlearned items as optimal study items (Patent Document 1).

WO2010/119571

The invention described in Patent Document 1 transmits keyword data from the learner's communication terminal to a management server. The management server extracts documents with a predetermined degree of similarity from documents stored in a document database based on the keywords, and defines the study range. Furthermore, the management server automatically generates items that are estimated to be unlearned items from within the study range based on data input at the learner's communication terminal as optimal study items. This makes it possible to present optimal study content to the learner.

However, in the invention described in Patent Document 1, learning items to be posed to a learner are generated based on unstudied items, but appropriate questions are not provided for each learner in a set. Furthermore, if there are questions that a learner has not yet tackled, the learner's tendency to answer those unstudied questions is not taken into consideration when presenting the questions.

The present invention was made in consideration of these problems, and its purpose is to provide each learner with one or more appropriate problems, using the learner's tendency to answer questions that they have not yet tackled.

In order to solve the above problems, the learning support system of the present invention includes an acquisition unit that acquires the learner's answer to each problem within a predetermined learning range, an answer judgment unit that judges whether the answer is correct or not, a tendency identification unit that identifies the learner's second answer tendency for unattended problems based on the first answer tendency of the learner indicated by the judgment result of the answer judgment unit, a problem selection unit that selects one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill the learning situation gap including the answer tendency between the learner and the other learners based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency for attempted problems of other learners associated with the desired school selected by the learner, and an output unit that outputs the one or more selected problems to the learner.

In order to solve the above problem, the information processing device of the present invention is an information processing device including a processor, and the processor executes the following operations: acquiring the learner's answer to each problem within a predetermined learning range; judging whether the answer is correct or not; identifying the learner's second answer tendency for unattended problems based on the learner's first answer tendency indicated by each judgment result for each problem; selecting one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill the learning situation gap including answer tendencies between the learner and the other learners based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency for questions that other learners associated with the desired school selected by the learner have tackled; and outputting the selected one or more problems to the learner.

In order to solve the above problem, the information processing method of the present invention includes an information processing device that acquires the learner's answer to each problem within a specified learning range, determines whether the answer is correct, identifies the learner's second answer tendency for unattended problems based on the learner's first answer tendency indicated by each judgment result for each problem, selects one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill the learning situation gap including answer tendencies between the learner and the other learners based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency for questions attended by other learners associated with the desired school selected by the learner, and outputs the one or more selected problems to the learner.

In order to solve the above problem, the program of the present invention causes an information processing device to execute the following: acquire the learner's answer to each problem within a specified learning range; determine whether the answer is correct or not; identify the learner's second answer tendency for unattended problems based on the learner's first answer tendency indicated by each judgment result for each problem; select one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill the learning situation gap including answer tendencies between the learner and the other learners based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency for questions that other learners associated with the desired school selected by the learner have attempted; and output the one or more selected problems to the learner.

According to the present invention, appropriate questions can be provided to each learner using the learner's tendency to answer questions that have not yet been tackled.

1 is a configuration diagram showing an example of a learning support system according to an embodiment of the present invention. FIG. 2 is a configuration diagram showing an example of a hardware configuration of a learning assistance server. FIG. 2 is a configuration diagram showing an example of the configuration of a learning support server. FIG. 11 is a diagram showing an example of learner basic information. FIG. 4 is a diagram illustrating an example of learning history information. FIG. 2 is a diagram showing an example of the configuration of a learning terminal; FIG. 11 is a diagram showing an example of a screen displayed by a user interface of the study terminal. FIG. 11 is a sequence diagram illustrating an example of a processing procedure of the learning support system.

Embodiments of the present invention will be described below with reference to the attached drawings. To facilitate understanding of the description, the same components in each drawing are denoted by the same reference numerals as much as possible, and duplicate descriptions will be omitted.

<Learning Support System 1>
FIG. 1 is a configuration diagram showing an example of a learning support system 1 in this embodiment. As shown in FIG. 1, the learning support system 1 includes a learning support server 10 and learning terminals 20A, B, and C. Hereinafter, when there is no need to distinguish between the learning terminals 20A, B, and C, they will be simply referred to as learning terminals 20. In the learning support system 1, the learning support server 10 and the learning terminals 20 are connected to each other so as to be able to communicate with each other via a network N. Note that, in this embodiment, the learning support system 1 is described as being composed of multiple devices, but it may be realized in one device, for example, it may be realized in only the learning terminal 20.

The learning support server 10 is an information processing device that provides various problems to the learner via the learning terminal 20. The learning support server 10 also acquires the answers when the learner works on the problems, identifies a second answer tendency for unattended problems using a first answer tendency indicated by the answer judgment result, and provides appropriate problems suited to the learner using the first answer tendency and the second answer tendency.

The learning terminal 20 is an information processing device that displays questions provided by the learning support server 10 on a display and accepts various operations from the learner. The learning terminal 20 is, for example, configured as a tablet terminal device or a personal computer. Note that the learning terminal 20 may also be configured as, for example, a home game device (including a portable game device), a mobile phone (a so-called feature phone), a smartphone (a multi-function mobile phone), a personal digital assistant, an e-book reader, or other computer device.

The network N may be a communication line or network related to information processing, including, for example, the Internet, and may be configured to enable transmission and reception of various information between the learning support server 10 and the learning terminal 20.

The configurations of the learning assistance server 10 and the learning terminal 20 will be described below.
<Learning Support Server 10>
Hereinafter, the hardware configuration of the learning assistance server 10 will be described with reference to Fig. 2. Fig. 2 is a configuration diagram showing an example of the hardware configuration of the learning assistance server 10 according to the present embodiment. As shown in Fig. 2, the learning assistance server 10 includes a processor 10a such as a CPU (Central Processing Unit) or a GPU (Graphical processing unit), a memory 10b, a storage device 10c such as a HDD (Hard Disk Drive) and/or an SSD (Solid State Drive), and a network interface 10d. In addition, each of the components 10a to 10d included in the learning assistance server 10 is connected to each other via a bus.

The processor 10a executes programs stored in the memory 10b and the storage device 10c to realize the processes and functions described in detail in FIG. 3 and subsequent figures. The memory 10b may include a read only memory (ROM) and a random access memory (RAM), and the ROM stores programs such as a boot program. The RAM is used as a work area for the processor 10a. The storage device 10c stores data on a disk by controlling the read/write of data according to the control of the processor 10a. The storage device 10c stores, for example, various tables described below. The programs stored in the memory 10b and the storage device 10c may be configured as computer-readable non-transitory storage media.

The network interface 10d is connected to a network N such as the Internet, and is connected to an external device such as a learning terminal 20 via the network N. The network interface 10d then serves as an interface between the network N and the inside of the computer device that realizes the learning support server 10, and controls the input and output of data between the computer device that realizes the learning support server 10 and the external device.

The configuration of the learning support server 10 will be described below with reference to FIG. 3. FIG. 3 is a configuration diagram showing an example of the configuration of the learning support server 10. As shown in FIG. 3, the learning support server 10 includes, for example, a memory unit 11, an acquisition unit 12, an answer determination unit 13, a trend identification unit 14, a question selection unit 15, an output unit 16, and a ranking identification unit 17.

The memory unit 11 may store, for example, learner basic information 11a, learning history information 11b, answer tendency prediction model 11c, question selection model 11d, and answer time prediction model 11e. The memory unit 11 may also store question information (not shown). The question information is, for example, information associated with the ID of each question, including content information of each question (curriculum name, subject, unit, question content, etc.), difficulty level, related question ID, the school the learner is aiming for, etc.

An example of learner basic information 11a will be described with reference to FIG. 4. As shown in FIG. 4, learner basic information 11a includes, for example, "school information," "desired school information," "level information," "pass/fail information," and the like, which indicate basic information about the learner, with an appropriate item such as a "learner ID" that identifies the learner as a primary key.

"Student ID" includes information that allows the learner to be identified. "School information" includes information about the school the learner attends, such as the school name, grade, and class. "Preferred school information" includes information about the school the learner is aiming for (junior high school, high school, university, etc.), such as the school name and course (faculty, department, etc.). "Level information" includes the desired school the learner is aiming for and the difficulty level of the course. "Pass/fail information" includes information about whether the learner was accepted into the desired school, or information such as "-" (not entered) if the learner is a current student. In addition to the above, learner basic information 11a may also include the learner's name, etc.

An example of learning history information 11b will be described with reference to FIG. 5. As shown in FIG. 5, learning history information 11b uses an appropriate item such as "learner ID" as a primary key, and includes "learning status information," "learning time information," and other information indicating the learning history of the learner.

"Student ID" corresponds to the learner ID shown in FIG. 4 and includes information that enables the learner to be identified. "Study status information" includes, for example, information on problems that the learner has tackled within a specified learning range, the timing, the results of whether the answer was correct or not, and information on problems that have not been tackled. The specified learning range may be changed as appropriate depending on the school that the learner wishes to attend, etc. "Study time information" includes information such as the time it took the learner to answer each problem that he or she tackled (hereinafter also referred to as "answer time").

The answer tendency prediction model 11c stored in the memory unit 11 in FIG. 3 is a model for predicting and identifying the answer tendency of a learner. For example, the answer tendency prediction model 11c includes a model for predicting and identifying the answer tendency for unattended questions based on the correctness of the answers corresponding to each problem that the learner has tackled.

As a specific example, answer tendency prediction model 11c may be a collection of records for predicting and identifying answer tendencies such as the learner's desired school information, the correct answer rate for the problems they have worked on, the range of problems they have worked on, and the difficulty of the problems they have worked on. In addition, answer tendency prediction model 11c may be an inference algorithm that predicts and infers answer tendencies (such as the correct answer rate) for problems of each level of difficulty, machine-learned using the learner's desired school information, the correct answer rate for the problems they have worked on, the range of problems they have worked on, and the difficulty of the problems they have worked on as learning data.

The problem selection model 11d stored in the memory unit 11 in FIG. 3 is a model for selecting one or more problems that will fill the gap between the learning situation of the learner and other learners in order for the learner to pass the school of his/her choice. For example, the problem selection model 11d includes a model for selecting one or more problems appropriate for the learner to narrow the gap between the learning situation of the learner and other learners, based on the desired school, course, range of study, and correct answer rate of the problems selected by the learner, as well as the range of study and correct answer rate of the problems selected by other learners associated with the desired school and course. A desired school is associated with multiple learners who have applied to the same school, and the other learners associated with the desired school selected by a given learner may be one or more, or may include those who have passed the desired school.

As a specific example, the problem selection model 11d may be a collection of records indicating each parameter for selecting one or more problems appropriate for a learner based on the learner's desired school and course, the scope of study addressed, and a first answer tendency for problems addressed and a second answer tendency for problems not addressed, as well as the scope of study addressed and a third answer tendency for problems addressed of other learners associated with the desired school and course.

The problem selection model 11d may also be a trained model that selects one or more problems that fill the gap in the learning situation, including the answer tendency, between the learner and other learners, based on the learning history including the first answer tendency, the second answer tendency, and the third answer tendency to the problems tackled by other learners associated with the learner's school of choice. In this case, the problem selection model 11d may be an inference algorithm for selecting one or more problems that fill the gap in the learning situation, including the answer tendency, between the learner and other learners, machine-learned using the learner's school of choice and course, the range of study, the first answer tendency to the problems tackled, the second answer tendency to the problems not tackled, and the range of study and the third answer tendency to the problems tackled by other learners associated with the school of choice and course as learning data. The one or more problems that fill the gap in the learning situation, including the answer tendency, between the learner and other learners include, for example, a problem with a large difference between the answer tendency (e.g., correct answer rate) of the learner and the answer tendency (e.g., correct answer rate) of the other learners.

The answer time prediction model 11e stored in the memory unit 11 in FIG. 3 is a model for predicting and identifying the answer time (predicted time) of a learner. For example, the answer time prediction model 11e includes a model for predicting and identifying the answer time (predicted time) for an unattended problem based on the answer time corresponding to each problem that the learner has tackled, the difficulty of the problem, etc.

As a specific example, the answer time prediction model 11e may be a collection of records for calculating the average answer time for each difficulty level of problems that the learner has tackled, and specifying the predicted time according to each difficulty level of unattended problems. In addition, the answer time prediction model 11e may be an inference algorithm that predicts and infers the answer time (predicted time) for problems of each difficulty level, which is machine-learned using the answer time of each problem by the learner, the difficulty level of each problem, and the like as learning data.

The acquisition unit 12 shown in FIG. 3 acquires various information from an external device such as the learning terminal 20. In addition, the acquisition unit 12 acquires, for example, from the learning terminal 20, a learner ID, question ID, answer information, answer time information, and the like for questions that the learner has worked on within a specified learning range (e.g., a learning range set in a curriculum).

The answer determination unit 13, for example, determines whether the answer given by the solver to a given question is correct or not based on the answer information.

The tendency identification unit 14 identifies a second answer tendency (e.g., correct answer rate) of the learner for unattended questions based on the first answer tendency (e.g., correct answer rate) of the learner indicated by the judgment result of the answer judgment unit 13. The tendency identification unit 14 may identify the second answer tendency using, for example, the answer tendency prediction model 11c stored in the memory unit 11. Furthermore, the unattended questions may be unattended questions within a predetermined learning range.

The problem selection unit 15 selects one or more problems based on the first answer tendency and the second answer tendency using a learned model that selects one or more problems that bridge the learning situation difference between the learner and other learners, including the answer tendency, based on a learning history including the identified first answer tendency, second answer tendency, and third answer tendency to questions addressed by other learners associated with the desired school selected by the specified learner. The problem selection unit 15 may select one or more problems using, for example, the problem selection model 11d stored in the memory unit 11.

The output unit 16 outputs the one or more selected questions to the learner. For example, the output unit 16 may use the learner's learner ID to identify the learning device 20 used by the learner, or identify the learning device 20 that sent the answer information, and transmit the selected one or more question information together to the identified learning device 20.

As a result, according to this embodiment, appropriate questions can be provided to each learner using the learner's tendency to answer questions that have not been addressed. For example, it is possible to select one or more questions that will bridge the learning situation gap with other learners by using the learner's first tendency to answer questions that have been addressed and the second tendency to answer questions that have not been addressed. In addition, since it is possible to select multiple questions at once, the processing load can be reduced compared to selecting questions one by one. For the learner, by solving and understanding the multiple selected problems, it becomes possible to prioritize solving problems in areas that are difficult for him/her compared to other learners.

The acquisition unit 12 may also acquire the answering time required by the learner to answer the question. For example, when transmitting answer information to the learning assistance server 10, the learning terminal 20 transmits the answering time required to answer the question. In this case, the acquisition unit 12 is able to acquire the answering time required for each question.

In this case, the tendency identification unit 14 may include determining the answer time (predicted time) required for the learner to answer the unattended problem based on the acquired answer time. For example, the tendency identification unit 14 may predict and determine the predicted time for the unattended problem using the answer time prediction model 11e stored in the memory unit 11. As a specific example, the tendency identification unit 14 may calculate the average answer time for problems of the same difficulty level and use this average answer time to determine the predicted time for the unattended problem of the same difficulty level, or may predict and determine the predicted time for the unattended problem using the answer time prediction model 11e that has been learned by machine learning.

In this case, the question selection unit 15 may select multiple questions based further on the predicted time for the identified question. For example, the question selection unit 15 may further select questions from the selected multiple questions such that the total answer time or predicted time falls within a predetermined time. The predetermined time may be, for example, the learner's average daily study time or a time set in advance.

As a result, according to this embodiment, the answering time to a question can be used to select a more appropriate question according to the learning situation of the learner. Furthermore, when the question selection unit 15 selects a predetermined number of questions or more, it can also use the answering time when selecting questions within the predetermined number.

The problem selection unit 15 may also assign a priority to each of the selected multiple problems. For example, the problem selection unit 15 uses the answering tendencies of other learners for each problem and the answering tendencies of the learner, and assigns a higher priority to the problem the greater the difference (e.g., the difference in correct answer rate). For example, the problem selection unit 15 may calculate, for each problem, the difference between the correct answer rate of problems that have been tackled by other learners, or the correct answer rate of problems that have not been tackled, or the average value of both, and the correct answer rate identified for problems that have not been tackled by the learner, and assign a higher priority to the problem the greater the difference.

In this case, the output unit 16 may output questions to the learner in order of priority. For example, the output unit 16 may transmit information on a predetermined number of questions in order of priority to the learning device 20.

As a result, according to this embodiment, it becomes possible to output more appropriate questions to the learner based on the priority assigned to the questions. Priorities are assigned in the order in which the learner is desired to understand the questions, which allows the learner to solve the questions efficiently.

The question selection unit 15 may further divide the questions into a plurality of groups. For example, the question selection unit 15 may divide the questions so that a predetermined number of questions are included for each predetermined period. As a specific example, the question selection unit 15 may divide the selected questions so that five questions can be asked per day.

In this case, the output unit 16 may output questions for each group. For example, the output unit 16 may distribute the questions to the learning device 20 by dividing them into folders for each group so that the questions can be solved for each group. The output unit 16 may also output the questions in one group each day.

As a result, this embodiment makes it possible to divide multiple problems into groups and present questions to each group to the extent that the learner is able to tackle them.

The acquisition unit 12 may also acquire answers to each question in the group. For example, when questions are presented for each group, the acquisition unit 12 may acquire answer information for the questions in the group each time the answers to the questions in the group are completed.

In this case, the answer determination unit 13 determines whether the answer is correct or not based on the answer information of the questions in the group. The tendency identification unit 14 may also include updating the second answer tendency of unattended questions based on the first answer tendency that is updated using the determination results of the answers to each question in the group. For example, the tendency identification unit 14 may treat the questions in the group as tackled questions and use the answer tendencies of these questions to identify the answer tendencies of unattended questions included in other groups.

In this case, the problem selection unit 15 may select problems to be included in the next group based on the updated first answer tendency and second answer tendency. For example, even if a problem has already been divided into the next group, the problem selection unit 15 may use the updated first answer tendency and second answer tendency to reselect a problem and change the problem that the learner is to solve next. At this time, it is preferable to use the above-mentioned priority order. For example, since the first answer tendency and second answer tendency are updated and the priority order of problems changes, the problem selection unit 15 may include a problem with a high priority order after the change in the problem group that the learner will solve next.

As a result, according to this embodiment, it is possible to present appropriate questions to the learner each time a question in a group is solved, using the answer tendencies of each question in the group.

The other learners used by the trend identification unit 14 and the question selection unit 15 may include a top predetermined number of learners who have been accepted into the same school of their choice as the learner or who are aspiring to the same school of their choice as the learner. In this case, the learning history information used may include the answers of the other learners to each question.

As a result, according to this embodiment, the learners to be compared are those with the highest grades among those aspiring to the same school of choice, or those who have been accepted into the same school of choice, and by comparing their learning situations with those of these learners (or, in the case of those who have been accepted, their learning situations at the same time), the learner is able to focus on solving problems where he or she lacks understanding.

The ranking determination unit 17 may also determine the ranking of the learner among learners who are applying to the same school as the learner, based on the first answer tendency and the second answer tendency of the learner. For example, when the correct answer rate is used as the answer tendency, the ranking determination unit 17 may calculate the average correct answer rate (which may include the correct answer rate determined for unattended questions) for each learner for each problem within a specified learning range, and determine the ranking of the learners in order of the highest average correct answer rate.

In this case, the output unit 16 may output the identified ranking to the learner. For example, the output unit 16 may transmit information on the learner's current ranking to the learning device 20 when outputting one or more selected questions.

As a result, according to this embodiment, learners can know their current ranking, which can help improve their motivation when studying.

<Study Terminal 20>
Next, the learning terminal 20 will be described. The hardware configuration of the learning terminal 20 (not shown) is a configuration in which, for example, an input device that accepts input operations and an output device that outputs information are added to the hardware configuration of the learning assistance server 10. The input device may be, for example, a touch panel, a microphone, a keyboard, and/or a camera. The output device may be, for example, a display and/or a speaker. Descriptions of configurations similar to those of the learning assistance server 10 will be omitted.

A display is a display device that emits light on the screen to project images. Examples of displays include liquid crystal displays and organic electroluminescence displays.

The touch panel is layered on the display and outputs a signal to the processor 10a according to the operation position. The touch panel can be of any of a variety of known types, such as a resistive film type, a capacitive type, an acoustic pulse recognition type, an ultrasonic surface acoustic wave type, an infrared light blocking type, or an image recognition type.

The microphone converts the speaker's voice, which is input as analog data, into digital data and generates digital audio data.

For example, when the reading menu is executed, the speaker performs digital/analog conversion of the digital audio data, and outputs audio based on the analog audio data by passing electricity through a coil in the speaker cone.

The camera captures an image of a subject by operating the touch panel and generates image data. The generated image data is stored in memory 10b or the like.

The configuration of the learning terminal 20 will be described below with reference to FIG. 6. FIG. 6 is a configuration diagram showing an example of the configuration of the learning terminal 20. As shown in FIG. 6, the learning terminal 20 includes, for example, a communication unit 21, a memory unit 22, a question display unit 23, an answer input receiving unit 24, and an answer display unit 25.

The communication unit 21 receives and acquires various information from the learning assistance server 10. The communication unit 21 also transmits answer information, answer time, etc., which will be described later, to the learning assistance server 10.

The memory unit 22 stores various information obtained from the learning support server 10 via the communication unit 21.

That is, the learning terminal 20 acquires various information related to the learner's learning from the learning assistance server 10, stores the various information in the memory unit 22, and uses it for processing in each unit. Note that the learning terminal 20 may store the various information in advance without acquiring the various information from the learning assistance server 10. Although the explanation below is omitted, the learning terminal 20 may, for example, download the various information from the learning assistance server 10 at predetermined intervals and acquire and use the downloaded various information from the memory unit 22 as appropriate.

The question display unit 23 displays questions on the display based on question information stored in the memory unit 22 or the like.

The answer input receiving unit 24 receives answers input by the learner. For example, the learner touches a touch panel of the display to enter the answer, and the processor converts the answer into standard characters. This allows the answer to be displayed on the display. The answer input receiving unit 24 may also measure how long it takes to answer each question.

The answer display unit 25 displays the answers entered by the learner on the display. The answer display unit 25 uses a processor to analyze the learner's handwritten input in a specified area, converts it into standard characters, and displays the standard characters. This allows the learner to learn by handwriting, improving the learner's learning efficiency.

<Screen example>
Fig. 7 is a diagram showing an example of a screen displayed by the user interface of the learning terminal 20. In the example shown in Fig. 7, the display screen of the learning terminal 20 displays a screen when one or more questions selected by the learning support server 10 are received. The "Aspiration major pack" on the upper left of the screen is a function realized by each of the above-mentioned processes, and by the learner selecting this "Aspiration major pack," it becomes possible to solve one or more problems selected in relation to the desired school, the learner's own problem-solving tendency, problem-solving tendency for unattended problems, and the learning situation of other learners.

In the example shown in FIG. 7, the learner can solve the selected multiple problems in groups of five. When the learner selects "Next 5 Questions" in the "High School Year 1/August Practice Pack," the next five problems that the learner should work on are displayed. The questions included in the next five questions may be selected by the learning support server 10 and displayed based on the latest answer trends updated using the answer trends of the current five questions.

In the example shown in FIG. 7, the ranking of the learner identified by the learning assistance server 10 may be displayed. In this example, the learner's ranking is 125th out of 1,054 people. The population number may be the total number of other learners who are applying to the same school as the learner's desired school. This ranking may be determined by the learning assistance server 10 using the answering tendency for questions that have been addressed within a specified learning range, or the answering tendency predicted for questions that have not been addressed. This allows the learner to easily understand his or her current ranking.

<Processing Procedure>
Next, an example of a processing procedure of the learning support system 1 will be described with reference to Fig. 8. Fig. 8 is a sequence diagram showing an example of a processing procedure of the learning support system 1.

In S102, the question display unit 23 of the learning terminal (terminal) 20 displays questions distributed by the learning support server 10 or stored in the memory unit 22.

In S104, the answer input receiving unit 24 of the learning device 20 receives an answer to the question displayed on the screen from the learner. For example, the answer input receiving unit 24 receives and acquires information input using an input interface such as a keyboard or touch panel as answer information.

At S106, the communication unit 21 of the learning device 20 transmits the answer information for the accepted question to the learning support server (server) 10.

In S108, the acquisition unit 12 of the learning support server 10 acquires the answer information received from the learning device 20. The answer information may include a question ID, the content of the answer, and the time required to answer the question.

At S110, the answer determination unit 13 of the learning assistance server 10 determines whether the answer included in the answer information is correct or not based on the acquired answer information.

In S112, the tendency identification unit 14 of the learning assistance server 10 identifies the second answer tendency of the learner for unattended questions based on the first answer tendency of the learner indicated by the judgment result of the answer judgment unit 13. The answer tendency is, for example, the percentage of correct answers.

In S114, the problem selection unit 15 of the learning support server 10 selects one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that bridge the learning situation gap, including answer tendencies, between the learner and other learners, based on the learning history including the identified first answer tendency, second answer tendency, and third answer tendency for questions addressed by other learners associated with the desired school selected by the learner.

In S116, the output unit 16 of the learning support server 10 outputs one or more questions selected by the question selection unit 15 to the learning device 20. This makes it possible to provide appropriate questions for each learner using the learner's tendency to answer questions that have not yet been tackled.

The learning assistance server 10 can also execute each of the processes described above, for example, assigning priorities to the selected questions or identifying the learner's ranking within a given group, and may transmit this information together with the question information to the learning terminal 20.

As described above, according to this embodiment, appropriate questions can be provided to each learner using the learner's tendency to answer questions that have not been tackled. For example, it is possible to select one or more questions that will bridge the learning situation gap with other learners by using the learner's first tendency to answer questions that have been tackled and the second tendency to answer questions that have not been tackled. In addition, since it is possible to select multiple questions at once, the processing load can be reduced compared to selecting questions one by one. For the learner, by solving and understanding the multiple selected problems, it becomes possible to prioritize solving problems in areas that are difficult for him/her compared to other learners.

The embodiment of the present invention has been described above, but this is merely an example for the purpose of explaining the present invention, and is not intended to limit the scope of the present invention to only this embodiment. The present invention can also be implemented in various other embodiments.

For example, as a modified example, some of the processing or functions of the learning assistance server 10 may be processed by another server. For example, the trend identification unit 14 and the question selection unit 15 may be configured in another server. In this case, the trend identification unit 14 of the learning assistance server 10 may send information required to identify the answer trend to the other server and obtain the identified answer trend. Also, the question selection unit 15 may send information required to select a question to the other server and obtain one or more selected questions.

1... learning support system, 10... learning support server, 11... memory unit, 12... acquisition unit, 13... answer determination unit, 14... trend identification unit, 15... question selection unit, 16... output unit, 17... ranking identification unit, 20... learning terminal, 21... communication unit, 22... memory unit, 23... question display unit, 24... answer input acceptance unit, 25... answer display unit.

Claims (10)

an acquisition unit that acquires a learner's answer to each question within a predetermined learning range;
an answer determination unit that determines whether the answer is correct;
a tendency identification unit that identifies a second answer tendency having at least a correct answer rate for unattended questions of the learner based on a first answer tendency having at least a correct answer rate of the learner indicated by a judgment result of the answer judgment unit;
a problem selection unit that selects one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill a gap in the learning situation between the learner and the other learners by using at least the correct answer rates for the tackled problems and unattended problems of the learner and the correct answer rates for the tackled problems of the other learners based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency having at least a correct answer rate for the tackled problems of the other learners associated with the desired school selected by the learner;
an output unit that outputs the one or more selected questions to the learner;
A learning support system equipped with: The acquiring unit acquires an answer time required for the answer,
the tendency identification unit includes identifying a predicted time required for the learner to answer an unattended question based on the answering time;
The learning support system according to claim 1 , wherein the question selection unit selects the one or more questions further based on the predicted time. The problem selection unit includes:
assigning a priority to each of the selected issues;
The learning support system according to claim 1 , wherein the output unit outputs questions to the learner in the order of the highest priority. The problem selection unit further divides the selected problems into a plurality of groups;
The learning support system according to claim 1 , wherein the output unit outputs a question for each of the groups. the acquiring unit acquiring an answer to each question in the group;
The tendency identification unit includes updating a second answer tendency of the unattended questions based on a first answer tendency updated using a determination result of an answer to each question in the group,
The learning support system according to claim 4 , wherein the question selection unit selects questions to be included in a next group based on the updated first answer tendency and the updated second answer tendency. The other learners include a top predetermined number of learners among learners who have been accepted into the school of their choice or learners who are aspiring to the school of their choice,
The learning support system according to claim 1 , wherein the learning history includes answer trends of the other learners for each question. A rank specification unit is further provided for specifying a rank among learners who are applying to the desired school based on the first answer tendency and the second answer tendency of the learner,
The learning support system according to claim 1 , wherein the output unit outputs the ranking. An information processing device including a processor,
The processor,
obtaining the learner's answer to each question within a given learning scope;
determining whether the answer is correct;
Identifying a second answer tendency having at least a correct answer rate for unattended questions of the learner based on a first answer tendency having at least a correct answer rate of the learner indicated by each judgment result of each question;
selecting one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill a gap in the learning situation between the learner and the other learners by using at least the correct answer rates for the trained problems and untrained problems of the learner and the correct answer rates for the trained problems of the other learners, based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency having at least a correct answer rate for the trained problems of the other learners associated with the desired school selected by the learner;
outputting the one or more selected questions to the learner;
An information processing device that executes the above. An information processing device,
obtaining the learner's answer to each question within a given learning scope;
determining whether the answer is correct;
Identifying a second answer tendency having at least a correct answer rate for unattended questions of the learner based on a first answer tendency having at least a correct answer rate of the learner indicated by each judgment result of each question;
selecting one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill a gap in the learning situation between the learner and the other learners by using at least the correct answer rates for the trained problems and untrained problems of the learner and the correct answer rates for the trained problems of the other learners, based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency having at least a correct answer rate for the trained problems of the other learners associated with the desired school selected by the learner;
outputting the one or more selected questions to the learner;
An information processing method for performing the above. In the information processing device,
obtaining the learner's answer to each question within a given learning scope;
determining whether the answer is correct;
Identifying a second answer tendency having at least a correct answer rate for unattended questions of the learner based on a first answer tendency having at least a correct answer rate of the learner indicated by each judgment result of each question;
selecting one or more problems based on the first answer tendency and the second answer tendency using a trained model that selects one or more problems that fill a gap in the learning situation between the learner and the other learners by using at least the correct answer rates for the trained problems and untrained problems of the learner and the correct answer rates for the trained problems of the other learners, based on a learning history including the first answer tendency, the second answer tendency, and a third answer tendency having at least a correct answer rate for the trained problems of the other learners associated with the desired school selected by the learner;
outputting the one or more selected questions to the learner;
A program that executes the following.

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