KR102637603B1 - Method and apparatus for providing user customized study contents - Google Patents

Abstract

According to an embodiment of the present disclosure, a method for providing user-customized learning content performed by a computing device includes receiving a request for presenting a problem for user learning from a user device, in response to the request for presenting a problem, determining at least one problem that the user can solve among a plurality of problems based on at least one of the user's personal information, the problem question range set by the user, or the user's solving activity information, and the determined at least one problem that can be solved by the user. and providing a problem to the user device.

Description

Method and apparatus for providing user customized learning content {METHOD AND APPARATUS FOR PROVIDING USER CUSTOMIZED STUDY CONTENTS}

This disclosure relates to a method and device for providing user-customized learning content.

As IT technology has recently developed, the number of learners using online education services is increasing. These online education services provide users with workbooks related to various fields such as language, mathematics, social studies, science, or humanities that correspond to the user's educational course, or problems directly created by educators (professional instructors, problem setting committee members, etc.). Provides learning content.

Users can solve the workbooks or questions provided according to the training course and receive learning content corresponding to the next level of difficulty. However, since the learning content provided in this way includes questions of standardized content corresponding to a designated educational course, it is difficult to view it as learning content that reflects the learning level of each user, and there is a limit to efficiently improving the user's learning ability.

Therefore, a method and device for providing user-customized learning content that can efficiently improve the user's learning level is required.

Korean Patent No. 10-2206599

The present disclosure has been devised in response to the above-described background technology, and seeks to provide a method and device for providing user-customized learning content.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

According to an embodiment of the present disclosure for solving the above-described problem, a method for providing user-customized learning content performed by a computing device includes the steps of receiving a request to submit a question for user learning from a user device; In response to the problem submission request, at least one problem that the user can solve among a plurality of problems based on at least one of the user's personal information, the problem problem range set by the user, or the user's solving activity information. It may include determining a problem and providing the determined at least one problem to the user device.

Alternatively, the user's personal information may include at least one of the user's age, gender, occupation, academic ability, or interests.

Alternatively, determining at least one problem that the user can solve may include determining at least one other user who has personal information similar to the user's personal information, and providing information to the at least one determined other user. It may include determining at least one problem that the user can solve among the plurality of problems based on the provided learning content.

Alternatively, before receiving the problem submission request, the method may further include generating tag information for each of the plurality of problems and storing the plurality of problems and the generated tag information in correspondence. You can.

Alternatively, the tag information may include the learning topic for each problem, the problem difficulty level of each problem, the number of letters constituting each problem, the number of technical terms included in each problem, and the number of technical terms constituting each problem. It may include at least one of the average length of sentences or the ratio of simple sentences and complex sentences constituting each problem.

Alternatively, the request includes information about a problem question range set by the user, wherein the problem question range includes at least one learning topic, question difficulty level, number of question characters, and technical terminology set by the user. It may include at least one of the number of sentences, average sentence length, ratio of simple sentences and complex sentences, or number of problems.

Alternatively, the step of determining at least one problem that the user can solve may include determining tag information corresponding to the problem question range among preset tag information for each of the plurality of problems, and

It may include determining at least one problem corresponding to the determined tag information among the plurality of problems as at least one problem that the user can solve.

Alternatively, the step of determining at least one problem that the user can solve may include collecting tag information and solving activity information for at least one problem solved by the user based on pre-stored detailed history information for each problem. determining a problem solving grade for each tag information of the user based on the collected tag information and the collected solving activity information, based on a problem solving grade for each tag information preset for each of a plurality of users. , determining at least one other user having the same level as the determined problem solving level for each tag information, and determining at least one problem that the user can solve based on learning content provided to the at least one determined other user. It may include steps.

Alternatively, the detailed history information for each problem may include at least one of the plurality of problems, tag information for each problem, at least one user who solved each problem, or information on the solving activity of each user. there is.

Alternatively, the solving activity information may include, the time taken by the plurality of users to solve each of the plurality of problems, the correct answer rate of the plurality of users for each of the plurality of problems, and the time taken by the plurality of users to solve each of the plurality of problems. It may include at least one of gaze pattern information while solving each of the problems or behavior pattern information while the plurality of users solve each of the plurality of problems.

Alternatively, the step of determining the problem solving grade according to the user's tag information may include classifying at least one problem solved by the user by tag information and solving the problem corresponding to the at least one problem classified according to the tag information. It may include determining a problem-solving grade for each tag information using at least one grade inference model learned to infer a problem-solving grade for each tag information based on activity information.

Alternatively, the step of determining at least one problem that can be solved by the user based on the learning content provided to the at least one determined other user may include difficulty levels for the problems provided to the at least one determined other user; Alternatively, it may be a step of determining at least one problem that the user can solve by considering whether the problem solving grade of at least one other user who solved the problems of the difficulty level is raised or lowered.

Alternatively, the step of determining at least one problem that the user can solve may include determining tag information corresponding to a problem question range set by the user among preset tag information for each of the plurality of problems. A step of collecting solving activity information for at least one problem solved by the user among the problems corresponding to the determined tag information, based on the tag information for the at least one problem and the collected solving activity information Determining a problem-solving grade for each tag information of the user, based on a problem-solving grade for each tag information preset for each of a plurality of users, at least one other device having the same grade as the determined problem-solving grade for each tag information It may include determining a user and determining at least one problem that the user can solve based on learning content provided to the at least one determined other user.

According to an embodiment of the present disclosure for solving the above-described problem, a computer program stored in a computer-readable storage medium, wherein the computer program is executed on one or more processors, performs the following operations for providing user-customized learning content. The operations include receiving a request for a question for user learning from a user device, and in response to the request for a question, the user's personal information, a problem question range set by the user, or the user's It may include an operation of determining at least one problem that the user can solve among a plurality of problems based on at least one of the solving activity information, and an operation of providing the determined at least one problem to the user device.

According to an embodiment of the present disclosure for solving the problems described above, a computing device for providing user-customized learning content includes at least one processor; and a memory, wherein the at least one processor receives a request for presenting a problem for user learning from a user device, and in response to the request for presenting a problem, includes the user's personal information, a problem setting range set by the user, or It may be configured to determine at least one problem that the user can solve among a plurality of problems based on at least one of the user's solving activity information, and provide the determined at least one problem to the user device.

The technical solutions obtainable from this disclosure are not limited to the solutions mentioned above, and other solutions not mentioned above will be clearly apparent to those skilled in the art from the description below. It will be understandable.

According to some embodiments of the present disclosure, in the present disclosure, learning content is provided based on at least one of the user's personal information, the problem question range set by the user, or the user's solving activity information, thereby determining the user's learning level or learning level. You can improve your abilities efficiently.

In addition, the present disclosure provides learning content in consideration of the user's past problem-solving history and the problem-solving history of other users with similar learning tendencies as the user, thereby providing learning content more suitable to the learning level or learning ability of each user. You can.

The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below. .

Various aspects will now be described with reference to the drawings, where like reference numerals are used to collectively refer to like elements. In the following examples, for purposes of explanation, numerous specific details are set forth to provide a comprehensive understanding of one or more aspects. However, it will be clear that such aspect(s) may be practiced without these specific details.
1 is a configuration diagram of an exemplary system for providing user-customized learning content according to an embodiment of the present disclosure.
Figure 2 is a block configuration diagram of a server according to an embodiment of the present disclosure.
Figure 3 is a flowchart illustrating an example of a method for providing user-customized learning content according to an embodiment of the present disclosure.
FIG. 4 is a flowchart illustrating an example of a method of determining at least one problem that a user can solve based on personal information according to an embodiment of the present disclosure.
FIG. 5 is a flowchart illustrating an example of a method of determining at least one problem that a user can solve based on a problem question range set by the user according to an embodiment of the present disclosure.
FIG. 6 is a flowchart illustrating an example of a method for setting tag information for each problem according to an embodiment of the present disclosure.
Figure 7 is an example diagram showing tag information for each problem according to an embodiment of the present disclosure.
FIG. 8 is a flowchart illustrating an example of a method of determining at least one problem that a user can solve among a plurality of problems based on tag information and problem-solving activity information according to an embodiment of the present disclosure.
Figure 9 is an example diagram for explaining detailed history information for each problem according to an embodiment of the present disclosure.
Figure 10 is an example diagram showing a problem solving grade for each tag information preset for each of a plurality of other users according to an embodiment of the present disclosure.
FIG. 11 is a flowchart illustrating an example of a method of determining a problem that a user can solve based on the problem range and problem solving grade according to an embodiment of the present disclosure.
Figure 12 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “a case containing only A,” “a case containing only B,” and “a case of combining A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

1 is a configuration diagram of an exemplary system for providing user-customized learning content according to an embodiment of the present disclosure.

Referring to FIG. 1, the system may include a user device 10 that requests questions for user learning and a server 100 that provides at least one problem suitable for the user according to the request. The components shown in FIG. 1 are exemplary, and additional components may exist or some of the components may be omitted.

According to some embodiments of the present disclosure, the user device 10 and the server 100 may mutually transmit and receive data for providing user-customized learning content according to some embodiments of the present disclosure through a wireless communication network. .

The user device 10 is a device for requesting questions for user learning. For example, all types of electronic devices that can connect to a wired/wireless network, such as a PC, laptop, mobile terminal, smart phone, tablet PC, etc. may include.

Specifically, the user device 10 may transmit a question submission request to the server 100 and receive at least one question from the server 100 in response to the question submission request.

According to some embodiments of the present disclosure, the user device 10 may be any entity capable of processing, storing, and transmitting and receiving any data, including a control unit, a storage unit (memory and persistent storage media), and a communication unit. .

The control unit in the present disclosure may be composed of one or more cores, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. It may include any type of processor for transmitting a question request to the server 100 by executing instructions stored in memory, such as a tensor processing unit). The processor may read the computer program stored in the memory and transmit a request for problem solving according to an embodiment of the present disclosure to the server 100.

The storage unit in the present disclosure may store a program for the operation of the control unit, and may temporarily or permanently store input/output data. The storage unit is a flash memory type, hard disk type, multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), and RAM (Random Access). Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, optical disk It may include at least one type of storage medium. This storage unit can be operated by control from the control unit. Additionally, memory and storage may be used interchangeably with each other in the present disclosure.

The communication unit in the present disclosure can connect the user device 10 to communicate with various devices. Specifically, the communication unit is connected to the server 100 and can exchange data between the user device 10 and the server 100.

The server 100 is a device for providing user-customized learning content in response to a question request from the user device 10, and may be any type of computer system, such as a computer, digital processor, portable device, or device controller. May include computer devices.

Specifically, the server 100 selects at least one problem that the user can solve among a plurality of problems based on at least one of the user's personal information, the problem question range set by the user, or the user's problem-solving activity information. may be determined, and at least one determined problem may be provided to the user device 10.

According to some embodiments of the present disclosure, the server 100 may be any entity capable of processing, storing, and transmitting and receiving any data, including a processor, a storage unit (memory and persistent storage media), and a communication unit.

A processor in the present disclosure may consist of one or more cores, and memory, such as a central processing unit (CPU), general purpose graphics processing unit (GPGPU), tensor processing unit (TPU), graphics processing unit (GPU), etc. of a computing device. It may include any type of processor that provides user-customized learning content in response to a question request by executing instructions stored on the computer. The processor can read the computer program stored in the memory and provide user-customized learning content according to an embodiment of the present disclosure.

The memory in the present disclosure may store a program for the operation of a processor, and may temporarily or permanently store input/output data. Memory includes flash memory type, hard disk type, multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, magnetic memory, magnetic memory. It may include at least one type of storage medium among disks and optical disks. These memories can be operated under processor control. Additionally, memory and storage may be used interchangeably with each other in the present disclosure.

The storage unit according to the present disclosure can store various data for providing user-customized learning content.

The communication unit in the present disclosure can connect the server 100 to communicate with various devices. Specifically, the communication unit can exchange data with the user device 10.

Figure 2 is a block configuration diagram of a server according to an embodiment of the present disclosure. The configuration of the server 100 shown in FIG. 2 is only a simplified example. In one embodiment of the present disclosure, the server 100 may include different configurations for performing the computing environment of the server 100, and only some of the disclosed configurations may configure the server 100.

The server 100 may include a communication unit 110, a storage unit 120, and a processor 130. However, the above-described components are not essential for implementing the server 100, so the server 100 may have more or less components than the components listed above. Here, each component may be composed of a separate chip, module, or device, or may be included in one device.

The communication unit 110 according to an embodiment of the present disclosure may include any type of wired/wireless Internet module for network connection. In the presented embodiment, the communication unit 110 may use various types of known wired networks and wireless networks.

According to some embodiments of the present disclosure, the communication unit 110 connects the server 100 to enable communication with an external device. The communication unit 110 may be connected to the user device 10 using wired/wireless communication, receive a question request from the user device 10, and transmit at least one question to the server 100.

According to an embodiment of the present disclosure, the storage unit 120 may store any type of information generated or determined by the processor 130 and any type of information received by the communication unit 110. According to some embodiments of the present disclosure, the storage unit 120 may store various data used to provide customized learning content according to a problem submission request from the user device 10.

Storage unit 120 may include memory and/or persistent storage media. The storage unit 120 includes flash memory type, hard disk type, multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, It may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk. The server 100 may operate in relation to web storage that performs the storage function of the storage unit 120 on the Internet. The description of the storage unit described above is merely an example, and the present disclosure is not limited thereto.

The processor 130 may consist of one or more cores and may include a processor for data analysis and processing, such as a central processing unit, a general-purpose graphics processing unit, or a tensor processing unit of a computing device.

The processor 130 may read the computer program stored in the storage unit 120 and provide user-customized learning content according to an embodiment of the present disclosure.

Specifically, when a problem submission request is received from the user device 10, the processor 130 responds to the received problem submission request by selecting the user's personal information, the problem assessment range set by the user, or the user's problem solving activity information. Customized learning content can be provided based on at least one of the following.

Hereinafter, a method for providing user-customized learning content will be described in detail with reference to FIGS. 3 to 11. In the presented embodiment, the operations of FIGS. 3 to 11 may be performed by the processor 130 of the server 100.

Figure 3 is a flowchart illustrating an example of a method for providing user-customized learning content according to an embodiment of the present disclosure.

Referring to FIG. 3, the processor 130 receives a request for a question for user learning from the user device 10 (S300), and in response to the request for a question, the user's personal information and a question set by the user are provided. Based on at least one of the scope or the user's problem-solving activity information, at least one problem that the user can solve among the plurality of problems is determined (S310). This will be described in detail below with reference to FIGS. 4 to 11.

FIG. 4 is a flowchart illustrating an example of a method of determining at least one problem that a user can solve based on personal information according to an embodiment of the present disclosure.

Referring to FIG. 4, the processor 130 determines at least one other user who has personal information similar to the user's personal information (S400), and allows the user to interpret based on learning content provided to at least one other user. Determine at least one problem (S410). Here, personal information may include, but is not limited to, at least one of age, gender, occupation, academic ability, or field of interest. Additionally, academic ability may mean academic knowledge, understanding, application, analysis, synthesis, or evaluation, but is not limited to this.

For example, the processor 130 determines at least one other user corresponding to at least one of a similar age group, the same gender, a similar occupation, or a similar field of interest as the user, and at least one of the problems provided to the at least one determined other user. For one problem, at least one problem that can be solved by the user can be determined.

Hereinafter, a method of determining at least one problem that a user can solve based on the problem range set by the user will be described with reference to FIGS. 5 and 6.

FIG. 5 is a flowchart illustrating an example of a method of determining at least one problem that a user can solve based on a problem question range set by the user according to an embodiment of the present disclosure.

Referring to FIG. 5, the processor 130 determines tag information corresponding to the problem question range set by the user among preset tag information for each of a plurality of problems (S500). Here, the preset tag information for each of the plurality of problems includes the learning topic for each problem, problem difficulty, number of letters constituting the problem, number of technical terms included in the problem, average length of sentences constituting the problem, Alternatively, it may include at least one of the ratio of simple sentences and complex sentences that constitute the problem. A method of setting such tag information will be described below with reference to FIG. 6.

FIG. 6 is a flowchart illustrating an example of a method for setting tag information for each problem according to an embodiment of the present disclosure.

Referring to FIG. 6, the processor 130 generates tag information for each of a plurality of problems (S600) and stores the plurality of problems and the tag information generated for each of the problems in correspondence (S610). ). Here, the plurality of problems may include problems collected from each of a plurality of workbooks related to various learning fields, or problems posed by educators engaged in various learning fields, but are not limited to this.

Specifically, before receiving a problem submission request from the user device 10, the processor 130 determines the learning topic for each of the plurality of problems, the problem difficulty level, the number of letters constituting the problem, and technical terms included in the problem. At least one of the number, the average length of sentences constituting the problem, or the ratio of simple sentences and complex sentences constituting the problem can be determined.

To determine a learning topic for each problem, the processor 130 may use an artificial intelligence-based topic inference model that has been trained to infer the topic by analyzing sentences or words that make up each problem. These topic inference models may be, but are not limited to, LDA (Latent Dirichlet Allocation) or HDP (Hierarchical Dirichlet Process) based on machine learning algorithms for topic modeling, and various artificial intelligence models for topic modeling. This can be used.

In order to determine the problem difficulty level for each problem, the processor 130 may obtain the difficulty level specified for each problem in the workbook or the difficulty level set by the test taker of the problem. In various embodiments, the processor 130 may use an artificial intelligence-based classification model learned to classify problems by difficulty, but is not limited to this. For example, the classification model may be an artificial intelligence-based model that is pre-trained based on at least one of the problem, the time taken to solve the problem, or the difficulty level set for the problem.

To determine the number of letters constituting each problem, the processor 130 may use an algorithm for calculating the number of letters. The processor 130 uses this algorithm to separate the text constituting the problem by word, The number of separated words can be calculated.

To determine the number of technical terms included in each problem, the processor 130 may use a technical terminology dictionary for each field. For example, the processor 130 may determine words that match technical terms recorded in a technical terminology dictionary for each field among texts constituting the problem, and calculate the number of words determined.

In order to determine the average length of the sentences constituting each problem, the processor 130 divides the text constituting each problem into sentences, calculates the length of each separated sentence, and averages the length calculated for each sentence. The value can be calculated. To classify text into sentences, the processor 130 may use Natural Language Processing (NLP) technology, but is not limited to this. To calculate the sentence length, the processor 130 may use an algorithm that calculates the length of a string or the number of characters, but is not limited to this.

In order to determine the ratio of simple sentences and complex sentences constituting each problem, the processor 130 divides the text constituting each problem into simple sentences, calculates the number of separated simple sentences, divides the text into complex sentences, and divides the text into complex sentences. The number of can be calculated. Subsequently, the processor 130 may calculate the ratio of simple sentences and complex sentences based on the calculated number of simple sentences and the calculated number of complex sentences. The processor 130 may use the above-described natural language processing technology to classify text into simple sentence units and complex sentence units, but is not limited to this.

In this way, the tag information set for each problem can be exemplarily shown in FIG. 7.

Figure 7 is an example diagram showing tag information for each problem according to an embodiment of the present disclosure.

Referring to FIG. 7, the processor 130 generates problem-specific tag information 700 that corresponds information indicating the learning topic, problem difficulty, number of characters, number of technical terms, average sentence length, and simple/complex sentence ratio determined for each problem. can be saved. For example, for problem A1, the processor 130 determines that the learning topic is 'humanities', the problem difficulty is 'medium', the number of characters is '998 words', the number of technical terms is '20 words', and the average sentence length is 'medium'. It is '15 words' and can be stored by matching tag information 'Tag A1' with a simple sentence/complex sentence ratio of '0.2'.

Referring again to FIG. 5, the problem question range set by the user may be included in the request received from the user device 10 or may be received together with the request. In addition, the scope of the problem set by the user includes at least one learning topic set by the user, the problem difficulty level set by the user, the number of characters set by the user for the problem, the number of technical terms set by the user for the problem, and the number of technical terms set by the user for the problem. It may include at least one of the average sentence length set by the user for the problem, the ratio of simple sentences and complex sentences set by the user for the problem, or the number of problems set by the user.

For example, at least one learning topic set by the user is 'Science', the difficulty level of the problem set by the user is 'Medium', the number of characters set by the user for the character is '2000', and the user's The number of technical terms set by the user is '30', the average sentence length set by the user for the problem is '25 characters', the ratio of simple sentences and complex sentences set by the user for the problem is '1.5', and the average sentence length set by the user for the problem is '1.5'. Assume that the number of problems set is '10'. In this case, the processor 130 determines that among the preset tag information for each of the plurality of problems, the learning topic is 'science', the problem difficulty level is 'medium', the number of characters is '2000 characters', and the number of technical terms is '30'. ', the average sentence length is '25 characters', and the ratio of simple sentences and complex sentences is '1.5', and 10 tag information can be determined.

The processor 130 determines at least one problem corresponding to at least one determined tag information as at least one problem that the user can solve (S510). For example, the processor 130 may determine 10 problems corresponding to 10 pieces of determined tag information as at least one problem that the user can solve.

Hereinafter, a method of determining at least one problem that a user can solve among a plurality of problems based on tag information and problem solving activity information will be described with reference to FIGS. 8 to 10.

FIG. 8 is a flowchart illustrating an example of a method of determining at least one problem that a user can solve among a plurality of problems based on tag information and problem-solving activity information according to an embodiment of the present disclosure.

Referring to FIG. 8, the processor 130 collects tag information and problem-solving activity information for at least one problem solved by the user in the past based on pre-stored detailed history information for each problem (S800). Here, the pre-stored detailed history information for each problem may include a plurality of problems, tag information for each problem, at least one user who solved each problem, and information on the solving activity of at least one user for each problem. In addition, information on at least one user's problem-solving activity includes information about at least one problem solved by the user prior to requesting the question, the time spent solving each problem, the user's percentage of correct answers for each problem, and the number of users solving each problem. It may include at least one of gaze pattern information while solving a problem or behavior pattern information while solving each problem. For example, gaze pattern information includes the number of gaze pauses or gaze reversals for each of a plurality of users, and behavior pattern information includes the width of left and right movements of the face of each of the plurality of users, the width of upward and downward movement of the face, and the number of times the gaze is reversed. It may include, but is not limited to, angles or arm movements. Detailed history information for each pre-stored problem will be described with reference to FIG. 9.

Figure 9 is an example diagram for explaining detailed history information for each problem according to an embodiment of the present disclosure.

Referring to FIG. 9, the processor 130 generates a plurality of problems (problem A1, problem A2, problem A3, ??, problem B1, ??) and tag information for each problem (tags A1, A2, A3, ??). ?, B1, ??), at least one user who solved each problem (User1, User2, User3, User4, ??), and solving activity information collected for each user (User1_A1 solving activity information, User 2_A1 solution activity information, detailed history information (900) for each problem that corresponds to ??) can be stored. For example, the processor 130 includes tag A1, which is tag information for problem A1, user 1 and user 2 who solved problem A1, and user 1's solving activity information (user 1_A1 solving activity information) collected in solving problem A1. ) and User 2's solving activity information (User 2_A1 solving activity information) can be stored in correspondence.

Referring again to FIG. 8, the processor 130 determines the user's problem solving grade for each tag information based on the collected tag information and the collected solving activity information (S810).

Specifically, the processor 130 is based on the collected tag information and the collected solving activity information. The user's problem solving grade by learning topic, problem solving grade by problem difficulty, problem solving grade by number of letters, and problem solving grade by number of technical terms. , at least one of a problem solving grade based on average sentence length or a problem solving grade based on simple sentence/complex sentence ratio can be determined. Here, the problem solving grade by tag information may mean a grade indicating the ease or difficulty in solving problems classified by tag information for at least one user. The higher the problem solving grade (upper), the easier it can be understood that the user solved the problems classified by tag information. The lower the problem solving grade (lower), the more difficult it can be understood that the user solved the problems classified by tag information. there is.

In order to determine the problem solving grade according to tag information, the processor 130 selects a plurality of problems solved by the user by learning topic, problem difficulty, number of characters, number of technical terms, average sentence length, or simple/complex sentence ratio. Divided into categories, solution activity information corresponding to problems by classified learning topics, solution activity information corresponding to problems by problem difficulty, solution activity information corresponding to problems by number of letters, solution activity information corresponding to problems by number of technical terms, Using at least one of the solution activity information corresponding to problems by average sentence length or the solution activity information corresponding to problems by simple sentence/complex sentence ratio, problem solving grade by learning topic, problem solving grade by problem difficulty, problem solving grade by number of letters, At least one of the problem solving grade according to the number of technical terms, the problem solving grade according to the average sentence length, or the problem solving grade according to the simple sentence/complex sentence ratio can be determined. For example, in order to determine the problem solving grade for each learning topic, the processor 130 divides the problems solved by the user into learning topics such as humanities, social science, science, and technology, and divides them into at least one problem related to the humanities and one related to society. The user's problem solving level for humanities, social studies, science, and technology may be determined based on the solving activity information collected for each of at least one problem, at least one problem related to science, and at least one problem related to technology. If the time taken to solve at least one problem related to the humanities is more than a preset threshold time, the correct answer rate is lower than the preset threshold rate, and the gaze pattern and behavior pattern are dispersed, the processor 130 determines the user's humanities related problem. The problem solving grade can be determined as 'low', the lowest grade. In various embodiments, if the time taken to solve at least one problem related to technology is less than the threshold time, the correct answer rate is higher than the threshold rate, and the gaze pattern and behavior pattern are not distributed, the processor 130 may determine the user's humanities problem. The problem solving grade can be determined as ‘Award’, which is the highest grade.

In various embodiments, in order to determine the problem solving grade for each tag information, the processor 130 uses an artificial intelligence-based grade inference model learned to infer the problem solving grade for each tag information of the user based on the user's solving activity information. The problem solving grade can be determined for each tag information. This grade inference model may be provided for each tag information, but is not limited to this. For example, a grade inference model by learning topic, a grade inference model by problem difficulty, a grade inference model by number of letters, a grade inference model by number of technical terms, a grade inference model by average sentence length, or a grade inference model by simple/complex sentence ratio are used. It can be.

In order to learn the rating inference model, the processor 130 analyzes each of the plurality of problems solved by each user for a plurality of users by learning topic, problem difficulty, number of characters, number of technical terms, and average sentence length. Alternatively, it can be classified by simple sentence/complex sentence ratio, and the solution activity information corresponding to each classified problem can be used as learning data. Supervised learning may be used to learn this grade inference model, but is not limited to this.

The processor 130 determines at least one user having the same grade as the determined problem-solving grade by tag information, based on the problem-solving grade by tag information preset for each of the plurality of other users (S820). Here, the problem solving grade for each tag information preset for each of the plurality of other users may be shown as shown in FIG. 10.

Figure 10 is an example diagram showing a problem solving grade for each tag information preset for each of a plurality of other users according to an embodiment of the present disclosure.

Referring to FIG. 10, the problem solving grade (1000) for each tag information preset for each of a plurality of other users is calculated by learning topic, problem difficulty, number of letters, number of technical terms, and average for each of the plurality of users. Includes problem solving grades by sentence length and simple/complex sentence ratio. For example, if the user's problem solving grade for 'humanities' is 'medium' and the technology is 'low', the processor 130 determines that the problem solving grade for 'humanities' is 'medium' and the technology is 'low'. 'User 2' corresponding to ' can be determined to be at least one user with the same level.

Referring again to FIG. 8, the processor 130 determines at least one problem that the user can solve based on learning content provided to at least one determined other user (S830). Specifically, the processor 130 considers the level of difficulty of at least one problem provided to at least one determined other user, or whether to increase or decrease the problem solving grade of at least one other user who solved a problem of the corresponding level of difficulty. The user can determine at least one problem that can be solved.

For example, when 'User 2' is provided with at least one problem whose difficulty level for 'Humanities' is 'High' and the problem difficulty level for 'Technology' is 'Medium', the processor 130 selects a plurality of problems. At least one problem provided to 'User 2' with a problem difficulty level of 'Large' for 'Humanities' and a problem difficulty level of 'Medium' for 'Technology' is converted into at least one problem that the user can solve. You can decide.

In various embodiments, in order to determine at least one problem that can be solved by a user in consideration of whether the problem solving grade of at least one other user is raised or lowered, the processor 130 determines at least one problem that can be solved by the user by tag information for each of the plurality of users. The history of problem solving grades can be saved, and learning content can be provided based on the saved history. Specifically, the processor 130 may store a history of whether the problem solving grade for each tag information of a plurality of users has been raised or lowered by learning content provided to each of the plurality of users.

For example, 'User 2' is provided with at least one problem with a problem difficulty level of 'Large' for 'Humanities' and a problem difficulty level of 'Medium' for 'Technology', and at least one problem is solved. If 'User 2's' problem solving grade for 'Humanities' and 'Technology' is increased, the processor 130 determines at least one problem provided to 'User 2', or higher than at least one problem provided to 'User 2'. The problem of difficulty may be determined as at least one problem that the user can solve. In various embodiments, 'User 2' is provided with at least one problem in which the problem difficulty level for 'humanities' is 'high' and the problem difficulty level for 'technology' is 'medium', and at least one problem is solved. If 'User 2's' problem solving grade for 'Humanities' and 'Technology' is lowered, the processor 130 selects a problem of lower difficulty level than at least one problem provided to 'User 2', and provides at least one problem that the user can solve. It can be decided on a single issue.

In this way, in the present disclosure, learning content more suitable for the user's learning level can be provided by providing learning content by considering not only the user's past problem solving history but also the problem solving history of other users with similar learning tendencies as the user.

Hereinafter, a method of determining a problem that a user can solve based on the problem range set by the user and the user's problem solving grade will be described with reference to FIG. 11.

FIG. 11 is a flowchart illustrating an example of a method of determining a problem that a user can solve based on the problem range and problem solving grade according to an embodiment of the present disclosure. In the presented embodiment, a case where a user has a history of solving problems in the past (i.e., user's solving activity information) will be described.

Referring to FIG. 11, the processor 130 determines tag information corresponding to the problem question range set by the user among preset tag information for each of a plurality of problems (S1100). In other words, the processor 130 provides tag information corresponding to at least one of the learning topic set by the user, problem difficulty, number of characters in the problem, number of technical terms in the problem, average sentence length of the problem, and ratio of simple sentences and complex sentences in the problem. You can decide.

Next, the processor 130 collects solving activity information for at least one problem solved by the user among the problems corresponding to the determined tag information (S1110), and tag information and the collected solving activity information for the at least one problem. Based on this, the problem solving grade for each user's tag information is determined (S1120). To determine the problem solving level for each user's tag information, the processor 130 may use the same method as described above in S710 of FIG. 7 . That is, the processor 130 divides at least one problem solved by the user by learning topic, problem difficulty, number of letters, number of technical terms, average sentence length, or simple sentence/complex sentence ratio, and categorizes each problem by learning topic. Solving activity information corresponding to the problem, solving activity information corresponding to the problem by problem difficulty, solving activity information corresponding to the problem by number of characters, solving activity information corresponding to the problem by number of technical terms, solving activity information corresponding to the problem by average sentence length By using at least one of the solution activity information or the solution activity information corresponding to the problem by simple sentence/complex sentence ratio, problem solving grade by learning topic, problem solving grade by problem difficulty, problem solving grade by number of letters, problem solving grade by number of technical terms, At least one of a problem solving grade based on average sentence length or a problem solving grade based on simple sentence/complex sentence ratio can be determined. For example, the processor 130 may provide solving activity information by learning topic, solving activity information by problem difficulty, solving activity information by number of letters, solving activity information by number of technical terms, solving activity information by average sentence length, or short sentence/ Based on at least one of the solving activity information by complex sentence ratio, problem solving grade by learning topic, problem solving grade by problem difficulty, problem solving grade by number of letters, problem solving grade by number of technical terms, problem solving grade by average sentence length, or short sentence/ Learning using at least one grade inference model trained to infer at least one of the problem solving grades by complex sentence ratio, problem solving grade by topic, problem solving grade by problem difficulty, problem solving grade by number of letters, problem solving grade by number of technical terms , at least one of a problem solving grade based on average sentence length or a problem solving grade based on simple sentence/complex sentence ratio can be determined.

The processor 130 determines at least one other user having the same grade as the determined problem-solving grade by tag information, based on the problem-solving grade by tag information preset for each of the plurality of other users (S1130). In other words, the processor 130 may determine at least one other user corresponding to the same level as the user's problem-solving level according to tag information from the problem-solving level according to tag information for a plurality of other users, as shown in FIG. 9. For example, when the problem difficulty level is 'High', it is assumed that the user's determined problem solving grade is 'Low'. In this case, the processor 130 may determine 'User 1' to be at least one other user who has the same problem solving level as the user.

The processor 130 determines at least one problem that the user can solve based on learning content provided to at least one determined other user (S1140). For example, assume that 'User 1' and 'User 2' are at least one other user who has the same level as the problem solving level according to the user's tag information. In this case, the processor 130 may combine the problems provided to 'User 1' and 'User 2' respectively and determine the combined problems as at least one problem solved by the user.

In various embodiments, the processor 130 determines whether to raise or lower the problem solving grade for each of 'User 1' and 'User 2' so that the problem solving grade of 'User 1' is raised and the problem solving grade of 'User 2' is raised. When the solution level is raised, the problems provided to 'User 1' and 'User 2' can be combined and the combined problems can be determined as at least one problem solved by the user. If the problem solving grade of 'User 1' is increased but the problem solving grade of 'User 2' is lowered, the processor 130 selects the problems provided to 'User 1' and those of lower difficulty level than the problems provided to 'User 2'. By combining problems, the combined problems can be determined to be at least one problem solved by the user.

In various embodiments, when the user's past problem-solving history does not exist, the processor 130 determines tag information corresponding to the problem question range set by the user (same as S1100) and at least one tag information corresponding to the determined tag information. Information on the solving activities of at least one other user for the problem can be collected. Here, the solving activity information of at least one other user indicates whether the problem solving grade of at least one other user was raised or lowered as the difficulty level of the provided problems was adjusted based on the problem solving grade of each tag information of the at least one other user. Additional information may be included. For example, assume that when at least one other user who solved a problem whose learning topic is 'humanities' solved a problem with higher difficulty than the corresponding problem, the problem solving rating for each learning topic was lowered. In this case, the processor 130 may determine that a problem with a level of difficulty lower than that set by the user is a problem that the user can solve.

Referring again to FIG. 3, the processor 130 provides at least one determined problem to the user device 10 (S320). In other words, the processor 130 may provide at least one determined problem to the user device 10 as user-customized learning content.

As such, in the present disclosure, the user's learning level or learning ability can be efficiently improved by providing learning content based on at least one of the user's personal information, the problem question range set by the user, or the user's solving activity information. there is.

Figure 12 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has generally been described above as being capable of being implemented by a computing device, those skilled in the art will understand that the present disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or in hardware and software. It will be well known that it can be implemented as a combination.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure are applicable to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1200 is shown that implements various aspects of the present disclosure, including a computer 1202, which includes a processing unit 1204, a system memory 1206, and a system bus 1208. do. System bus 1208 couples system components, including but not limited to system memory 1206, to processing unit 1204. Processing unit 1204 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1204.

System bus 1208 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1206 includes read only memory (ROM) 1210 and random access memory (RAM) 1212. The basic input/output system (BIOS) is stored in non-volatile memory 1210, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1202, such as during startup. Contains routines. RAM 1212 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1202 may also include an internal hard disk drive (HDD) 1214 (e.g., EIDE, SATA)—the internal hard disk drive 1214 may also be configured for external use within a suitable chassis (not shown). -, a magnetic floppy disk drive (FDD) 1216 (e.g., for reading from or writing to a removable diskette 1218), and an optical disk drive 1220 (e.g., a CD-ROM for reading the disk 1222 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1214, magnetic disk drive 1216, and optical disk drive 1220 are connected to system bus 1208 by hard disk drive interface 1224, magnetic disk drive interface 1226, and optical drive interface 1228, respectively. ) can be connected to. The interface 1224 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1202, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also recognize removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the example operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in drives and RAM 1212, including an operating system 1230, one or more application programs 1232, other program modules 1234, and program data 1236. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1212. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1202 through one or more wired/wireless input devices, such as a keyboard 1238 and a pointing device such as mouse 1240. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1204 through an input device interface 1242, which is often connected to the system bus 1208, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1244 or other type of display device is also connected to system bus 1208 through an interface, such as a video adapter 1246. In addition to monitor 1244, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1202 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1248, via wired and/or wireless communications. Remote computer(s) 1248 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1202. For simplicity, only memory storage device 1250 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1252 and/or a larger network, such as a wide area network (WAN) 1254. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1202 is connected to local network 1252 through wired and/or wireless communications network interfaces or adapters 1256. Adapter 1256 may facilitate wired or wireless communication to a local area network (LAN) 1252, which also includes a wireless access point installed thereon for communicating with wireless adapter 1256. When used in a WAN networking environment, the computer 1202 may include a modem 1258 or be connected to a communicating computing device on the WAN 1254 or to establish communications over the WAN 1254, such as over the Internet. Have other means. Modem 1258, which may be internal or external and a wired or wireless device, is coupled to system bus 1208 via serial port interface 1242. In a networked environment, program modules described for computer 1202, or portions thereof, may be stored in remote memory/storage device 1250. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1202 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) enables connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a cell tower. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (15)

A method for providing user-customized learning content performed by a computing device, comprising:
Receiving a request to submit questions for user learning from a user device;
In response to the problem submission request, determining at least one problem that the user can solve among a plurality of problems based on the user's personal information, the problem problem range set by the user, and the user's solving activity information. steps; and
Providing the determined at least one problem to the user device,
The above solving activity information is,
Gaze pattern information related to the user's gaze while solving the at least one problem - the gaze pattern information includes the number of gaze stops and gaze reversals - and behavior pattern information related to the user's behavior - the behavior pattern information includes the left and right movement width of the face, the up and down movement width of the face, the face angle, and the arm movement,
The step of determining at least one problem that the user can solve is:
collecting tag information and solving activity information for at least one problem solved by the user based on pre-stored detailed history information for each problem;
determining a problem solving grade for each tag information of the user based on the collected tag information and the collected solving activity information;
Based on the problem solving level for each tag information preset for each of the plurality of users, determining a plurality of other users having the same level as the determined problem solving level for each tag information; and
Determining at least one problem that can be solved by the user based on the determined learning content provided to a plurality of other users - Determining the at least one problem includes combining a plurality of problems provided to the other users Including determining said one problem - comprising,
How to provide learning content. The method of claim 1, wherein the user's personal information is:
Containing at least one of the user's age, gender, occupation, academic ability, or interests,
How to provide learning content. delete delete The method of claim 1, wherein the tag information is:
Learning topic for each problem, problem difficulty of each problem, number of letters constituting each problem, number of technical terms included in each problem, average length of sentences constituting each problem, or each problem Containing at least one of the ratio of simple sentences and complex sentences that make up,
How to provide learning content. The method of claim 1, wherein the request is:
Contains information about the scope of the problem set by the user,
The scope of the above question is,
Set by the user, including at least one of at least one learning topic, problem difficulty, number of problem letters, number of technical terms, average sentence length, ratio of simple sentences and complex sentences, or number of problems,
How to provide learning content. delete delete delete The method of claim 1, wherein the solving activity information is:
Containing at least one of the time taken by the plurality of users to solve each of the plurality of problems and the correct answer rate of the plurality of users for each of the plurality of problems,
How to provide learning content. The method of claim 1, wherein the step of determining the problem solving grade according to the user's tag information comprises:
Classifying at least one problem solved by the user by tag information; and
Determining a problem solving grade for each tag information using at least one grade inference model learned to infer a problem solving grade for each tag information based on solving activity information corresponding to at least one problem classified for each tag information. Including,
How to provide learning content. delete delete A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, performs the following operations to provide customized learning content, the operations being:
An operation of receiving a request to submit a problem for user learning from a user device;
In response to the problem submission request, determining at least one problem that the user can solve among a plurality of problems based on the user's personal information, the problem problem range set by the user, and the user's solving activity information. action; and
An operation of providing the determined at least one problem to the user device,
The above solving activity information is,
Gaze pattern information related to the user's gaze while solving the at least one problem - the gaze pattern information includes the number of gaze stops and gaze reversals - and behavior pattern information related to the user's behavior - the behavior pattern information includes the left and right movement width of the face, the up and down movement width of the face, the face angle, and the arm movement,
The operation of determining at least one problem that the user can solve is:
collecting tag information and solving activity information for at least one problem solved by the user based on pre-stored detailed history information for each problem;
determining a problem solving grade for each tag information of the user based on the collected tag information and the collected solving activity information;
An operation of determining a plurality of other users having the same level as the determined problem-solving level according to tag information, based on the problem-solving level according to tag information preset for each of the plurality of users; and
An operation of determining at least one problem that can be solved by the user based on the determined learning content provided to the other users - The operation of determining the at least one problem is performed by combining a plurality of problems provided to the other users. Including determining said one problem - comprising,
A computer program stored on a computer-readable storage medium.
A computing device for providing user-customized learning content,
at least one processor; and
Contains memory,
The at least one processor:
Receive a request to submit questions for user learning from the user device,
In response to the problem submission request, determining at least one problem that the user can solve among a plurality of problems based on the user's personal information, the problem problem range set by the user, and the user's solving activity information. do,
configured to provide the determined at least one problem to the user device,
The above solving activity information is,
Gaze pattern information related to the user's gaze while solving the at least one problem - the gaze pattern information includes the number of gaze stops and gaze reversals - and behavior pattern information related to the user's behavior - the behavior pattern information includes the left and right movement width of the face, the up and down movement width of the face, the face angle, and the arm movement,
Determining at least one problem that the user can solve includes:
collect tag information and solution activity information for at least one problem solved by the user based on pre-stored detailed history information for each problem;
determining a problem solving grade for each tag information of the user based on the collected tag information and the collected solving activity information;
Based on the problem-solving level for each tag information preset for each of the plurality of users, determining a plurality of other users having the same level as the determined problem-solving level for each tag information; and,
Determine at least one problem that can be solved by the user based on the determined learning content provided to the other users - Determining the at least one problem is performed by combining a plurality of problems provided to the other users to determine the one problem. involving deciding a problem - involving doing;
Computing device.

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