KR102438104B1 - Customized learning problem solving contents providing system, method and computer program - Google Patents

Abstract

The customized learning problem solving content providing system according to an embodiment of the present invention is accessible to at least one learner terminal, tests the learner's level in connection with the video providing platform, and provides a customized learning problem solving video and a learning server that receives problem information from the learner terminal, provides at least one problem solving video corresponding to the problem, and searches a database for recommended problems similar to the corresponding problem.

Description

Customized learning problem solving contents providing system, method and computer program

The present invention relates to a learning problem-solving content providing system, and more particularly, to a customized learning problem-solving content providing system that provides problem-solving content in consideration of a learner's learner level.

In the past, most of the learning was done using a normal study sheet in which an answer sheet and an explanation sheet were attached to the existing study sheet to help the learner's learning.

However, in general, even if the worksheet is configured to suit the learner's level to some extent, there is a limit to recommending items appropriate to the level, there is a difficulty in managing incorrect answers, and recommending personalized questions related to incorrect answers is difficult. impossible.

Recently, to compensate for these shortcomings, a new type of smart learning system is being developed that measures the level of learners using terminals such as mobile phones and computers, recommends personalized problems, or helps learning. The disadvantage of the system is that it does not help the learning by the offline study paper efficiently, but it has to do new learning in parallel with the existing learning, so there was a double burden on the learner.

In addition, the existing online learning system provides uniform problem solving according to the learner's level, and the problem solving provision method is set, so there is a problem that problem solving is provided that is not suitable for the learner, and the content of problem solving is poor or evaluation is difficult. It was a learning system structure that could lead to continuous quality degradation by failing to perform.

Therefore, in order to solve the above problems, a customized learning problem-solving content providing system that searches for and provides customized problem-solving videos corresponding to the problems presented by learners and provides high-quality problem-solving videos according to the evaluation. research became necessary.

Korea Patent No. 10-2002914 (registered on July 17, 2019)

It is an object of the present invention to provide a customized learning problem-solving video to help the learner's learning according to the learner's level, and to provide a customized learning problem-solving content providing system that provides a service in connection with the video providing platform with a problem-solving video considering the learner's level will provide

The customized learning problem solving content providing system according to an embodiment of the present invention is accessible to at least one learner terminal, tests the learner's level in connection with the video providing platform, and provides a customized learning problem solving video and a learning server that receives problem information from the learner terminal, provides at least one problem solving video corresponding to the problem, and searches a database for recommended problems similar to the corresponding problem.

The learning server includes: a data analysis unit for extracting the text of the problem by receiving the problem image taken through the learner terminal or the problem information directly input to the learner terminal; a level matching unit that evaluates the learning level of the learner and performs a level test so that the learning level can be provided with a problem-solving video tailored to the learning level; an item matching unit that identifies problems included in the problem information based on the text extracted from the data analysis unit, and searches the database for problems that are the same as or similar to the corresponding problem; an answer providing unit that searches for a video of solving a problem for the problem provided by the question matching unit in association with a video providing platform, and receives a video of solving a problem corresponding to the problem from the video providing platform as a result of the search and provides it to the learner terminal; It further includes a problem recommendation unit for providing a recommended problem suitable for the learner based on the learner level by the level test and the problem information provided from the learner terminal.

The data analysis unit is characterized in that it uses an optical character recognition (OCR) technology for extracting text from the image to confirm the problem content in the problem image.

The answer providing unit may provide the video list in the form of a list by recommendation priority when there are a plurality of searched problem-solving videos, and the recommendation priority is the learner level or priority by expert recommendation, the area around the learner, the number of views, the rating ( It is characterized in that it is set to any one of the number of likes) and subscribers.

When the problem recommendation unit fails to collect problem information and information on the learner level by the level test initially in the learning server, the problem-solving video or recommended problems provided as priority is a cold start of the recommendation algorithm. ) is used to search for conditions similar to those of the learner, and problem-solving videos or recommended problems provided to past learners with the most similar conditions are provided to the learner terminal.

The method for providing customized learning problem-solving content according to an embodiment of the present invention is such that at least one learner terminal can be accessed, the level of the learner is tested in connection with the video providing platform, and a customized learning problem-solving video can be provided. In the method for providing answer content using a learning server that receives problem information to the learner terminal and provides at least one video of solving a problem corresponding to the problem, the learner terminal provides a solution to the problem transmitting the problem information in the form of an image to the learning server after shooting with the learner terminal; The learning server extracts the problem text from the transmitted problem information; The learning server requests a video video providing platform for solving a problem for the corresponding problem, and receiving a video search result from the video providing platform for solving a video problem; The learning server provides a search result list with a set priority when there are a plurality of video clips for solving the problem found in connection with the video providing platform, and transmitting the video for solving the problem when requested from the search result list. ; The learner terminal prepares evaluation information for the video after playing the video of solving the problem and provides it to the learning server; and providing a recommended problem similar to the corresponding problem from a previously stored database when the learner terminal is requested.

In the above, the learning server performing a level test for measuring the learner level in order to provide a customized video problem-solving; providing a problem-solving video for the corresponding problem included in the problem information after performing the level test; further includes

The customized learning problem solving content providing method is performed by a computer program stored in a computer-readable storage medium.

The customized learning problem-solving content providing system of the present invention has the advantage that the learner's learning effect is doubled because it can provide differentially customized problem-solving video according to the learner's level.

In addition, since it is possible to utilize the server database of the video providing platform in connection with the video providing platform existing on the video providing platform, the system installation and maintenance cost for building a vast amount of video content can be reduced, and it is easy to build. have.

In addition, it is possible to generate revenue by setting and providing advertisements in the form of interim insertion or hyperlinks to the video for solving problems. Advertising revenue can be provided from local academies, contributing to the revitalization of local academies, and by promoting the academy, learners have the effect of finding a suitable local academy according to their learning achievement.

1 is a block diagram showing the overall configuration of a content providing system for customized learning problem solving according to an embodiment of the present invention.
2 is a block diagram showing a detailed configuration of a learning server.
3 is a flowchart illustrating a process of a method for providing customized learning problem solving content according to an embodiment of the present invention.

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. However, the spirit of the present invention is not limited to the presented embodiment, and those skilled in the art who understand the spirit of the present invention may add, change, delete, etc. other elements within the scope of the same spirit, through addition, change, deletion, etc. Other embodiments included within the scope of the invention may be easily suggested, but this will also be included within the scope of the invention. In addition, components having the same function within the scope of the same idea shown in the drawings of each embodiment will be described using the same reference numerals.

1 is a block diagram showing the overall configuration of a content providing system for customized learning problem solving according to an embodiment of the present invention.

As shown in FIG. 1, the customized learning problem solving content providing system of the present invention allows a plurality of learner terminals 100 to be connected to a communication network 300 such as the Internet and LTE network, and is linked with a video providing platform 400 to test the learner's level, and includes a learning server 200 that provides a customized learning problem solving video for each level and expert recommendation.

In addition, the learning server 200 receives problem information to the learner terminal 100 through the communication network 300 , provides a problem solving video in response to the problem, and smoothly provides learning contents such as performing a learner level test It is possible to provide a learning application to the learner terminal 100 for this purpose, and to provide a related learning content service by interworking with the learning application.

The communication network 300 may have a built-in communication protocol compatible with the learning server 200 and the learner terminal 100 or the learning server 200 and the video providing platform 400 for communication linkage, and if necessary, a router, a gateway, etc. may be included.

As shown in FIG. 2 , the learning server 200 further includes a data analysis unit 210 , a level matching unit 220 , an item matching unit 230 , an answer providing unit 240 , and a problem recommendation unit 250 . At least one separate database (not shown) for storing and providing various data and information may be provided.

The data analysis unit 210 may receive the problem image captured through the learner terminal 100 or the problem information directly input to the learner terminal 100 to analyze the corresponding problem. Optical character recognition (OCR) technology that extracts text from the image may be applied to identify the problem content in the problem image.

The level matching unit 220 may evaluate the learning level of the learner and perform a level test so that a problem-solving video can be provided tailored to the learning level, and a separate test item data for the learner learning level evaluation is stored in the database provided, and the learner level, which is the result of each learner, can be stored and managed separately.

In addition, in order to evaluate the learner's level, for example, computerized adaptive testing (CAT), which can be represented by computer-aided assessment, is a computerized adaptive test, which presents different levels of difficulty according to the ability and level of the learner and conducts the evaluation. can present a new problem and measure the learner's ability according to the result.

In addition, the level matching unit 220 may determine the learner level by, for example, measuring the level value of the test item to which the item response theory is applied, and measuring the learner's reaction according to the level value of the test item.

The item matching unit 230 may identify problems included in the problem information based on the text extracted from the data analysis unit 210 and search the database for the same or similar problems. In addition, the searched problem is provided to the answer providing unit 240 so that a video of solving the problem for the problem can be provided to the learner.

The answer providing unit 240 searches the problem solving video for the problem provided by the question matching unit 230 in association with the video providing platform 400, and as a result of the search, the question solving video corresponding to the problem is provided by the video providing platform 400 ) and may be provided to the learner terminal 100 or provided to the learner terminal 100 by direct communication between the platform and the learner.

In addition, when there are a plurality of searched problem-solving videos, the video list may be provided in the form of a list for each recommendation priority. The recommendation priority may be determined by the learner level or priority by expert recommendation, the area around the learner, the number of views, the rating (likes), the number of subscribers, and the like.

The video providing platform 400 is a service platform (server) that provides video content on a communication network 300 such as the Internet, and may be, for example, YouTube, Naver TV, Kakao TV, etc. It can also be an OTT (Over The Top) service platform that can watch (consume) media contents such as broadcasts/programs through the Internet.

The video providing platform 400 may link with the learning server 200 to allow a DB search upon request from the learning server 200, and provide a corresponding problem solving video to the learner terminal 100 according to the related video search results. .

The problem recommendation unit 250 may search the DB for a recommended problem suitable for the learner based on the problem information provided from the learner terminal 100 and the learner level by the level test, and provide it.

The recommended problems may be problems of a similar level and type to the problems included in the problem information, or they may be problems classified according to the learner level and included in the corresponding level.

In addition, when the learning server 200 does not initially collect information on the problem information, the learner level by the level test, etc. as learner information, the problem solving video and the recommended problems provided as priority are the AI-based recommendation algorithm By using a cold start of , it is possible to search for a condition similar to the learner, and provide a video of solving a problem or a recommended problem provided to a past learner with the most similar condition to the learner terminal 100 .

In this case, the recommended condition is performed based on basic learner information, and the learner information collected for this purpose may include age, intelligence, learner's residence area, preferred subjects, incorrect answer questions, and the like. Here, the virtual similar condition may be, for example, a condition in which the number of matching or similar information included in the learner information is the largest. In the case of the learner's residential area, it may be a problem-solving video or a recommended problem provided in the area in which the learner resides, and for example, it may be content provided by a local academy.

In addition, the recommendation algorithm method applied to similar conditions may utilize an artificial intelligence algorithm using similarity analysis.

Furthermore, in addition to the similarity calculation method, the dimension of the interaction matrix is reduced, approximated by two or more small matrices with k latent components, and matrix factorization (matrix factorization) that can predict the grade of each learner item by multiplying the corresponding row and column ) can also be used.

In addition, it explores the state space of a set of possible frequent items by using an association rule or a mining rule in which frequently consumed items are connected to the edge of the graph, and removes branching in the infrequent search space, A set of frequently used items is used to create rules, and these rules can also generate recommendations (recommendations).

For example, learner information is initially collected in order to provide a recommended learning service from the learning server 200 to the learner terminal 100, and based on the collected learner information, the most appropriate learning service for the age group is recommended or learner It is also possible to generate a related rule in consideration of the age, intelligence, region of the learner's residence, preferred subject, etc.

Furthermore, the learning server 200 may provide customized mentoring information by analyzing the learner's learning level using artificial intelligence (AI). For this, the learning server 200 provides problem information provided from the learner terminal 100, Analyze the learner level, age, intelligence, previous problem learning history, etc., and analyze the HELP content requested by the learner to generate and provide the corresponding mentoring information through the problem recommendation unit 250, and according to the learner's request or setting It provides pre-stored customized problems that fit the learning level of the learner.

In addition, the learning server 200 conducts individual learning management, learning level analysis and log record, individual level trend analysis for each learning area, and relative comparative statistics with the population for each learning stage in the area in which the learner's current learner level and mentoring are required. And the level, etc. may be comprehensively determined using artificial intelligence (AI) to generate mentoring information, and may be provided to the learner terminal 100 .

As an embodiment of such artificial intelligence (AI), a machine learning model that separates features inherent in learners through relationships and interest data may be used.

Accordingly, the reliability data (S) and the learning material list (Y) stored in advance in the database are input, and the learning material cluster data (V) in consideration of the learner's characteristics (U) and the learner's learning characteristics are obtained by machine learning. will be extracted

Here, the reliability data (S) is the accumulated data of the correlation distributions between the learner disposition set, the keyword, and the curriculum extracted by matching the learning information of a similar type in the database based on the learning information provided from the learner terminal 100, and a service It is iterative learning data by experience consisting of data (sparse matrix) derived from data accumulated in , and other analysis models.

The algorithm of the learning model according to the embodiment may be calculated by Equation (1).

Here, Y _ij is learning interest data (2-mode), U _i V _j ^T : predicted learning interest data (2-mode), S _ii ' is actual learner trust impact data (1-mode), U _i U _i' ^T is the predicted learner trust impact data (1-mode).

As expressed in Equation 1, machine learning is performed by feeding back the error until the error between the learning interest data and the predicted learning interest data disappears, and the error between the actual learner reliability data and the predicted reliability data is The machine learns by feeding back the error until it disappears.

Furthermore, the learning server 200 may further include an evaluation providing unit that receives and stores the evaluation information provided by the input of the learner terminal 100 after playing the problem solving video, and provides evaluation information upon the learner's request, and the evaluation providing unit The function of may be included in the function of the answer providing unit 240 .

The evaluation information may include, for example, satisfaction, comments, viewer ratings, number of views, ratings, and the like.

Furthermore, the learning server 200 may include an advertisement providing unit that sets and provides advertisements in the form of being inserted or hyperlinked in the middle of the problem solving video, and the function of the advertisement providing unit may be included in the function of the answer providing unit 240 . have.

If the problem-solving video is a video provided by a local academy around the learner, the revenue can be provided from the local academy, contributing to the revitalization of the local academy, and by promoting the academy, the learner can find a suitable area according to his or her learning achievement. It has the effect of finding a school.

3 is a flowchart illustrating a process of a method for providing customized learning problem solving content according to an embodiment of the present invention.

First, the problem information (or directly input problem) in the form of an image can be transmitted to the learning server 200 after photographing the documented problem for which the learner terminal 100 wants to receive a solution (answer) with the camera of the terminal. (S10).

The learning server 200 extracts the problem text by reading the OCR from the transmitted problem information (S20).

The learning server 200 may search for or receive a search result from the platform by requesting a video for solving a problem for the corresponding problem in connection with the video providing platform 400 (S30).

The learning server 200 provides a search result list with a set priority in connection with the video providing platform 400 and there are a plurality of video clips for solving the problem for the searched problem. can be transmitted (S40).

The learner terminal 100 prepares evaluation information for the video after playing the video of solving the problem and provides it to the learning server 200 (S50).

The learning server 200 may store the evaluation information and then provide the evaluation information to another learner terminal 100, and when the learner terminal 100 is requested, the corresponding problem (or previously learned problem) and A similar recommendation problem can be provided from a pre-stored database.

Furthermore, the learning server 200 may perform a level test for measuring a learner's level in order to provide a customized video of solving a problem, and then provide a video of solving a problem for the corresponding problem. Through this, a video of solving a problem suitable for the learner's level may be provided to the learner terminal 100 .

It is preferable that the level test performed in the learning server 200 is performed initially when the learning history is not present by determining the learning history learned by the terminal when a problem solving video is requested by the learner terminal 100, and thereafter, periodically after the learner's learning Level tests may be updated to account for capabilities.

100 ; learner terminal
200 ; learning server
210; data analysis department
220 ; level matching department
230 ; Item matching department
240 ; answer provider
250 ; problem recommendation department
300 ; communications network
400 ; video platform

Claims (8)

At least one learner terminal is accessible, and the learner terminal is provided with problem information so that the learner's level can be tested in connection with the video providing platform, and a customized learning problem solving video can be provided, and at least one corresponding to the problem of problem-solving videos are provided, and recommended problems similar to those problems are searched and provided in the database
learning server,
a data analysis unit for receiving the problem image taken through the learner terminal or the problem information directly input to the learner terminal and extracting the text of the problem;
a level matching unit that evaluates the learning level of the learner and performs a level test so that the learning level can be provided with a problem-solving video tailored to the learning level;
an item matching unit that identifies problems included in the problem information based on the text extracted from the data analysis unit, and searches the database for problems that are the same as or similar to the corresponding problem;
an answer providing unit that searches for a video of solving a problem for the problem provided by the item matching unit in association with a video providing platform, and receives a video of solving a problem corresponding to the search result from the video providing platform from the video providing platform and provides the video to the learner terminal;
Further comprising a problem recommendation unit for providing a suitable recommendation problem to the learner based on the learner level by the level test and the problem information provided from the learner terminal,
The problem recommendation department,
When the learning server fails to collect problem information and information on the learner level by the level test at first, the problem solving video or the recommended problems provided with priority are cold start or similarity analysis. Using an artificial intelligence algorithm, matrix factorization, association rule, or mining rule, search for conditions similar to the learner and solve problems provided to past learners with the most similar conditions or Provide recommended problems to the learner terminal,
The conditions recommended by the learning server are provided based on learner information, and include at least one selected from age, intelligence, learner's residence area, preferred subjects, and incorrect answers, and the most similar condition is that the information included in the learner information is The number of matching or similar information is the largest condition,
The learning server analyzes the learner's learning level using artificial intelligence (AI) to provide customized mentoring information, but analyzes problem information, learner level, age, intelligence and previous problem learning history, etc., and HELP content requested by the learner Analyze and provide mentoring information through the problem recommendation unit, and provide a pre-stored customized problem suitable for the learner's learning level according to the learner's request or setting,
The mentoring information includes the current learner level and the area and level requiring mentoring through individual learning management, learning level analysis and log record, level trend analysis by individual learning area, and relative comparative statistics with the population for each learning stage. Customized learning problem solving content providing system, characterized in that it is determined through AI). delete According to claim 1,
The data analysis unit
A customized learning problem solving content providing system, characterized in that it uses optical character recognition (OCR) technology to extract text from the image to identify the problem content from the problem image. According to claim 1,
The answer provider
If there are multiple searched problem-solving videos, the video list can be provided in the form of a list by recommendation priority. Customized learning problem solving content providing system, characterized in that it is set to any one of the number. delete At least one learner terminal is accessible, and the learner terminal is provided with problem information so that the learner's level can be tested in connection with the video providing platform, and a customized learning problem solving video can be provided, and at least one corresponding to the problem In the problem solving content providing method using a learning server that provides a problem solving video of
Transmitting the problem information in the form of an image to the learning server after photographing the documented problem with the learner terminal by the learner terminal;
The learning server extracts the problem text from the transmitted problem information;
The learning server requests a video video providing platform for solving a problem for the corresponding problem, and receiving a video search result from the video providing platform for solving a video problem;
The learning server provides a search result list with a set priority when there are a plurality of video clips for solving the problem found in connection with the video providing platform, and transmitting the video for solving the problem when requested from the search result list. ;
The learner terminal prepares evaluation information for the video after playing the video of solving the problem and provides it to the learning server;
Comprising the step of providing a recommendation problem similar to the corresponding problem from a previously stored database when the learner terminal is requested,
performing, by the learning server, a level test to measure the learner's level in order to provide a customized video of solving a problem;
After performing the level test, providing a problem-solving video for the problem included in the problem information; further comprising,
The step of providing from the pre-stored database,
When the learning server fails to collect problem information and information on the learner level by the level test at first, the problem solving video or the recommended problems provided with priority are cold start or similarity analysis. Using artificial intelligence algorithms, matrix factorization, association rules, or mining rules to search for conditions similar to learners and provide problem-solving videos or recommendations to past learners with the most similar conditions Provide the problems to the learner terminal,
The conditions recommended by the learning server are provided based on learner information, and include at least one selected from age, intelligence, learner's residence area, preferred subjects, and incorrect answers, and the most similar condition is that the information included in the learner information is The number of matching or similar information is the largest condition,
The learning server analyzes the learner's learning level using artificial intelligence (AI) to provide customized mentoring information, but analyzes problem information, learner level, age, intelligence and previous problem learning history, etc., and HELP content requested by the learner Analyze and provide mentoring information through the problem recommendation unit, and according to the learner's request or setting, provide a pre-stored customized problem suitable for the learner's learning level,
The mentoring information includes the current learner level and the area and level requiring mentoring through individual learning management, learning level analysis and log record, level trend analysis by individual learning area, and relative comparative statistics with the population for each learning stage. A method of providing customized learning problem-solving content, characterized in that it is generated by judging through AI. delete A computer program stored in a computer-readable storage medium for performing the method for providing customized learning problem solving content of claim 6.

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