Learning assistance system using rearrangement problem of which information origin is customized

Abstract

With the state of the art, it is not possible to utilize rearrangement problems having information origins based on the essence of learning processes, namely that learning is to understand concepts and to understand connections between the concepts. The present invention makes such utilization possible for both educational learning and language learning.　This learning assistance system uses a rearrangement problem of which an information origin is customized and is characterized by a step of analyzing and acquiring a book, audio, video content, and other relevant information selected by a learner by using a natural language processing technique or a machine learning algorithm in accordance with a profile or a preference and needs of the learner, a step of creating, on the basis of the acquired information, the customized rearrangement problem in a language desired by the learner, a step of converting the created problem into a signal, digital data, or other formats and transmitting the conversion result to a terminal used by the learner by using an optical fiber cable, a wireless communication, the Internet, or other communication means, a step in which the learner answers by using the rearrangement problem, a step of determining whether the answer is correct or wrong, a step of conjecturing the cause of an incorrect answer to the rearrangement problem and creating appropriate advice by using a natural language processing technique or a machine learning algorithm in consideration of one or both of the level of the contents or a language level of the learner, and the function which allows the learner to select the steps and a desired language at a point designated by the learner within each step, the function being capable of processing the selections by using a natural language processing technique, a machine learning algorithm, or other suitable technical means.

Description

A learning support system using customized information-based rearrangement problems

[0001] The present invention relates to a system that uses natural language processing technology and machine learning algorithms to provide personalized learning support based on a learner's profile, preferences, and selected learning content (books, audio, video, etc.). The system creates and provides customized reordering questions to the learner based on the learner's content level, linguistic level, and interests. The system analyzes the learner's comprehension and responses through the reordering questions to identify the cause of incorrect answers. The system then provides appropriate advice taking into account the learner's content level, linguistic level, or both to support the learning process. The present invention is novel in that it creates reordering questions based on the learner's profile and preferences to support understanding of the subject matter and the learning of vocabulary, grammar, and syntax, and in language learning, provides an effective learning tool for deepening language comprehension.

[0002] In recent years, the information revolution brought about by the Internet has led to the advancement of information technology in education, providing a variety of educational support services. For example, not only services providing video content and online tutors, but also interactive learning platforms and personalized learning using AI are becoming increasingly popular. Learning involves understanding concepts and the relationships and connections between concepts. In other words, learning requires understanding the vocabulary that represents concepts and the relationships and connections between vocabulary elements. While learning support systems using rearrangement problems exist in the field of language learning, they do not provide personalized support based on the learner's individual profile, preferences, and selected learning content. The present invention includes a step of using natural language processing technology and machine learning algorithms to acquire and analyze the learner's profile, preferences, and related information such as the books, audio, and video content selected by the learner. Based on the analysis results and the acquired information, the system also includes a step of creating customized rearrangement problems in the learner's preferred language, a step of sending the created problems to the learner's device, a step of the learner answering the rearrangement problems, and a step of determining whether the answers are correct. This information-driven learning support system using customized rearrangement problems can provide a more effective and personalized learning experience than conventional systems. Furthermore, the system includes a step of inferring the cause of incorrect answers to rearrangement questions and using natural language processing techniques or machine learning algorithms to create appropriate advice taking into account the learner's content level, language level, or both, thereby enabling the learner to progress effectively in their studies.

[0003] JP 2018-17904 A JP 2022-175437 A

[0004] Conventional learning support systems have struggled to create personalized reordering questions based on learners' profiles and preferences, as well as the learning content they select (books, audio, video, etc.). This is due to a lack of technology for creating information-driven reordering questions that address the different needs and levels of each learner. As a result, it has been difficult to provide an optimized learning experience for each learner, and there has been a lack of effective learning support. There has also been a lack of technology for having learners answer questions, analyzing their errors, understanding their learning status from the results, and providing individually customized advice. This has resulted in the challenge of not being able to fully realize effective learning support that addresses the different needs and levels of each learner.

[0005] The main invention of the present invention for solving the above problems is a learning support system, characterized by the steps of: analyzing and acquiring books, audio, video content, and other related information selected by a learner based on the learner's profile, wishes, and needs using natural language processing technology and machine learning algorithms; creating customized rearrangement problems in the learner's preferred language based on the acquired information; converting the created problems into signals, digital data, or other formats and transmitting them to a terminal used by the learner via optical fiber cables, wireless communication, the Internet, or other communication means; the learner solving the problems using the rearrangement problems; determining whether the answers are correct; inferring the cause of incorrect answers to the rearrangement problems and creating appropriate advice using natural language processing technology and machine learning algorithms, taking into account the learner's content level, language level, or both; and allowing the learner to select a desired language for each step and at a point specified by the learner within each step, and processing the selection using natural language processing technology, machine learning algorithms, or other appropriate technical means. Other problems and solutions disclosed in this application will be made clear in the section on embodiments of the invention and the drawings.

[0006] According to the present invention, regardless of the learner's profile and preferences, or the language in which the books, audio, video content, and other related information selected by the learner are provided, the semantic content can be analyzed to create rearrangement questions that are individually customized to the learner's needs. Furthermore, various advice can be given based on the learner's answers. This allows learners to have an optimal learning experience according to their level and interests, and to progress effectively in their studies.

[0007] FIG. 1 is a diagram illustrating an example configuration of a learning support system according to an embodiment of the present disclosure. FIG. 2 is a diagram illustrating an alternative example configuration of a learning support system according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating the overall flow of learning in this embodiment. FIG. 4 is a diagram illustrating an example of creating a Japanese rearrangement problem based on a learner's profile, preferences, and content (Japanese books). FIG. 5 is a diagram illustrating an example of determining the correctness of a learner's answer to the created rearrangement problem and creating Japanese advice related to the content. FIG. 6 is a diagram illustrating an example of creating an English rearrangement problem based on a learner's profile, preferences, and content (Japanese books). FIG. 7 is a diagram illustrating an example of determining the correctness of a learner's answer to the created rearrangement problem and creating advice in the language (Japanese) in which the learner wants to receive advice regarding the language (English) that the learner is studying.

[0008] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that in this specification and drawings, components having substantially the same functional configuration are designated by the same reference numerals, and redundant description will be omitted. A learning support system according to an embodiment of the present disclosure aims to support learning using a learner's profile and preferences, books, audio, and video content selected by the learner, and other related information. Learning involves understanding concepts and the relationships and connections between concepts. In other words, learning requires understanding vocabulary that expresses concepts and the relationships and connections between vocabulary. The learning support system of this embodiment acquires and analyzes the learner's profile and preferences, books, audio, and video content selected by the learner, and other related information, and creates customized rearrangement problems in the learner's preferred language based on the analysis results and the acquired information. The learner answers the created rearrangement problems, and the correctness of the answer is determined. This provides a more effective and personalized learning experience than conventional systems. In other words, learning enables the learner to understand concepts and the relationships and connections between concepts. To answer rearrangement questions, learners must understand the meaning of the vocabulary, i.e., the concept, and also understand the use of syntactic and grammatical knowledge of the language used. In liberal arts studies, rearrangement tasks test semantic comprehension, while language learning tests not only semantic comprehension but also the ability to use the vocabulary, syntactic, and grammatical knowledge of the language, making rearrangement questions effective for learning. Learners' profiles and preferences, the books, audio, and video content they select, and other relevant information are entered. For example, this information includes the learner's background and learning goals, the textbooks, reference books, academic books, novels, and other books they use, audio content such as podcasts and radio, educational video content and movies, the language of the content, and the language they wish to use for learning.A learner practices rearrangement problems and answers them. The correctness of the answers is determined, and the correctness of the answers is analyzed using natural language processing technology or machine learning algorithms. Appropriate advice is then provided taking into account the learner's content level, language level, or both. FIG. 1 is a diagram illustrating an example configuration of a learning support system according to an embodiment of the present disclosure. As illustrated, the learning support system according to this embodiment includes a server 10. The server 10 is communicatively connected to a learner terminal 20 (hereinafter referred to as terminal 20) and a supporter terminal 30 (hereinafter referred to as terminal 30) via a network NW. Note that this configuration is merely an example, and one configuration may incorporate or include other configurations. In this embodiment, the network NW is assumed to be the Internet. The network NW may be constructed, for example, using a public telephone network, a mobile phone network, a wireless communication network, Ethernet (registered trademark), or the like. The server 10 is, for example, a computer used to provide learning support. For example, the server 10 may be a general-purpose computer such as a workstation or a personal computer, or may be logically implemented using cloud computing. The terminal 20 is a computer used by the learner G to input his / her profile and preferences by text or voice, and to input answers to rearrangement problems when studying. For example, the terminal 20 is a smartphone, tablet computer, personal computer, etc. The terminal 20 may input the content to be studied by the learner G. The supporter TA creates rearrangement problems based on the content to be studied and provides them to the learner G. The supporter TA also determines whether the answers given by the learner G are correct, analyzes the learner G's answers, and creates advice using natural language processing technology, machine learning algorithms, or other appropriate technology. In language learning, the supporter TA creates advice on vocabulary, grammar, syntax, etc. using natural language processing technology, machine learning algorithms, or other appropriate technology and delivers it to the learner G. The terminal 30 is a computer operated by the supporter TA who supports the learner G's learning, and may be, for example, a smartphone, tablet computer, personal computer, etc.FIG. 2 shows a case where the supporter TA is replaced by a program on a server and automated. While FIG. 2 shows the use of multiple different servers, a single server may also be used. FIG. 3 is a diagram illustrating the learning flow in this embodiment. Learner G uses terminal 20 to perform a series of learning tasks. During this process, learner G inputs the content to be studied, as well as learner G's own profile, preferences, and other related information at the request of the supporter TA. Learner G may enter the content himself or provide the supporter TA with the title of the content, which the supporter TA then obtains and inputs. The input content, learner G's profile, preferences, and other related information are sent to the supporter TA and analyzed on server 10. Based on the analysis results, the supporter TA creates rearrangement problems on server 10, sends them to learner G, and learns on terminal 20. Learner G solves the rearrangement problems by answering them. Learner G enters the answers to the rearrangement problems on terminal 20, and the answers are then sent to the supporter TA. The supporter TA determines whether the answer is correct on the server 10, analyzes the correctness using natural language processing technology or machine learning algorithms, and prepares appropriate advice taking into consideration the content level, language level, or both of the learner G, and sends it to the learner G. In language learning, the supporter TA sends not only advice on the semantic content but also advice on the vocabulary of the language, syntactic knowledge, ability to use grammatical knowledge, etc.

[0009] FIG. 4 is a diagram showing an example of creating a Japanese rearrangement problem based on a learner's profile, preferences, and content (Japanese book) according to this embodiment. This diagram shows an example of creating a Japanese rearrangement problem based on the contents of a Japanese book, and the learner can select their desired language at each step and at a location specified by the learner within each step. FIG. 5 is a diagram showing an example of determining the accuracy of a learner's answer to a created rearrangement problem based on this embodiment and creating Japanese advice regarding the content. As in FIG. 4, the advice is written in Japanese, but the learner can select their desired language at each step and at a location specified by the learner within each step. FIG. 6 is a diagram showing an example of creating an English rearrangement problem based on a learner's profile, preferences, and content (Japanese book) according to this embodiment. This diagram shows an example of creating an English rearrangement problem based on the contents of a Japanese book, and the learner can select their desired language at each step and at a location specified by the learner within each step. FIG. 7 is a diagram showing an example of determining the accuracy of a learner's answer to a created rearrangement problem based on this embodiment and creating advice in Japanese regarding English. In language learning, advice is also provided on vocabulary, grammar, syntax, etc. The advice is provided in Japanese, but the learner can select their preferred language at each step and at designated points within each step. Disclosure Note that the present disclosure includes the following configuration: [Item 1] A learning support system using information-driven customized rearrangement problems, comprising: a step of analyzing and acquiring books, audio, video content, and other related information selected by the learner based on the learner's profile, preferences, and needs using natural language processing technology and machine learning algorithms; a step of creating customized rearrangement problems in the learner's preferred language based on the acquired information; a step of converting the created problems into signals, digital data, or other formats, and transmitting them to a terminal used by the learner via optical fiber cable, wireless communication, the Internet, or other communication means; a step of the learner answering the rearrangement problems; and a step of determining whether the answers are correct.[Item 2] The learning support system of claim 1, further comprising a step of inferring the cause of an incorrect answer to a rearrangement problem and using natural language processing technology or a machine learning algorithm to create appropriate advice taking into consideration the learner's content level, linguistic level, or both. [Item 3] The learning support system of claim 1 or claim 2, further comprising a function that enables a learner to select a desired language for each step and at a location specified by the learner within each step, and to process the selection using natural language processing technology, a machine learning algorithm, or other appropriate technical means.

[0010] The learning support system of the present invention can be widely used in online education platforms, corporate in-house education programs, and the like.

[0011] The symbols and descriptions of each element shown in Figure 1 are listed below. 10 Server 20 Learner terminal (Learner G's terminal) 30 Supporter terminal (Supporter TA's terminal) The symbols and descriptions of each element shown in Figure 4 are listed below. 401 An example of input content for the learner to study (Japanese book) 402 An example of the learner's profile and wishes (input in Japanese) 403 An example of a created Japanese rearrangement problem The symbols and descriptions of each element shown in Figure 5 are listed below. 404 An example of a created Japanese rearrangement problem 405 An example of determining the correctness of the learner's answer to the created rearrangement problem 406 An example of creating advice in Japanese regarding the content The symbols and descriptions of each element shown in Figure 6 are listed below. 501 An example of input content for the learner to study (Japanese book) 502 An example of the learner's profile and wishes (input in Japanese) 503 An example of a created English rearrangement problem The symbols and descriptions of each element shown in Figure 7 are listed below. 504 An example of a created English rearrangement problem 505 An example of determining whether a learner's answer to the created rearrangement problem is correct or incorrect 506 An example of creating advice in Japanese about English

Claims

1. A learning support system using information-driven customized rearrangement problems, comprising the steps of: analyzing and obtaining books, audio, video content, and other related information selected by a learner based on the learner's profile, wishes, and needs using natural language processing technology and machine learning algorithms; creating customized rearrangement problems in the learner's preferred language based on the obtained information; converting the created problems into signals, digital data, or other formats, and transmitting them to a terminal used by the learner using optical fiber cables, wireless communication, the Internet, or other communication means; having the learner answer using the rearrangement problems; and determining whether the answers are correct or incorrect.

2. A learning support system using customized information-driven rearrangement problems according to claim 1, further comprising the step of inferring the cause of an incorrect answer to the rearrangement problem and using natural language processing technology or a machine learning algorithm to create appropriate advice taking into account the learner's content level, language level, or both.

3. A learning support system for customized information-driven sorting problems as claimed in claim 1 or claim 2, which has the function of allowing a learner to select a desired language for each step and at a point within each step designated by the learner, and to process the selection using natural language processing technology, machine learning algorithms, or other appropriate technical means.

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