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KR-20260062374-A - System for Analyzing and Improving English Learning Performance by Type

KR20260062374AKR 20260062374 AKR20260062374 AKR 20260062374AKR-20260062374-A

Abstract

The type-specific English learning performance analysis and improvement system according to the present invention is characterized by comprising: an English problem database in which English problems are classified and stored by type, such as topic, fill-in-the-blank, order/matching, vocabulary/grammar, reference, and agreement; a problem-solving module that receives an answer from a learner for an English problem containing a plurality of the above types; an analysis module that analyzes the learner's answer to identify an error pattern for each type; and a customized education module that provides customized learning materials to the learner based on the error pattern. According to the type-specific English learning performance analysis and improvement system of the present invention, by analyzing type-specific error patterns to specifically identify the difficulties a learner experiences in a particular type and providing customized learning materials based on this, it has the advantage of effectively supplementing the learner's weaknesses.

Inventors

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Assignees

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Dates

Publication Date
20260507
Application Date
20241029

Claims (7)

  1. As a system for analyzing and improving English learning performance by type, An English question database that stores English questions categorized by type: topic, fill-in-the-blank, order/matching, vocabulary/grammar, reference, and agreement; A problem-solving module that receives the learner's answers to English problems containing multiple of the above-mentioned types; An analysis module that analyzes the answers of the above-mentioned learner to identify incorrect answer patterns for each type; and, An English learning performance analysis and improvement system characterized by including a customized education module that provides customized learning materials to the learner based on the above incorrect answer patterns.
  2. In Article 1, The above English problem is, The above six types include detailed attributes such as analytical ability, logical ability, and reading comprehension, and The above analysis module is, By analyzing the answers of the above learners, we identify error patterns by detailed attributes related to analytical, logical, and reading comprehension skills for the above types, and An English learning performance analysis and improvement system characterized by visualizing the accuracy rates for analytical ability, logical ability, and reading comprehension in the form of a graph based on the analysis results of incorrect answer patterns for each detailed attribute mentioned above.
  3. In Article 1, The above analysis module is, A regression analysis algorithm that analyzes the correlation between the error rate and the correct answer rate for each of the above types, and An English learning performance analysis and improvement system characterized by including a random forest algorithm that predicts the effect of a change in the error rate occurring in a specific type on a change in the correct answer rate in another type by analyzing the interaction between the types based on multiple decision trees to derive the effect of the change in the error rate on the correct answer rate.
  4. In Paragraph 3, The above regression analysis algorithm is, A correlation coefficient calculation unit that calculates the correlation coefficient between the error rate and the correct answer rate for each problem type of the learner and numerically derives the correlation in the above English problem, and An English learning performance analysis and improvement system characterized by including a weak type analysis unit that identifies the learner's weak problem types by analyzing the relationship between the error rate and the correct answer rate by problem type based on the above correlation.
  5. In Paragraph 3, The above random forest algorithm is, A type-specific decision tree generation unit that generates multiple independent decision trees for each type, learns from the learner's past learning data, and analyzes the tendency of incorrect answer selection observed in the corresponding type to calculate a predicted value regarding the potential for learning improvement of that type, and An English learning performance analysis and improvement system characterized by including a decision tree combination unit that integrates the above-mentioned predicted values to derive the impact of changes in the error rate of a specific type on other types.
  6. In Paragraph 5, The above decision tree combination unit is, An interaction analysis part that integrates the calculated predicted values to analyze the interaction between the types, and An English learning performance analysis and improvement system characterized by including an impact analysis part that derives the effect of a change in the error rate of a specific type on the correct answer rate of another type based on the above interaction.
  7. In Paragraph 6, The above interaction analysis part is, The interaction coefficient between the above types is calculated using the following mathematical formula 1, and Mathematical formula 1. (Here, is the interaction coefficient of the change in the error rate of type i on the change in the accuracy rate of type j, is the change in error rate (%) of type i, is the change in the accuracy rate of type j (%) The above impact analysis part is, An English learning performance analysis and improvement system characterized by predicting the impact of changes in the error rate of a specific type on the correct answer rate of another type through the following mathematical formula 2. Mathematical formula 2. (Here, is the change in the accuracy rate (%) of type j to be predicted, is the change in error rate (%) of type i, ε is the interaction coefficient of the change in the error rate of type i on the change in the correct answer rate of type j, where n is the total number of types)

Description

System for Analyzing and Improving English Learning Performance by Type The present invention relates to a system for analyzing and improving English learning performance by type, and more specifically, to a system capable of efficiently improving learning performance by systematically supplementing and improving a learner's weaknesses through the analysis of a learner's answers by type to identify error patterns in each question type and the provision of customized learning materials based on this analysis. In the current English learning evaluation system, learners are evaluated on their performance by solving various types of problems. However, existing systems not only lack precise criteria for classifying question types but often simply categorize learners' performance by type into correct and incorrect answers, failing to provide a concrete analysis of which question types learners struggle with and what patterns of errors occur. Consequently, learners have faced limitations in comprehensively understanding their learning weaknesses and effectively addressing them. The limitations of the existing system manifest as the following problems. First, although there are problems categorized by type, they lack the capability to precisely analyze learners' error patterns; consequently, learners often fail to recognize recurring learning errors and repeat the same mistakes. Second, existing learning systems limit learning effectiveness because they do not provide specific diagnoses of the causes and solutions when learners repeatedly make incorrect answers in certain types of questions. Third, there is a lack of a system to supplement deficiencies by providing customized learning materials to learners based on the analysis of learning outcomes by type. For example, if a learner frequently makes mistakes on fill-in-the-blank questions, it is possible that the learner is struggling with vocabulary selection or context comprehension, but existing assessment systems fail to specifically analyze these tendencies or provide customized materials. As a result, learners found it difficult to systematically compensate for their weaknesses and faced limitations in improving overall learning outcomes. Korean Patent No. 10-2024-0129517 discloses a system for providing an English education platform that introduces the concept of types, but it has a problem in that it fails to provide the correct means to respond to various types of actual English problems by providing English education through personality types based on MBTI, which are completely unrelated to the attributes of English problems. Therefore, there is a need for a system that can systematically address learners' weaknesses by analyzing their error patterns for each question type and providing customized learning materials tailored to the types of problems they struggle with. FIG. 1 is a block diagram illustrating the schematic configuration of the system of the present invention. FIG. 2 is a conceptual diagram illustrating an example of an English problem of the present invention. FIG. 3 is a conceptual diagram illustrating the analysis results of the analysis module of the present invention and the concept of customized learning materials of the customized education module. Figure 4 is a conceptual diagram showing the accuracy rates for analytical ability, logical ability, and reading comprehension in the first graph. Figure 5 is a conceptual diagram showing the accuracy rates for analytical ability, logical ability, and reading comprehension in the second graph. FIG. 6 is a block diagram illustrating the detailed configuration of the analysis module. Preferred embodiments of the present invention will be described in detail below with reference to the attached drawings. The attached drawings are not drawn to scale, and the same reference numerals in each drawing refer to the same components. First of all, the system of the present invention can be implemented through a main server. In other words, the entity that builds an English problem database (110) in the system of the present invention, receives answers through the learner's problem-solving process, analyzes the answers, and provides customized learning materials is the main server. Therefore, unless otherwise noted, it is understood that the subject of the system of the present invention is the main server. This main server is a series of entities for implementing the system of the present invention and includes a server PC and a network communication network, etc. In addition, the main server is equipped with a hardware-based central processing unit (CPU) and storage means such as memory and hard disks, on which programs, or software, capable of being executed on the CPU are installed and executed; a series of specific configurations for such software will be described later as constituent units such as 'modules,' 'parts,' and 'parts.' Such configurations, such as 'module,' 'part,' 'section,' etc., re