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베이지언 네트워크를 이용한 영어 관사 학습의 인지진단 평가

An Application of Bayesian Networks to Cognitive Diagnostic Modeling of English Article Learning

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한국언어과학회 (The Korean Association Of Language Sciences)|2023.02|언어과학 (Journal of Language Sciences)|제30권 제1호|pp. 147-173|DOI http://dx.doi.org/10.14384/kals.2023.30.1.147

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키워드

graph theoryEnglish articlescognitive diagnostic modeling위계적 지식 구조그래프 모형인진진단모형베이지언 네트워크정보 업데이트영어 관사 사용hierarchical knowledge structuresbayesian networksbelief update
주제분류언어학

초록

The English article system has been known one of the most challenging components of English grammar for foreign learners to master, which needs continuous learning and feedback through assessments. However, current cognitive diagnostic models(CDMs) show some serious limitations: heavy reliance on large scale data, inability to model skill hierarchies and insufficient flexibility to efficiently handle small but repeated measurements. The purpose of the current study was to examine the potential of Bayesian network-based cognitive diagnostic modeling(BN-CDM) as an alternative to the current CDMs. A group of 124 college students(98 females and 26 males) joined a weekly 10-min learning session of the English article system throughout a semester and were administered a series of diagnostic tests. The test data were analyzed using conventional CDMs and BN-CDM. The results show that BN-CDM can handle small but repeated test data much more efficiently than conventional CDMs with a full consideration of hierarchical structures of the subject domain. The study also discusses some pedagogical implications of the results.

Abstract

The English article system has been known one of the most challenging components of English grammar for foreign learners to master, which needs continuous learning and feedback through assessments. However, current cognitive diagnostic models(CDMs) show some serious limitations: heavy reliance on large scale data, inability to model skill hierarchies and insufficient flexibility to efficiently handle small but repeated measurements. The purpose of the current study was to examine the potential of Bayesian network-based cognitive diagnostic modeling(BN-CDM) as an alternative to the current CDMs. A group of 124 college students(98 females and 26 males) joined a weekly 10-min learning session of the English article system throughout a semester and were administered a series of diagnostic tests. The test data were analyzed using conventional CDMs and BN-CDM. The results show that BN-CDM can handle small but repeated test data much more efficiently than conventional CDMs with a full consideration of hierarchical structures of the subject domain. The study also discusses some pedagogical implications of the results.

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