한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제13권3호
  • /
  • Pages.55-65
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  • 2022
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  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
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맞춤형 영어 교육을 지원하기 위한 콘텐츠 기반 분석 기법

Analysis technique to support personalized English education based on contents

  • 정우성 (서울교육대학교 교육전문대학원) ;
  • 이은주 (경북대학교 컴퓨터학부)
  • Jung, Woosung (Graduate School of Education, Seoul National University of Education) ;
  • Lee, Eunjoo (School of Computer Science and Engineering, Kyungpook National University)
  • 투고 : 2022.01.20
  • 심사 : 2022.03.20
  • 발행 : 2022.03.28
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초록

인터넷 기술과 모바일 등의 기기 발전으로 교육환경도 전통적이고 수동적인 방식에서 학습자 중심의 능동적인 방식으로 변화하고 있다. 이에 따라 학습자 개개인의 수준별 맞춤 교육의 역할도 커지고 있으며, 이에는 개별 학습자의 프로파일 구축이 중요하다. 기존의 ICT 기반 맞춤형 영어 교육의 다수는 어휘에 초점을 맞추고 있으며, 학습 콘텐츠에 대한 분석에 많은 노력을 기울이고 있다. 본 논문에서는 보다 정밀하게 사용자의 학습상태를 정의하기 위하여 단어와 문법을 대상으로 학습 상태를 구축하였다. 그리고 학습자가 특정 콘텐츠에 얼마나 익숙한지를 알려주는 콘텐츠에 대한 숙련도 메트릭을 정의하였다. 이후 실제 영문 에세이 데이터를 기반으로 사전학습을 통하여 사용자들의 숙련도를 결정하고, 시뮬레이션을 통하여 평가 에세이 데이터에 대하여 적용성이 있음을 보였다. 또한 본 연구에서 제안한 분석기법은 학습상황에 대하여 통계치나 그래프를 제공하고 학습자 수준에 적합한 학습자료를 생성하는데 필요한 데이터를 제공할 수 있다.

As Internet and mobile technology is developing, the educational environment is changing from the traditional passive way into an active one driven by learners. It is important to construct the proper learner's profile for personalized education where learners are able to study according to their learning levels. The existing studies on ICT-based personalized education have mostly focused on vocabulary and learning contents. In this paper, learning profile is constructed with not only vocabulary but grammar to define a learner's learning status in more detailed way. A proficiency metric is defined which shows how a learner is accustomed to the learning contents. The simulational results present the suggested approach is effective to the evaluation essay data with each learner's proficiency that is determined after pre-learning process. Additionally, the proposed analysis technique enables to provide statistics or graphs of the learner's status and necessary data for the learner's learning contents.

키워드

과제정보

This work was supported by the 2021 Research Fund of Seoul National University of Education.

참고문헌

  1. K. Noh, S. Ju & J. Jung. (2011). An Exploratory Study on Concept and Realization Conditions of Smart Learning. Journal of Digital Convergence, 9(2), 79-88. DOI : 10.7236/JIIBC.2016.16.1.21
  2. W. Jung. (2020). A Design for the Personalized Difficulty Level Metric based on Learning State. Journal of the Korea Convergence Society, 11(3), 67-75. DOI: 10.15207/JKCS.2020.11.3.067
  3. S. M. nafea, F. Siewe & Y. He. (2018). ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach. International Journal of Information and Education Technology, 8(12). DOI : 10.18178/ijiet.2018.8.12.1151
  4. W. Jung & E. Lee (2021). Analysis Techniques to Support Personalized Music Education Based on Learner and Chord Data. Journal of the Korea Convergence Society, 12(2), 51-60. DOI : 10.15207/JKCS.2021.12.2.051
  5. H. Kim. (2017). A Comparative Study of English Vocabulary Learning between Mobile Application and Vocabulary List. STEM Journal, 18(1), 183-206. DOI : 10.16875/stem.2017.18.1.183
  6. H. Kim. (2019). Cutomized Mobile Application Based on Learners' Different Levels in the EFL Classroom: Efficiency and Value of Apps. LAK Journal. 27(1). 42-62. DOI : 10.24303/lakdoi.2019.27.1.42
  7. M. Okhdar & A. Ghaffari (2018). English Vocabulary Learning through Recommender System based on Sentence Complexity and Vocabulary Difficulty. Kyberbetes, 47(1), 44-57. DOI : 10.1108/K-06-2017-0198
  8. C. Chen & Y. Li (2010). Personalised context-aware ubiquitous learning system for supporting effective English vocabulary learning. Interactive Learning Environments, 18(4), 341-364. DOI : 10.1080/10494820802602329
  9. C. Chen, L. Chen & S. Yang (2018). An English Vocabulary Learning App with Self-regulated Learning Mechanism to Improve Learning Performance and Motivation. Computer Assisted Language Learning, 32(3), 1-24. DOI : 10.1080/09588221.2018.1485708
  10. L. Fang, L. Tuan, S. Hui & L Wu. (2017). Personalized Question Recommendation for English Grammar. Expert Systems, 35(2). DOI : 10.1111/exsy.12244
  11. X. Wang, J. Chen & T. Zhang. (2021). Facilitating English Grammar Learning by a Personalized Mobile-Assisted System With a Self-Regulated Learning Mechanism. Frontiers in Psychology, 12(10). DOI : 10.3389/fpsyg.2021.624430
  12. K. Kim & H. Shin. (2016). Student-oriented Multi-dimensional Analysis System using Educational Profiling. Journal of Digital Convergence, 14(6). 263-270. DOI : 10.14400/JDC.2016.14.6.263
  13. R. Felder & L. Silverman. (1988). Learning and Teaching Styles in Engineering Education. Engineering Education, 78(7), 674-681. DOI : 10.1109/FIE.2008.4720326
  14. D. Silva & F. Dora. (2014). An Automatic Approach for Customization of Teaching Process based on Learning Styles in Adaptive and Intelligent Learning Systems. Brazilian Journal of Computers in Education, 22(2), 1. DOI : 10.5753/RBIE.2014.22.02.1
  15. The Basic Spelling Vocabulary List. (2022, Jan 1). https://www.readingrockets.org/article/basic-spelling-vocabulary-list
  16. Penn Part of Speech Tags. (2022, Jan 1). https://cs.nyu.edu/~grishman/jet/guide/PennPOS.html
  17. Ministry of Education. (2015). 2015 Revised National English Curriculum : Ministry of Education.