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교과기반 학습성취 평가 및 적응형 피드백 시스템 설계

Study on Course-Embedded Learning Achievement Evaluation and Adaptive Feedback

  • 정현숙 (조선대학교 컴퓨터공학과) ;
  • 김정민 (대진대학교 컴퓨터공학과)
  • 투고 : 2022.10.31
  • 심사 : 2022.11.09
  • 발행 : 2022.11.30

초록

고등교육기관의 역량 중심 교육과정 운영을 위해서는 교과목 수준에서 교과 학습목표(성과기준)의 성취수준을 다각도로 평가하여 학습자의 역량 함양 정도를 파악하는 교과기반 학습평가 방법에 대한 연구가 지속적으로 필요하다. 본 연구에서는 교과목 학습성과, 학습주제, 학습개념 기반의 학습평가 모델 및 성취수준에 따른 개인화된 학습 피드백 모델을 제안한다. 먼저 데이터 모델링 과정에서 교과목의 계층화된 학습성과, 학습주제 및 학습개념 그래프 및 학습성과-평가 매트릭스 모델을 정의하고 이를 기반으로 학습성과별, 학습주제별, 학습자별 등 다각도의 학습성취 수준을 측정하고 피드백하는 알고리즘을 제안한다. 제안한 학습성취평가 모델의 유효성을 검증하기 위해 자바프로그래밍 교과목에 적용하여 실제 데이터를 기반으로 실험을 진행하였으며 그 결과 성취수준의 산출 및 학습 피드백이 가능함을 보였다.

The research of course-embedded learning evaluation method, which can be used to measure the competency of learners by evaluation of learning outcomes, has been performed for competency-based education in the university. In this paper, we propose an learning evaluation and adaptive feedback model based on learning outcomes, learning subjects, learning concepts graph, and an evaluation matrix. Firstly, we define the layered learning outcomes, a graph of learning subjects and concepts, and two association matric. Secondly, we define algorithms to calculate the level of learning achievement and the learning feedback to learners. We applied the proposed method to a specific course, "Java Programing", to validate the effectiveness of our method. The experimental results show that our proposed method can be useful to measure the learning achievement of learners and provide adaptive feedbacks to them.

키워드

과제정보

이 논문은 2021학년도 조선대학교 학술연구비의 지원을 받아 연구되었음.

참고문헌

  1. J. B. Yoo, J. S. Won, & S. E. Chung, "The Influences of Accomplishment of Outcome-based Education in Nursing Students", JCCT, Vol. 5, No. 2, pp. 329-336, 2019.
  2. S. N. Son & I. Y. Kim, Kim, H. S. Song, J. S. Lee, & Y. J. Choi, "Competency-Based Education and Core Competencies in Higher Education", Korean Journal of General Education, Vol. 15, No. 1, pp. 11-30, 2021.
  3. A. Lakas & A. N. Belkacem, "A Framework for Course-embedded Assessment for Evaluating Learning Outcomes of a Network Programming Course". 2021 IEEE Global Engineering Education Conference (EDUCON), pp. 989-995, Austria : Vienna, 2021.
  4. H. K. Kim, "Development on the model of outcome-based course evaluation design for Course-Embedded Assessment", Journal of Engineering Education Research, Vol. 18, No. 6, pp. 24-31, 2015. https://doi.org/10.18108/jeer.2015.18.6.24
  5. H. S. Chung & J. M. Kim, "Data modeling and algorithms design for implementing Competency -based Learning Outcomes Assessment System", JCIT, Vol. 11, No. 11, pp. 335-344, 2021.
  6. A. Dewani, S. Bhatti & M. A. Memon, "Analysis of Outcome-based educational model in Engineering Education with preliminary Findings", IJACT, Vol. 10, No. 1, pp. 1~9, 2022.
  7. Y. I. Han, "A Study of the Adaptability of CEA for Learning Outcomes Assessment of Nursing Management Coourses", Journal of LearnerCentered Curriculum and Instruction, Vol. 16, No. 5, pp. 301-327, 2016.
  8. S. M. Nam, "Development of a ProgramOutcomes Assessment System based on Course Embedded Assessment for Nursing Education", Journal Korean Academic Society Nurse Education, Vol. 23, No. 2, pp. 135-145, 2017. https://doi.org/10.5977/jkasne.2017.23.2.135
  9. S. J. Lee & C. Y. Jo, "Development and Imple mentation the Program Outcomes Assessment System based on Web-based Course Embedded Assessment(CEA)", The Journal of the Convert gence on Culture Technology, Vol. 5, No. 1, pp. 67-75, 2019.
  10. X. Diao, Q. Zeng, L. Li, H. Duan, H. Zhao & Z. Song, "Personalized Learning Path Recommen dation Based on Weak Concept Learning", Mobile Information Systems, pp.1-17. 2022.
  11. C. Bian, D. Wang, S. Liu, W. Lu & J. Dong, "Adaptive learning path recommendation based on graph theory and an improved immune algorithm", KSII Transactions on Internet & Information Systems, Vol. 13, No. 5, pp. 2277-2297, 2019. https://doi.org/10.3837/tiis.2019.05.003
  12. C. L. Tang, J. Liao, H. C. Wang, C. Y. Sung & W. C. Lin, "Concept guide: supporting online video learning with concept map-based recommendation of learning path", In Proceedings of the Web Conference 2021, pp. 2757-2768, 2021.