컴퓨터교육학회논문지 (The Journal of Korean Association of Computer Education)

  • 제23권1호
  • /
  • Pages.77-90
  • /
  • 2020
  • /
  • 1598-5016(pISSN)
  • /
  • 2733-9785(eISSN)
한국컴퓨터교육학회 (The Korean Association of Computer Education)
권호 표지
DOI QR코드

DOI QR Code

온라인 문제기반학습에서의 학습행태 분석: 학습분석 기법을 적용하여

Investigating Learning Type in Online Problem-Based Learning: Applying Learning Analysis Techniques

  • 이성혜 (KAIST 과학영재교육연구원) ;
  • 최경애 (중부대학교) ;
  • 박민서 (KAIST 과학영재교육연구원) ;
  • 한정윤 (서울대학교 스마트 휴머니티 융합 사업단)
  • 투고 : 2019.11.04
  • 심사 : 2020.01.14
  • 발행 : 2020.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 온라인 문제기반학습에서 학습자의 학습행태에 따른 학습유형을 파악하고 각 학습유형의 특징을 조사하여 효과적인 온라인 문제기반학습 설계를 위한 시사점을 도출하기 위해 수행되었다. 본 연구를 위해 6주 동안 K대학에서 운영된 문제기반학습 프로그램에 참여한 1,341명의 초·중학생의 온라인 활동 데이터가 수집되었고, 이를 통하여 학습자들의 학습행태를 나타내는 48개의 변인이 추출되었다. 추출된 변인은 학습자들의 학습유형을 구분하기 위한 계층적 군집분석 기법에 활용되었으며, 구분된 학습유형에 따라 학습행태와 학업성취도 측면에서 어떠한 차이가 있는지 비교·분석하였다. 그 결과, 학습자의 온라인 학습유형은 학습참여 수준에 따라 '고수준 학습참여형(군집 1)', '중수준 학습참여형(군집 2)', '저수준 학습참여형(군집 3)'으로 구분되었다. 또한, 학습참여 수준이 높은 군집이 높은 학업성취도를 얻은 것으로 확인되었다. 이러한 결과를 바탕으로 온라인 문제기반학습을 효과적으로 설계·운영하기 위한 시사점을 제시하였다.

The purpose of the study is to provide educational implications for more effective Problem-based learning(PBL) by investigating students' learning types based on their online learning behaviors. A total of 1,341 students participated in the study, and they engaged in a six-week-long PBL program run by K University. For the study, participants' online activity data were collected. From the data, a total of 48 variables that represent their various online learning behaviors were extracted. Based on the variables, hierarchical cluster analysis was conducted to analyze learning types. Also, the differences in learning characteristics and achievements were investigated by considering types of learning. As a result, the learning types in online PBL were classified as 'high-level participation (cluster 1)', 'medium-level participation (cluster 2)', and 'low-level participation (cluster 3)'. In addition, the achievement level was found to be highest in 'high-level participation (cluster 1)' and lowest in 'low-level participation (cluster 3)'. Based on the results, the implications for improving online PBL were suggested.

키워드

참고문헌

  1. Barrows, H.S., &Myers, A. C. (1993). Problem based learning in secondary schools. Unpublished monograph. Springfield, IL: Problem Based Learning Institute. Lanphier High School and Southern Illinois University Medical School.
  2. 홍기칠 (2009). 자기주도학습력 수준에 따른 문제중심학습의 효과. 사고개발, 5(2), 25-48.
  3. Papamitsiou, Z., &Economides, A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49-64.
  4. Juhanak, L., Zounek, J., &Rohlikova, L. (2019). Using process mining to analyze students' quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496-506. https://doi.org/10.1016/j.chb.2017.12.015
  5. Kuo, Y., Luo, J., &Brielmaier, J. (2015). Investigating students' use of lecture videos in online courses: A case study for understanding learning behaviors via data mining. Advances in Web-Based Learning-ICWL 2015, 231-237.
  6. Shimada, A., Okubo, F., Yin, C., Terai, M., Kentaro, K., Yamada, M., &Ogata, H. (2015). Analysis of preview behavior in e-Book system. Workshop Proceedings of the 23rd International Conference on Computers in Education, ICCE 2015, 593-600.
  7. Jovanovic, J., Gasevic, D., Dawson, S., Pardo, A., &Mirriahi, N. (2017). Learning Analytics to Unveil Learning Strategies in a Flipped Classroom. The Internet and Higher Education, 33, 74-85. https://doi.org/10.1016/j.iheduc.2017.02.001
  8. Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: using learning analytics to detect patterns in the Learning of Computer Programming. Journal of the Learning Sciences, 23(4), 561-599. https://doi.org/10.1080/10508406.2014.954750
  9. 한정윤.이성혜 (2019). 온라인 소프트웨어 교육에서 학습자의 자기조절학습 관련 특성에 기반한 온라인 학습 유형 분석: 계층적 군집 분석 기법을 활용하여. 컴퓨터교육학회 논문지, 22(5), 51-65.
  10. Antonenko, P. D., Toy, S., &Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398. https://doi.org/10.1007/s11423-012-9235-8
  11. Barrows, H. S., &Tamblyn, R. M. (1980). Problem based learning: An approach to medical education. New York: Springer Publishing Company.
  12. 조연순 (2006). 문제중심학습의 이론과 실제. 서울: 학지사
  13. Fogarty, R. (1997). Problem-based learning & other curriculum models for the multiple intelligences classroom. Arlington eights. IL: IRI SkyLight.
  14. Yeo, J. (2015). Building theory-practice nexus in pre-service physics teacher education through problem-based learning. Walker, A., Leary, H., Hmelo-Silver, C.E. & Ertmer, P.A. (Ed.) Essential readings in Problem Based Learning. Purdue University Press. West Lafayette, Indiana.
  15. 장경원 (2006). 온라인 PBL에서 학습자들의 문제해결 활동 특성 분석. 교육정보미디어연구, 12(3), 33-63.
  16. Schmidt, H. G., Rotgans, J. I., &Yew, E H. (2011). The process of problem‐based learning: what works and why. Medical Education 45, 792-806. https://doi.org/10.1111/j.1365-2923.2011.04035.x
  17. 강미량 (2003). PBL이 아동의 사회도덕성에 미치는 영향. 강인애 (편). PBL의 이론과 실제. 175-220. 서울: 문음사[11]
  18. 최희정 (2004). 문제중심학습이 간호학생의 비판적 사고, 메타인지 및 문제해결과정에 미치는 영향. 대한간호학회지, 34(5), 712-721. https://doi.org/10.4040/jkan.2004.34.5.712
  19. 이정미 (2013). 문헌정보학 교육에서 문제기반 학습법 적용이 자기조절학습능력 향상에 미치는 효과. 한국문헌정보학회지, 47(4), 103-124. https://doi.org/10.4275/KSLIS.2013.47.4.103
  20. Atan, H., Sulaiman, F. &Idrus, R. M.(2005). The effectiveness of problem-based learning in the web-based environment for the delivery of an undergraduate physics course. International Education Journal, 6(4), 430-437.
  21. 김경현.정미경.최운필 (2005). 웹 기반 문제중심학습 프로그램 개발과 학업성취에 미치는 효과분석. 한국정보교육학회, 9(1), 1-14.
  22. 성은모.최욱 (2006). 인터넷 활용 문제중심 탐구학습이 학습자들의 탐구능력, 학업성취도 및 파지에 미치는 효과. 교육정보미디어연구, 12(2), 129-159.
  23. Uribe, D., Klein, J. D. &Sullivan, H. (2003). The effect of computer-mediated collaborative learning on solving ill-defined problems. Educational Technology, Research and Development, 51(1), 5-19. https://doi.org/10.1007/BF02504514
  24. Lee, S., Chae, Y., &Choi K. (2019). Learners' perceptions and experiences of using e-textbooks in online learning environment. Educational Technology International, 20(2), 195-221.
  25. 이인경 (1997). 강의기반 학습과 문제기반 학습에 있어서의 학습전략에 관한 연구. 교육공학연구, 13(2), 241-261.
  26. Siemens, G., &Long, P. (2011). Penetrating the fog-analytics in learning and education. Educause Review, 46(5), 30-32.
  27. Dutt, A., Ismail, M.A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5 (2017), 15991-16005. https://doi.org/10.1109/ACCESS.2017.2654247
  28. Berland, M., Martin, T., Benton, T., Smith, C. P., & Davis, D. (2013). Using Learning Analytics to Understand the Learning Pathways of Novice Programmers. Journal of the Learning Sciences, 22(4), 564-599. https://doi.org/10.1080/10508406.2013.836655
  29. Hastie, T., Tibshirani, R., Friedman, J., &Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85.
  30. Langfelder, P., Zhang, B., &Horvath, S. (2007). Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics, 24(5), 719-720. https://doi.org/10.1093/bioinformatics/btm563
  31. Hair, J. F., &Black, W. C. (2000). Cluster analysis. In L. G. Grim & P. R. Yarnold (Eds.), Reading and Understanding More Multivariate Statistics. (pp. 147-205). Washington, DC: Psychological Association.
  32. Ward, J. H. (1968). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236-244. https://doi.org/10.1080/01621459.1963.10500845
  33. You, J. W. (2015). Examining the effect of academic procrastination on achievement using LMS data in e-learning. Educational Technology & Society, 18(3), 124-134.
  34. Cho, M. &You, J. S. (2017). Exploring online students' self-regulated learning with self-reported surveys and log files: a data mining approach. Interactive Learning Environment, 25(8), 970-982. https://doi.org/10.1080/10494820.2016.1232278

피인용 문헌

  1. 비대면 강의환경에서의 온라인 학습패턴과 학습 효과의 상관관계 연구 vol.21, pp.8, 2020, https://doi.org/10.5762/kais.2020.21.8.557
  2. 영어 듣기와 읽기 수업을 위한 블렌디드 러닝 사례 연구 vol.19, pp.4, 2021, https://doi.org/10.14400/jdc.2021.19.4.241