해외 학습분석학(Learning Analytics) 연구에 대한 동향 분석: 실증 연구 중심으로 = An International Literature Review on Learning Analytics: Focused on empirical studies

다국어 초록 (Multilingual Abstract)

Advances in technology brings mcuh changes in learning environment. Especially, advances in Internet technology have created various learning settings such as LMSs/VLEs, MOOC, and Web-based learning. These newly emerging learning settings have contributed to provide more effective learning environment for learners than ever before. These learning environment enable collection of vast amount of data and analyze such data for meaningful interpretation. Recently, much attention has been paid to Learning Analytics in order to understand different learning types or learners, predict learners’ performances, and further to develop various teaching strategies under those learning settings. Although many studies on Learning Analytics have been conducted, we lack information about research objectives, learning settings, and data analysis methods used in Learning Analytics. Therefore, the purpose of this literature review of international research was to identify trends of current Learning Analytics studies in terms of research objectives, learning settings, and data analysis methods. In this research, we reviewed a total of 154 published in international articles and presentations made between September of 2013 through August of 2015. This literature review applied Papamitsiou & Economides (2014) literature review framework and methods. The research findings showed that (1) prediction of performances, recommendation of resources, and student behavior modeling were prevalent in terms of research objectives, (2) LMSs/VLEs and web-based education were prevalent in terms of learning settings, and (3) statistics was prevalent in terms of data analysis method. Specifically, various data analysis methods have been used such as Text Mining and Social Network Analysis. Interestingly, Bayesian Inference Network or Machine Learning which is not commonly used in Educational Technology was found from this literature review. We suggested several implications to improve Learning Analytics in Korea. It is necessary to conduct studies with various learning objectives, learning settings, and data analysis methods. Finally, it should lead to conceptualize Learning Analytics for Korean researchers. In addition to these conclusion, the use of data for education offer implication for researchers in the field of Korean Educational Technology. The research using Learning Analytics can provide strategies for designing hybrid learning environment, student achievements and feedback, and the visualized data could provide additional information about students’ learning process. Further researches in the area of Learning Analytics can provide important implications for those who research to improve online and offline learning environment.

참고문헌 (Reference)

1 하건희, "학습자 성찰 도구로써 대시보드 개발을 위한 사용성 검사 연구" 이화여자대학교 2015

2 조일현, "학습분석학을 활용한 e-러닝 학업성과 추정 모형의 통계적 유의성 확보 시점 규명" 한국교육공학회 29 (29): 285-306, 2013

3 박연정, "학습 분석학 기반 대시보드의 설계와 적용" 한국교육정보미디어학회 20 (20): 191-216, 2014

4 김민하, "학습 분석의 데이터 유형과 응용 분야" 2015

5 조용상, "표준화 이슈리포트: 학습분석 기술 활용 가능성 및 전망" 한국교육학술정보원 2013

6 노규성, "이러닝에서의 빅데이터 적용 정책 연구" 미래창조과학부 2014

7 조일현, "이러닝에서 학습자의 시간관리 전략이 학업성취도에 미치는 영향: 학습분석학적 접근" 한국교육정보미디어학회 19 (19): 83-107, 2013

8 이영환, "웹 3. 0 : 세상을 바꾸고 있다" 보문각 2010

9 허희옥, "웹 2.0의 교육적 활용에 대한 연구 동향 분석: 블로그와 위키를 중심으로" 한국컴퓨터교육학회 13 (13): 59-70, 2010

10 김성호, "웹 2.0 전망 및 서비스 동향에 관한 연구" 한국디지털정책학회 5 (5): 135-154, 2007

11 정윤혁, "빅데이터와 교육분석(Education Analytics)" 5 (5): 44-49, 2015

12 권영옥, "빅데이터를 활용한 맞춤형 교육 서비스 활성화 방안연구" 한국지능정보시스템학회 19 (19): 87-100, 2013

13 고윤미, "국내 Learning Analytics 연구 동향 분석" 2015

14 O'Reilly, T., "What is web 2.0" O'Reilly Media, Inc 2009

15 Siemens, G., "What are learning analytics?"

16 류중희, "Web2. 0에서 Mobile 2. 0으로" 23 (23): 99-106, 2006

17 Schroeder, U., "Web-based learning-yes we can!" Springer Berlin Heidelberg 25-33, 2009

18 Cena, F., "Web 3.0: Merging semantic web with social web" 2009

19 Morris, R. D., "Web 3. 0 : Implications for online learning" 55 (55): 42-46, 2011

20 Coffrin, C., "Visualizing patterns of student engagement and performance in MOOCs" ACM 83-92, 2014

21 Antonenko, P. D., "Using cluster analysis for data mining in educational technology research" 60 (60): 383-398, 2012

22 안미리, "Use of learning analytics to assess learner progress: Baysian networks and other tools" 2015

23 Ferguson, R., "The state of learning analytics in 2012: A review and future challenges. (Technical Report KMI-2012)"

24 Johnson, L., "The 2014 Horizon Report" The New Media Consortium 2014

25 Johnson, L., "The 2013 Horizon Report" The New Media Consortium 2013

26 Johnson, L., "The 2012 Horizon Report" The New Media Consortium 2012

27 Johnson, L., "The 2011 Horizon Report" The New Media Consortium 2011

28 Papamitsiou, Z. K., "Temporal learning analytics for computer based testing" ACM 31-35, 2014

29 Tane, J., "Semantic resource management for the web: an e-learning application" ACM 1-10, 2004

30 나일주, "National level data metrics framework development for learning analytics in South Korea" 2015

31 Jeong, H., "Mining student behavior models in learning-by-teaching environments" 2008

32 Bailey, K. D., "Methods of social research" The Free Press 1994

33 Elias, T., "Learning analytics: Definitions, processes and potential"

34 Papamitsiou, Z., "Learning analytics and educational data mining in practice : A systematic literature review of empirical evidence" 17 (17): 49-64, 2014

35 U.S., Department of Education, office of Educational Technology, "Enhancing teaching and learning through educational data mining and learning analytics: An issue brief" 2012

36 Romero, C., "Educational data mining-A review of the state of the art. Systems, Man, and Cybernetics, Part C-Applications and Reviews" 40 (40): 601-618, 2010

37 Liñán, L. C., "Educational data mining and learning analytics : differences, similarities, and time evolution. RUSC" 12 (12): 98-112, 2015

38 Yadav, S. K., "Data mining: A prediction for performance improvement of engineering students using classification"

39 Palazuelos, C., "Computational Collective Intelligence. Technologies and Applications" Springer Berlin Heidelberg 651-660, 2013

40 Hernández-García, Á., "Applying social learning analytics to message boards in online distance learning : A case study" 47 : 68-80, 2015

41 Campbell, J. P., "Academic analytics [White paper]"

42 Scott, J., "A matter of record: documentary sources in social research" Polity Press 1990

43 Educause, "7 Things you should know about analytics, EDUCAUSE 7 Things you should know series"