자기조절학습 행동데이터에 대한 탐색적 연구: 해외 학습분석 연구를 중심으로 = An Exploration of Self-regulated Learning Behavior Data: International Research on Learning Analytics

국문 초록 (Abstract)

본 연구의 목적은 학습분석 기반의 자기조절학습 연구의 해외 연구 동향을 분석하고, 자기조절학습의 하위전략과 전략별 분석데이터가 무엇인지 분석하는 데 있다. 이를 위해 2020년까지 발표된 해외 학술지 논문 및 학술대회 발표자료이면서, 자기조절학습 또는 자기조절학습의 하위요인을 학습분석 방법을 적용하여 분석한 논문 50편을 분석하였다. 학습분석 기반 자기조절학습 연구의 전반적인 동향을 분석하기 위해 연도별, 연구맥락별, 데이터 수집 환경별, 연구목적별, 자기조절학습 전략 및 하위요인별 빈도 분석을 실시하였다. 분석 대상 연구 중 자기조절학습의 하위요인을 명시한 연구를 바탕으로 이들이 분석한 요인과 이러한 요인을 측정하기 위해 활용된 분석데이터를 제시하였다. 분석결과 2012년 이후 꾸준히 연구수가 증가하는 추세를 보였으며, 대학 맥락에서 수행된 연구가 대부분이었다. 데이터가 수집된 학습환경은 MOOC를 포함한 학습관리시스템이 절반 이상을 차지했다. 연구목적별로는 자기조절학습 행동을 기반으로 학습자 및 학습 행동 모델링, 행동데이터를 활용하여 성취도 등 수행결과 예측, 수행결과와의 관계 분석, 자기보고식 자기조절학습 설문 결과와 행동데이터 비교 또는 그 관계를 살펴본 연구 등으로 분류되었다. 자기조절학습 전략별로는 메타인지전략, 행동조절전략, 인지전략, 환경조절전략, 학습전략 순으로 논문 수가 많았으며, 가장 많은 연구에서 분석된 하위요인은 시간관리, 자기평가 및 성찰이었다. 또한 이와 같은 요인을 측정하기 위한 데이터가 무엇인지를 제시하고, 이를 바탕으로 자기조절학습 행동 분석데이터의 특징과 학습분석 연구를 위한 시사점을 제안하였다.

다국어 초록 (Multilingual Abstract)

This study seeks to analyze trends in overseas research on self-regulated learning based on learning analytics, and to analyze sub-strategies of self-regulated learning and lists of data. For the analysis, 50 papers from overseas journals and conference proceedings prior to 2020 were screened. Frequency analyses are calculated by year, research context, data collection environment, research purpose, self-regulated learning strategy, and sub-factor. Sub-factors and analyzed data are then described in light of previous studies that specify sub-factors of self-regulated learning. The results indicated a steady increase in the number of studies since 2012, most of which were conducted in the context of universities. In over half of these studies, data was collected on Learning Management Systems (LMS) and Massive Open Online Courses (MOOC). With respect to research purpose, studies may be classified as learners and learning behaviors modeling, performance prediction, relationship analysis with performance results, and behavior comparison between survey and behavioral data. With respect to self-regulated learning strategy, the greatest number of papers concern metacognitive strategy, followed by behavior control strategy, cognitive control strategy, environmental control strategy, and learning strategy. Time management and self-evaluation and reflection are the most frequently analyzed sub-factors. Analyzed data were also collected to measure these factors. Based on these results above, analysis of data on the features of self-regulating learning behavior is presented and the implications for learning analysis research are suggested.

참고문헌 (Reference)

1 안미리, "해외 학습분석학(Learning Analytics) 연구에 대한 동향 분석: 실증 연구 중심으로" 한국교육정보미디어학회 21 (21): 601-643, 2015

2 안미리, "학습분석학 국내 문헌 고찰: 로그 데이터를 이용한 실증연구를 중심으로" 한국교육공학회 32 (32): 253-291, 2016

3 성은모, "학습분석학 관점에서 학습자의 자기주도학습 지원을 위한 학습 데이터 탐색 연구" 한국교육공학회 32 (32): 487-533, 2016

4 양명희, "자기조절학습의 모형탐색과 타당화 연구" 서울대학교 대학원 2000

5 권성연, "자기조절학습의 단계와 구성 요인 규명" 이화여자대학교 대학원 2003

6 한정윤, "온라인 소프트웨어 교육에서 학습자의 자기조절학습 관련 특성에 기반한 온라인 학습 유형 분석: 계층적 군집 분석 기법을 활용하여" 한국컴퓨터교육학회 22 (22): 51-65, 2019

7 Juhaňák, L., "Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system" 92 : 496-506, 2019

8 Montgomery, A., "Using learning analytics to explore self-regulated learning in flipped blended learning music teacher education" 50 (50): 114-127, 2019

9 de Barba, P., "The importance and meaning of session behaviour in a MOOC" 146 : 103772-, 2020

10 Rake, G., "The impact of online graduate students’ motivation and self regulation on academic procrastination" 9 (9): 78-93, 2010

11 Panadero, E., "The future of student self assessment : A review of known unknowns and potential directions" 28 : 803-830, 2016

12 Hill, J. R., "Teaching and learning in digital environments : The resurgence of resource-based learning" 49 (49): 37-52, 2001

13 Bandura, A., "Social foundations of thought and action: A social cognitive theory" Prentice-Hall 1986

14 Broadbent, J., "Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review" 27 : 1-13, 2015

15 Viberg, O., "Self-Regulated Learning and Learning Analytics in Online Learning Environments : A Review of Empirical Research" 524-533, 2020

16 Siemens, G., "Penetrating the fog-analytics in learning and education" 46 (46): 30-32, 2011

17 Artino, A. R., "Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training" 24 : 260-270, 2008

18 Pintrich, P., "Motivational and self-regulated learning components of classroom academic performance" 82 (82): 33-40, 1990

19 Puustinen, M., "Models of self-regulated learning : A review" 45 (45): 269-286, 2001

20 Weinstein, C., "Learning and Study Strategies Inventory" H & H Publishing 1987

21 Jovanović, J., "Learning analytics to unveil learning strategies in a flipped classroom" 33 : 74-85, 2017

22 Yamada, M., "Learning analytics in ubiquitous learning environments: Self-regulated learning perspective" Asia-Pacific Society for Computers in Education 306-331, 2016

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

24 Sun, Z., "International Society of the Learning Sciences" 66-73, 2016

25 Winne, P., "Improving measurements of self-regulated learning. A metacognitive view of individual differences in self-regulated learning" 45 (45): 267-276, 2010

26 You, J., "Identifying significant indicators using LMS data to predict course achievement in online learning" 29 : 23-30, 2016

27 Zimmerman, B., "Handbook of Self-regulation" Academic Press 2000

28 Papamitsiou, Z., "From childhood to maturity : Are we there yet?-Mapping the intellectual progress in learning analytics during the past decade" 559-568, 2020

29 Fincham, E., "From Study Tactics to Learning Strategies : An Analytical Method for Extracting Interpretable Representations" 12 (12): 59-72, 2019

30 Rodrigues, R., "Forecasting students’ performance through self-regulated learning behavioral analysis" 17 (17): 52-74, 2019

31 Diana, N., "Extracting measures of active learning and student self-regulated learning strategies from MOOC data" 583-585, 2016

32 Pardo, A., "Exploring the relation between Self-regulation, Online activities, and academic performance: A case study" 422-429, 2016

33 Wong, J., "Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course" 140 : 103595-, 2019

34 Li, S., "Examining temporal dynamics of self-regulated learning behaviors in STEM learning : A network approach" 158 : 103987-, 2020

35 Zimmerman, B., "Development of structures interview for assessing student use of self-regulated learning strategies" 23 (23): 614-628, 1986

36 Winne, P., "Construct and consequential validity for learning analytics based on trace data" 112 : 106457-, 2020

37 Pardo, A., "Combining University student self-regulated learning indicators and engagement with online learning events to predict academic performance" 10 (10): 82-92, 2017

38 Thoresen, C. E., "Behavioral self-control" Rinehart & Winston 1974

39 Zimmerman, B., "Becoming a self-regulated learner: Which are the key subprocesses?" 11 : 307-313, 1986

40 Uzir, N., "Analytics of time management and learning strategies for effective online learning in blended environments" 392-401, 2020

41 Matcha, W., "Analytics of learning strategies : Associations with academic performance and feedback" 461-470, 2019

42 Bakar, M., "A process mining approach to understand self regulated-learning in moodle environment" 8 (8): 74-80, 2019

43 Pintrich, P., "A conceptual framework for assessing motivation and self-regulated learning in college students" 16 (16): 385-407, 2004