온라인 소프트웨어 교육에서 학습자의 자기조절학습 관련 특성에 기반한 온라인 학습 유형 분석: 계층적 군집 분석 기법을 활용하여 = Investigating Online Learning Types Based on self-regulated learning in Online Software Education: Applying Hierarchical Cluster Analysis

국문 초록 (Abstract)

본 연구에서는 온라인 소프트웨어 교육 환경에서 학습자의 자기조절학습특성에 따른 온라인 학습 유형을 파악하고 각 유형의 특징을 살펴봄으로써 보다 전략적인 온라인 소프트웨어 교육을 위한 시사점을 제공하고자 하였다. 이를 위해 K대학의 온라인 소프트웨어 교육과정에 참여하고 있는 K-12 학생 809명의 온라인 학습 로그 데이터로부터 자기조절학습 특성 변인을 추출한 후, 계층적 군집 분석 기법(hierarchical cluster analysis)을 활용하여 자기조절학습 특성에 따른 학습자 군집 도출 및 군집 유형에 따른 온라인 학습 특성과 학습 결과의 차이를 비교 분석하였다. 그 결과, 온라인 소프트웨어 교육 학습자들의 자기조절학습 유형은 '고수준 자기조절학습형(군집 1)', '중수준 자기조절학습형(군집 2)', 그리고 '저수준 자기조절학습형(군집 3)'으로 나타났다. 온라인 자기조절학습 유형에 따른 학업성취도 수준은 '고수준 자기조절학습형(군집 1)'이 가장 높고, '저수준 자기조절학습형(군집 3)'이 가장 낮은 것으로 확인되었다. 이러한 결과를 바탕으로 효과적인 온라인 소프트웨어 교육 운영을 위한 시사점을 제시하였다.

다국어 초록 (Multilingual Abstract)

This study aims to provide educational implications for more strategic online software education by the types of online learning according to learners' self-regulated learning characteristics in the online software education environment and examining the characteristics of each type. For this, variables related to self-regulated learning characteristic were extracted from the log data of 809 students participating in the online software learning program of K University, and then analyzed using hierarchical cluster analysis. Based on hierarchical cluster analysis learner clusters according to the characteristics of self-regulated learning were derived and the differences between learners' learning characteristics and learning results according to cluster types were examined. As a result, the types of self-regulated learning of online software learners were classified as 'high level self-regulated learning type (group 1)', 'medium level self-regulated learning type (group 2)', and 'low level self-regulated learning type (group 3)'. The achievement level was found to be highest in 'high-level self-regulated learning type (group 1)' and 'low-level self-regulated learning type (group 3)' was the lowest. Based on these results, the implications for effective online software education were suggested.

참고문헌 (Reference)

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

2 나일주, "학습분석 모델 및 확장 방안 연구 보고서" 서울특별시교육청, 서울대학교 2015

3 이미숙, "컴퓨터 기능 교육에서 초인지를 이용한 협력적 성찰 수업모형의 개발 및 적용" 한국정보교육학회 9 (9): 339-348, 2005

4 김갑수, "초등학생들을 위한 프로그래밍 언어 교육 방법론" 초등교육연구원 19 (19): 135-152, 2009

5 노일경, "재직 학습자의 원격고등교육과정에서의 학습활동 특성 및 학업성취 영향 변인 분석: 학습분석을 적용하여" 원격교육연구소 12 (12): 53-78, 2016

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

7 성은모, "온라인 소프트웨어 교육 학습자들의 자기주도학습 유형 분류 및 특징 분석" 한국컴퓨터교육학회 22 (22): 31-46, 2019

8 교육부, "실과(기술·가정)/정보과 교육과정"

9 교육부, "소프트웨어 교육 활성화 기본계획"

10 Azevedo, R., "Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning" 40 (40): 199-209, 2005

11 Hastie, T., "The elements of statistical learning : data mining, inference and prediction" 27 (27): 83-85, 2005

12 Wong, J., "Supporting self-regulated learning in online learning environments and MOOCs: A systematic review" 35 (35): 356-373, 2017

13 Wang, C. H., "Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning" 34 (34): 302-323, 2013

14 Hew, K. F., "Students’ and instructors’ use of massive open online courses(MOOCs) : Motivations and challenges" 12 : 45-58, 2014

15 Xia, B. S., "Student performance in computing education: an empirical analysis of online learning in programming education environments" 42 (42): 1025-1037, 2017

16 Cho, M. H., "Self-regulation in online learning" 34 (34): 290-301, 2013

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

18 Zimmerman, B. J., "Self-regulated learning and academic achievement: An Overview" 25 (25): 3-17, 1990

19 Tsai, C. W., "Research trends in self-regulated learning research in online learning environments : A review of studies published in selected journals from 2003to 2012" 44 (44): 107-110, 2013

20 Hair, J. F., "Reading and Understanding More Multivariate Statistics" Psychological Association 147-205, 2000

21 Klingsieck, K. B., "Procrastination in a distance university setting" 33 (33): 295-310, 2012

22 Smith, V. C., "Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses" 16 (16): 51-61, 2012

23 Minaei-Bidgoli, B., "Predicting Student Performance: An Application of Data Mining Methods with an educational Web-based System" 2003

24 Macfadyen, L., "Numbers are not enough. why e-learning analytics failed to inform an institutional strategic plan" 15 (15): 149-163, 2012

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

26 Pintrich, P. R., "Motivational and self-regulated learning components of classroom academic performance" 82 (82): 33-50, 1990

27 Brooks, C., "Modelling and quantifying the behaviours of students in lecture capture environments" 75 : 282-292, 2014

28 Barnard, L., "Measuring self-regulation in online and blended learning environments" 12 (12): 1-6, 2009

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

30 Ward, J. H., "Hierarchical grouping to optimize an objective function" 58 : 236-244, 1968

31 Zimmerman, B. J., "Handbook of self-regulation of learning and performance" Routledge 1-12, 2011

32 Cho, M., "Exploring online students‘ self-regulated learning with self-reported surveys and log files : a data mining approach" 25 (25): 970-982, 2017

33 You, J. W., "Examining the effect of academic procrastination on achievement using LMS data in e-learning" 18 (18): 124-134, 2015

34 EBS, "EBS SW 소개"

35 Pintrich, P. R., "Development of achievement motivation" Academic 249-284, 2002

36 Huang, T., "Developing a self-regulated oriented online programming teaching and learning system" 115-120, 2014

37 Langfelder, P., "Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R" 24 (24): 719-720, 2007

38 Zimmerman, B., "Construct validation of a strategy model of student self-regulated learning" 80 : 284-290, 1988

39 Lehmann, T., "Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning" 32 : 313-323, 2014

40 Robinson, P., "An online learning platform for teaching, learning, and assessment of programming" 547-556, 2017

41 James, G., "An Introduction to Statistical Learning" Springer 2013

42 Lee, Y., "A review of online course dropout research : Implications for practice and future research" 59 : 593-618, 2011

43 정현철, "4차 산업혁명시대, 과학영재 어떻게 육성할 것인가" 학지사 2018

44 과학기술정보통신부, "019년도 소프트웨어 영재학급 선정 지원"