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학습분석학 관점에서 학습자의 자기주도학습 지원을 위한 학습 데이터 탐색 연구

Exploring Learning Data for Supporting Self-Directed Learning in the Perspective of Learning Analytics

유미나|진성희|성은모
한국교육공학회 (Korean Society Of Educational Technology)|2016.09|교육공학연구 (Journal of Educational Technology)|제32권 제3호|pp. 487-533|DOI http://dx.doi.org/10.17232/KSET.32.3.487

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자기주도학습이러닝self-directed learning학습데이터e-Learninglearning activity학습활동학습행동learning behavior학습분석학learning analyticslearning data
주제분류교육학

초록

이러닝 학습을 성공적으로 수행하기 위해서는 학습에 대한 학습자의 자율성과 주도성을 기반으로 자기주도적인 학습이 이루어져야 한다. 이를 위해서는 학습자들의 자기주도학습을 증진시키고 이를 지원해 줄 수 있는 이러닝 학습환경의 제공을 위해 학습분석학 관점에서 그 요소들을 추출하는 것은 중요한 과제가 될 수 있다. 이에 본 연구에서는 이러닝 학습환경에서 학습자의 자기주도학습을 지원하기 위해 학습분석학 관점에서 활용될 수 있는 학습데이터의 유형과 특성에 대해서 이론적으로 탐색하고, 자기주도학습 구성요소별 관련 학습데이터 유형과 특성이 적합한지에 대한 타당성 여부를 전문가들의 의견을 수렴함으로써 객관적인 타당성을 확보하고자 하였다. 연구결과, 학습분석학 관점에서 이러닝 학습환경에서 적용 가능한 자기주도학습의 구성요소로 메타인지(계획, 모니터링, 성찰), 학습전략(예습, 복습, 노트필기, 강조하기, 정보공유, 질의응답), 행동관리(학습참여, 시간관리, 도움구하기) 등 3가지 영역으로 도출되었다. 이를 지원하기 위한 학습데이터의 유형으로는 이러닝 학습환경에서 학습을 수행하는 과정에서 발생하게 되는 모든 활동의 기록을 남는 학습 흔적 데이터(활동 시간, 개별활동 여부, 관계활동 여부 등), 학습자가 학습과정 중에 생성하게 되는 학습자 생성 데이터(계획, 목표, 메시지, 주석, 코멘트, 작성 글, 제출과제 등), 교수자 및 튜터가 학습자들의 수업을 안내하고 상호작용하며 학습과정의 성취수준을 파악하기 위한 평가활동 등으로 생성되는 교수자 및 튜터 생성 데이터(퀴즈 피드백, 학생 작성글 댓글, 퀴즈문항, 과제 점수 등), 학습분석학 관점에서 학습자가 남긴 흔적 및 생성데이터, 교수자 및 튜터가 남긴 흔적 및 생성데이터를 활용하여 학습분석학 관점에서 계산되어 산출 가능한 학습분석 데이터(시간, 빈도, 상대적/절대적 수준, 비율, 관계, 비교, 분포, 규칙성, 길이, 질적 데이터 등)가 도출되었다. 이를 바탕으로 자기주도학습 구성요인별 활용될 수 있는 학습활동 및 학습행동에 따른 학습데이터의 유형을 분류하였다. 이러한 연구결과를 바탕으로 학습분석학 관점에서 도출된 자기주도학습지원을 위한 학습데이터 결과도출에 대한 의의와 제한점, 그리고 이러닝 학습환경 설계에 대한 시사점을 제시하였다.

Abstract

In order for a successful e-Learning, it should be made for learner’s self-directed learning (SDL) based on the autonomy and initiative of learning. Thus, promoting the SDL of the learner and providing SDL environment of e-learning may be one of the important subject. The present study aims to explore learning data for supporting SDL in the perspective of learning analytics for e-Learning. For the purpose, factors of SDL were explored to be related learning data in the perspective of learning analytics in e-Learning. Also, learning data for supporting SDL in e-Learning was categorized and validated by experts who were learning analytics. As a result, the 3 domains of SDL in the perspective of learning analytics in e-Learning were extracted; meta-cognition(planning, monitoring, and reflecting), learning strategy(preparation strategy, review strategy, note taking, highlighting, information sharing strategy, and questioning-answering strategy), behavior management(learning engagement, time management, getting help). Also, there were found out 3 styles for learning data to promoting SDL in e-Learning; learning trace data(activity time, activity whether or not), student artifacts data(plan, goal, message, remark, comment, response text, submit assignment), teacher and tutor artifacts data(quiz feedback, response text for students, post quiz items, post assignment scores), learning analysis data(time, frequency, relative and absolute level, ratio, relation, comparison, distribution, regularity, length, qualitative data). Based on the results, meaning, implication, and limitation of the study on the exploring learning data for supporting SDL in the perspective of learning analytics for e-Learning were discussed.

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