대학 블랜디드 학습 환경에서 학습자 특성과 온라인 학습 활동이 학업 성취에 미치는 효과: 학습분석 접근법 = The Effect of Student Characteristics and Online Behaviors on Student Learning Achievement in Blended-learning Environments Using Learning Analytics Approaches

다국어 초록 (Multilingual Abstract)

The purpose of this study is to investigate the relationships between student characteristics, online learning activities, and learning outcomes in blended-learning environments. Studies on learning analytics in higher education consistently suggest that the analytics regarding the relationships among student demographic data, student interaction or behaviors on the LMS will predict the at-risk students and will provide the appropriate feedback to lower drop-out rates. Two types of logistic regression analyses were conducted to understand student learning and predict the learning performance in blended-learning environments. The results revealed that the best predictive variables for student learning performance were the student’s first and second week activities on the LMS - assignment submission behaviors on the first and second weeks. In addition, frequency of log-in activities on the LMS and learning time were significantly related to student performance on the course. This study provided highly reliable predictive models (85%-95%classification rate) using student LMS weekly behaviors with student characteristics to predict student performance in blended-learning environments.