사이버 대학에서 학습자 특성 및 학습 활동이 학업성취도에 미치는 영향: 3P 모형을 활용한 학습분석적 접근 = (Re)Binding the Factors Affecting Student Learning Outcomes in a Cyber University Using the 3P Model: Learning Analytics Approaches

다국어 초록 (Multilingual Abstract)

This study investigates the relationships among the presage, process, and product factors drawn from Biggs’ 3P model in one cyber university. Although previous studies thoroughly and clearly found the interrelationships among one or two student characteristics factors and learning outcomes, the current study aimed at extending those findings into holistically understanding the relationship among those factors by using learning analytics (educational data mining) approaches. The framework was drawn from Biggs’ 3P model; each P factor was examined to comprehend the relationship among the presage, process, and product factors. A total of 14,105 aggregated student data were collected to investigate the factors relating to student learning outcomes during the last two semesters-Fall, 2013, and Spring, 2014. Logit model from logistic sequential regression showed a sound predictability and model fit. The results showed that six student characteristics-academic discipline, gender, age, level of education, jobs, and year-and two learning process factors-content-to-access ratio and the numbers of participation in the course bulletins-were closely related to student learning outcomes. Specifically, student learning process factors were identified as the ones that mostly affected student learning outcomes. These findings implied that student learning outcomes were not simply predicted by one or two characteristics and learning processes, but rather were the complicated interaction of factors in a cyber university.

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