Educational technology approach toward learning analytics: relationship between student online behavior and learning performance in higher education

Abstract

The aim of this study is to suggest more meaningful components for learning analytics in order to help learners improving their learning achievement continuously through an educational technology approach. Multiple linear regression analysis is conducted to determine which factors influence student's academic achievement. 84 undergraduate students in a women's university in South Korea participated in this study. The six-predictor model was able to account for 33.5% of the variance in final grade, F(6, 77) = 6.457, p < .001, R2 = .335. Total studying time in LMS, interaction with peers, regularity of learning interval in LMS, and number of downloads were determined to be significant factors for students' academic achievement in online learning environment. These four controllable variables not only predict learning outcomes significantly but also can be changed if learners put more effort to improve their academic performance. The results provide a rationale for the treatment for student time management effort.