TY - JOUR
T1 - Relations between student online learning behavior and academic achievement in higher education
T2 - A learning analytics approach
AU - Jo, Il Hyun
AU - Yu, Taeho
AU - Lee, Hyeyun
AU - Kim, Yeonjoo
N1 - Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.
PY - 2015
Y1 - 2015
N2 - The purpose of this study is to suggest more meaningful components for learning analytics in order to help learners to improve their learning achievement continuously through an educational technology approach. 41 undergraduate students in a women’s university in South Korea participated in this study. The seven-predictor model was able to account for 99.3% of the variance in the final grade, F(8, 32) = 547.424, p <. 001, R2 =.993. Total login frequency in LMS, (ir)regularity of learning interval in LMS, and total assignments and assessment composites had a significant (p <.05) correlation with final grades. However, total studying time in LMS (β =.038, t =.868, p >.05), interactions with content (β = −.004, t = −.240, p >.05), interactions with peers (β =.015, t =.766, p >.05), and interactions with instructor (β =.009, t =.354, p >.05) did not predict final grades. The results provide a rationale for the treatment for student time management effort.
AB - The purpose of this study is to suggest more meaningful components for learning analytics in order to help learners to improve their learning achievement continuously through an educational technology approach. 41 undergraduate students in a women’s university in South Korea participated in this study. The seven-predictor model was able to account for 99.3% of the variance in the final grade, F(8, 32) = 547.424, p <. 001, R2 =.993. Total login frequency in LMS, (ir)regularity of learning interval in LMS, and total assignments and assessment composites had a significant (p <.05) correlation with final grades. However, total studying time in LMS (β =.038, t =.868, p >.05), interactions with content (β = −.004, t = −.240, p >.05), interactions with peers (β =.015, t =.766, p >.05), and interactions with instructor (β =.009, t =.354, p >.05) did not predict final grades. The results provide a rationale for the treatment for student time management effort.
KW - E-learning
KW - Educational technology
KW - Higher education
KW - Learning analytics
UR - http://www.scopus.com/inward/record.url?scp=84994332342&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-44188-6_38
DO - 10.1007/978-3-662-44188-6_38
M3 - Article
AN - SCOPUS:84994332342
SN - 2196-4963
SP - 275
EP - 287
JO - Lecture Notes in Educational Technology
JF - Lecture Notes in Educational Technology
IS - 9783662441879
ER -