@article{
 title = {Relations between student online learning behavior and academic achievement in higher education: A learning analytics approach},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 keywords = {E-learning,Educational technology,Higher education,Learning analytics},
 pages = {275-287},
 publisher = {Springer International Publishing},
 id = {32dd264e-d410-3f9f-aa8d-84f279c01da6},
 created = {2020-02-03T13:53:54.912Z},
 accessed = {2020-02-03},
 file_attached = {false},
 profile_id = {bfdabac2-d7f2-3c5b-aa7a-06431c0ae35e},
 group_id = {ed1fa25d-c56b-3067-962d-9d08ff49394c},
 last_modified = {2020-02-03T13:53:55.317Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {af1b8628-85f8-4733-9f52-121159400517},
 private_publication = {false},
 abstract = {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.},
 bibtype = {article},
 author = {Jo, Il Hyun and Yu, Taeho and Lee, Hyeyun and Kim, Yeonjoo},
 journal = {Lecture Notes in Educational Technology},
 number = {9783662441879}
}

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