Technical Journal, Vol. 9 No. 3, 2015.
Preliminary communication
Predictors of Online Learning Acceptance among University Students: An Analysis Based on Data Mining
Darko Dukić
orcid.org/0000-0002-9131-232X
; Sveučilište Josipa Jurja Strossmayera u Osijeku, Osijek, Hrvatska
Dina Jukić
; Sveučilište Josipa Jurja Strossmayera u Osijeku, Osijek, Hrvatska
Abstract
The development of information and communication technologies has a strong impact on the entire education. As a result, online learning occupies an increasingly important place in the teaching process at higher education institutions. The aim of this study is to determine the attitudes of Croatian university students toward online learning and to identify the most important predictors of its acceptance. The survey was conducted via an online questionnaire and the analysis was based on decision trees, one of the most popular data mining methods. According to the results, most students have a positive attitude toward online learning, and the level of ICT knowledge and skills stands out as the most significant predictor of acceptance. For students who rated their ICT knowledge and skills as very good, the next best predictor is the enrolment status, whereas for those with poorer competencies, it is gender.
Keywords
acceptance; data mining; decision trees; online learning; predictors; university students
Hrčak ID:
146234
URI
Publication date:
15.9.2015.
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