Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

Agudo-Peregrina, Ángel F. and Iglesias-Pradas, Santiago and Conde-González, Miguel Ángel and Hernández-García, Ángel (2014) Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31. pp. 542-550. ISSN 0747-5632

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Official URL: http://www.sciencedirect.com/science/article/pii/S...

Abstract

Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying infor- mation in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are discussed next.

Item Type: Article
Uncontrolled Keywords: Cognitive Depth 1, Indicators, multiple backwards stepwise regression, Total number of clicks, Interactions, Educational data, e-Learning, Learning analytics, Academic performance, Virtual learning environments
Subjects: Educational technology > Early warning systems
Educational technology > Learning analytics
Educational technology > Plugins
Divisions: Higher education, Universities, Vocational training, Colleges
Depositing User: Elizabeth Dalton
Date Deposited: 05 Jul 2017 21:34
Last Modified: 05 Jul 2017 21:34
URI: http://research.moodle.net/id/eprint/212

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