Learning Analytics for Smart Learning

Although several qualitative analyses appeared in the domain of Learning Analytics (LA), a systematic quantitative analysis of the effects of the empirical research findings toward the development of more reliable Smart Learning Environments (SLE) is still missing. This chapter aims at preserving and enhancing the chronicles of recent LA developments as well as covering the Z. Papamitsiou (*) • A.A. Economides Interdepartmental Programme of Postgraduate Studies in Information Systems, University of Macedonia, Thessaloniki, Greece e-mail: [email protected]; [email protected] # Springer International Publishing Switzerland 2016 J.M. Spector et al. (eds.), Learning, Design, and Technology, DOI 10.1007/978-3-319-17727-4_15-1 1 abovementioned gap. The core question is where these two research areas intersect and how the significant LA research findings could be beneficial for guiding the construction of SLEs. This meta-analysis study synthesizes research on the effectiveness of LA and targets at determining the influence of its dimensions on learning outcomes so far. Sixty-six experimental and quasi-experimental papers published from 2009 through September 2015 in the domain of LA were coded and analyzed. Overall, the weighted random effects mean effect size (g) was 0.433 (p = 0.001). The collection was heterogeneous (Qt(66) = 78.47). Here, the results of the statistical and classification processes applied during the meta-analysis process are presented and the most important issues raised are discussed.