Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence.

TLDR

The study reviews empirical evidence on Learning Analytics and Educational Data Mining to outline their impact on adaptive learning and identify future research questions. The authors systematically examined 40 experimental case studies from 2008–2013, categorizing research questions, methods, and findings using non‑statistical evaluation. The review identifies four major research directions, highlights the added value of LA/EDM, and underscores the need for further investigation.

Abstract

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008-2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.