Educational data mining: a systematic review of research and emerging trends
Information Discovery and Delivery
ISSN: 2398-6247
Article publication date: 19 May 2020
Issue publication date: 10 October 2020
Abstract
Purpose
Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education.
Design/methodology/approach
This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research.
Findings
Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward.
Research limitations/implications
Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals.
Originality/value
This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.
Keywords
Acknowledgements
Conflict of interest: The authors have declared no conflicts of interest for this article.
This study was supported by National Natural Science Foundation of China Under Grant No. 61877027.
Citation
Du, X., Yang, J., Hung, J.-L. and Shelton, B. (2020), "Educational data mining: a systematic review of research and emerging trends", Information Discovery and Delivery, Vol. 48 No. 4, pp. 225-236. https://doi.org/10.1108/IDD-09-2019-0070
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited