A systematic meta-Review and analysis of learning analytics research. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Du, Xu; Yang, Juan; Shelton, Brett E.; Hung, Jui-Long; Zhang, Mingyan
• Is Part Of: Behaviour & information technology. Volume 40:Issue 1(2021)
• Page Start: 49
• Page End: 62
• Publication Date: 2021-01-02
• Subjects: Systematic meta-review -- learning analytics -- educational data mining -- big data -- prediction of performance -- learner modelling; Electronic data processing -- Periodicals; Human engineering -- Periodicals; Information technology -- Periodicals; 303.4833
• DOI: 10.1080/0144929X.2019.1669712
• Languages: English
• ISSNs: 0144-929X

Abstract

ABSTRACT: As an emerging field of research, learning analytics (LA) offers practitioners and researchers information about educational data that is helpful for supporting decisions in management of teaching and learning. While often combined with educational data mining (EDM), crucial distinctions exist for LA that mandate a separate review. This study aims to conduct a systematic meta-review of LA for mining key information that could assist in describing new and helpful directions to this field of inquiry. Within 901 LA articles analyzed, eight reviews were identified and synthesised to identify and determine consistencies and gaps. Results show that LA is at the stage of early majority and has attracted great research efforts from other fields. The majority of LA publications were focused on proposing LA concepts or frameworks and conducting proof-of-concept analysis rather than conducting actual data analysis. Collecting small datasets for LA research is predominant, especially in K-12 field. Finally, four major LA research topics, including prediction of performance, decision support for teachers and learners, detection of behavioural patterns & learner modelling and dropout prediction, were identified and discussed deeply. The future research of LA is also outlined for purpose of better understanding and optimising learning as well as learning contexts.