A systematic meta-Review and analysis of learning analytics research.

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. [ABSTRACT FROM AUTHOR]

Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)