A systematic review of studies on predicting student learning outcomes using learning analytics

Xiao Hu, Christy W.L. Cheong, Wenwen Ding, Michelle Woo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Predicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.

Original languageEnglish
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Pages528-529
Number of pages2
ISBN (Electronic)9781450348706
DOIs
Publication statusPublished - 13 Mar 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: 13 Mar 201717 Mar 2017

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Learning Analytics and Knowledge, LAK 2017
Country/TerritoryCanada
CityVancouver
Period13/03/1717/03/17

Keywords

  • Learning context
  • Learning outcomes
  • Methods
  • Performances
  • Prediction
  • Systematic review

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