Predicting academic performance: A systematic literature review

Arto Hellas, Petri Ihantola, Andrew Petersen, Vangel V. Ajanovski, Mirela Gutica, Timo Hynninen, Antti Knutas, Juho Leinonen, Chris Messom, Soohyun Nam Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

Original languageEnglish (US)
Title of host publicationITiCSE 2018 Companion - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
EditorsBruce Scharlau, Guido Rossling
PublisherAssociation for Computing Machinery
Pages175-199
Number of pages25
ISBN (Electronic)9781450362238
DOIs
StatePublished - Jul 2 2018
Event23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 - Larnaca, Cyprus
Duration: Jul 2 2018Jul 4 2018

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
ISSN (Print)1942-647X

Conference

Conference23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018
CountryCyprus
CityLarnaca
Period7/2/187/4/18

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • Education

Keywords

  • Analytics
  • Educational data mining
  • Learning analytics
  • Literature review
  • Mapping study
  • Performance
  • Prediction

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