Predicting academic performance: a systematic literature review

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

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

16 Citations (Scopus)

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
Title of host publicationITiCSE 2018 Companion - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
Subtitle of host publicationJuly 2–4, 2018 Larnaca, Cyprus
EditorsGuido Rossling, Bruce Scharlau
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages175-199
Number of pages25
ISBN (Electronic)9781450362238
DOIs
Publication statusPublished - 2018
EventAnnual Conference on Innovation and Technology in Computer Science Education 2018 - Larnaca, Cyprus
Duration: 2 Jul 20184 Jul 2018
Conference number: 23rd
https://iticse.acm.org/ITiCSE2018/

Conference

ConferenceAnnual Conference on Innovation and Technology in Computer Science Education 2018
Abbreviated titleITiCSE 2018
CountryCyprus
CityLarnaca
Period2/07/184/07/18
Internet address

Keywords

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

Cite this

Hellas, A., Ajanovski, V. V., Knutas, A., Ihantola, P., Gutica, M., Leinonen, J., Liao, S. N., Petersen, A., Hynninen, T., & Messom, C. (2018). Predicting academic performance: a systematic literature review. In G. Rossling, & B. Scharlau (Eds.), ITiCSE 2018 Companion - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education: July 2–4, 2018 Larnaca, Cyprus (pp. 175-199). Association for Computing Machinery (ACM). https://doi.org/10.1145/3293881.3295783