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 contributionScientificpeer-review

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 publicationProceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
Number of pages25
Place of PublicationNew York, NY, USA
PublisherACM
Publication date2018
Pages175-199
ISBN (Print)978-1-4503-6223-8
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in conference proceedings
Event23rd Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018) - University of Central Lancashire, Cyprus, Larnaca, Cyprus
Duration: 2 Jul 20184 Jul 2018
Conference number: 23
https://iticse.acm.org/

Publication series

NameITiCSE 2018 Companion
PublisherACM

Fields of Science

  • analytics
  • educational data mining
  • learning analytics
  • literature review
  • mapping study
  • performance
  • prediction
  • 113 Computer and information sciences

Cite this

Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., ... Liao, S. N. (2018). Predicting Academic Performance: A Systematic Literature Review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (pp. 175-199). (ITiCSE 2018 Companion). New York, NY, USA: ACM. https://doi.org/10.1145/3293881.3295783
Hellas, Arto ; Ihantola, Petri ; Petersen, Andrew ; Ajanovski, Vangel V. ; Gutica, Mirela ; Hynninen, Timo ; Knutas, Antti ; Leinonen, Juho ; Messom, Chris ; Liao, Soohyun Nam. / Predicting Academic Performance : A Systematic Literature Review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education . New York, NY, USA : ACM, 2018. pp. 175-199 (ITiCSE 2018 Companion).
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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.",
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author = "Arto Hellas and Petri Ihantola and Andrew Petersen and Ajanovski, {Vangel V.} and Mirela Gutica and Timo Hynninen and Antti Knutas and Juho Leinonen and Chris Messom and Liao, {Soohyun Nam}",
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Hellas, A, Ihantola, P, Petersen, A, Ajanovski, VV, Gutica, M, Hynninen, T, Knutas, A, Leinonen, J, Messom, C & Liao, SN 2018, Predicting Academic Performance: A Systematic Literature Review. in Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education . ITiCSE 2018 Companion, ACM, New York, NY, USA, pp. 175-199, 23rd Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018), Larnaca, Cyprus, 02/07/2018. https://doi.org/10.1145/3293881.3295783

Predicting Academic Performance : A Systematic Literature Review. / Hellas, Arto; Ihantola, Petri; Petersen, Andrew; Ajanovski, Vangel V.; Gutica, Mirela; Hynninen, Timo; Knutas, Antti; Leinonen, Juho; Messom, Chris; Liao, Soohyun Nam.

Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education . New York, NY, USA : ACM, 2018. p. 175-199 (ITiCSE 2018 Companion).

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

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Hellas A, Ihantola P, Petersen A, Ajanovski VV, Gutica M, Hynninen T et al. Predicting Academic Performance: A Systematic Literature Review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education . New York, NY, USA: ACM. 2018. p. 175-199. (ITiCSE 2018 Companion). https://doi.org/10.1145/3293881.3295783