research-article

Open access

Predicting academic performance: a systematic literature review

Authors:

Petri Ihantola,

Andrew Petersen,

Vangel V. Ajanovski,

Soohyun Nam LiaoAuthors Info & Claims

ITiCSE 2018 Companion: Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education

Pages 175 - 199

https://doi.org/10.1145/3293881.3295783

Published: 02 July 2018 Publication History

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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Index Terms

Predicting academic performance: a systematic literature review
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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ITiCSE 2018 Companion: Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education

July 2018

235 pages

ISBN:9781450362238

DOI:10.1145/3293881

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University of Aberdeen, UK

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