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25. Predicting Academic Performance: A Systematic Literature Review

Author

Antti Knutas, Andrew Petersen, Chris Messom, Mirela Gutica, Soohyun Nam Liao, Juho Leinonen, Vangel V. Ajanovski, Timo Hynninen, Petri Ihantola, Arto Hellas, Department of Computer Science, RAGE - Agile Education Research group / Matti Luukkainen, and Department of Education

Subjects
educational data mining, Computer science, literature review, Learning analytics, 02 engineering and technology, Academic achievement, Educational data mining, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Performance prediction, analytics, learning analytics, business.industry, 4. Education, 05 social sciences, 050301 education, prediction, mapping study, 113 Computer and information sciences, Data science, Variety (cybernetics), Systematic review, Work (electrical), Analytics, business, 0503 education, performance
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.

Published
2018

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