Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/26748
Title: Predicting academic performance: a systematic literature review
Authors: Hellas, Arto
Ihantola, Petri
Petersen, Andrew
Ajanovski, Vangel V.
Gutica, Mirela
Hynninen, Timo
Knutas, Antti
Leinonen, Juho
Messom, Chris
Liao, Soohyun Nam
Issue Date: 2-Jul-2018
Publisher: ACM
Conference: Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
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.
URI: http://hdl.handle.net/20.500.12188/26748
DOI: 10.1145/3293881.3295783
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

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