@inproceedings{e62211fd69e84d89a9f37e98eef25dac,
title = "Predicting academic performance: A systematic literature 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.",
keywords = "Analytics, Educational data mining, Learning analytics, Literature review, Mapping study, Performance, Prediction",
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}",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 ; Conference date: 02-07-2018 Through 04-07-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1145/3293881.3295783",
language = "English (US)",
series = "Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE",
publisher = "Association for Computing Machinery",
pages = "175--199",
editor = "Bruce Scharlau and Guido Rossling",
booktitle = "ITiCSE 2018 Companion - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education",
}