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 language | English |
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Title of host publication | ITiCSE 2018 Companion - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education |
Subtitle of host publication | July 2–4, 2018 Larnaca, Cyprus |
Editors | Guido Rossling, Bruce Scharlau |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 175-199 |
Number of pages | 25 |
ISBN (Electronic) | 9781450362238 |
DOIs | |
Publication status | Published - 2018 |
Event | Annual Conference on Innovation and Technology in Computer Science Education - Working Groups Reports 2018 - Larnaca, Cyprus Duration: 2 Jul 2018 → 4 Jul 2018 https://dl.acm.org/doi/proceedings/10.1145/3293881 (Proceedings) |
Conference
Conference | Annual Conference on Innovation and Technology in Computer Science Education - Working Groups Reports 2018 |
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Abbreviated title | ITiCSE-WGR 2018 |
Country/Territory | Cyprus |
City | Larnaca |
Period | 2/07/18 → 4/07/18 |
Internet address |
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Keywords
- Analytics
- Educational data mining
- Learning analytics
- Literature review
- Mapping study
- Performance
- Prediction