The DOI matches its Crossref record; 437 of the 439 references it cites were found in a registry or an index.
Predicting academic performance: a systematic literature review
Open accessAbstract
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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“Compared to their results, we find that the work in this area is reported more clearly in all four aspects measured.”
From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [92]
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1 retracted
Retracted: M5P model tree in predicting student performance: A case study
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Research works printed but not linked: 14
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- 10.5555/2684939.2684975 · DOI
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- J James Manoharan , Dr S Hari Ganesh , and M Lovelin Ponn Felcia . 2014 . Discovering Student’s Academic Performance Based on GPA using k-Means Clustering Algorithm. In IEEE World Congress on Computing and Communication Technology. J James Manoharan, Dr S Hari Ganesh, and M Lovelin Ponn Felcia. 2014. Discovering Student’s Academic Performance Based on GPA using k-Means Clustering Algorithm. In IEEE World Congress on Computing and Communication Technology.
- 10.5555/2145432.2145462 · DOI
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- 10.5555/2904446.2904471 · DOI
- Arto Vihavainen , Matti Luukkainen , and Jaakko Kurhila . 2013 . Using students’ programming behavior to predict success in an introductory mathematics course . In Proceedings of the 6th International Conference on Educational Data Mining. Arto Vihavainen, Matti Luukkainen, and Jaakko Kurhila. 2013. Using students’ programming behavior to predict success in an introductory mathematics course. In Proceedings of the 6th International Conference on Educational Data Mining.
Cited by
Sorted by citations
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00436-z · Cited by 852
“Future research should ensure that a detailed description is provided on what is being predicted, how and why (Hellas et al., 2018), could be deepened by more diverse study design, such as longitudinal and large-scale studies (Ifenthaler & Yau, 2020) with multiple data collection techniques (Abu…”
BackgroundMethodInfluentialYağcı, M. (2022) · Smart Learning Environments · doi:10.1186/s40561-022-00192-z · Cited by 709
Namoun, A., & Alshanqiti, A. M. (2020) · Applied Sciences · doi:10.3390/app11010237 · Cited by 480
A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques
Albreiki, B., Zaki, N., & Alashwal, H. (2021) · Education Sciences · doi:10.3390/educsci11090552 · Cited by 430
The Robots Are Here: Navigating the Generative AI Revolution in Computing Education
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., et al. (2023) · Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3623762.3633499 · Cited by 396
Self-citation
Metadata
- Authors
- Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N.
- Affiliations
- University of Helsinki (FI)
- University of Toronto (CA)
- Ss. Cyril and Methodius University in Skopje (MK)
- British Columbia Institute of Technology (CA)
- South Eastern Finland University of Applied Sciences (FI)
- Lappeenranta-Lahti University of Technology (FI)
- Monash University (AU)
- University of California San Diego (US)
- ORCID
- Venue
- Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
- Publisher
- ACM
- Published
- 2018-07-02
- Pages
- 175-199
- OpenAlex
- W2909913588
- Cited by
- 355
- References
- 439
- Citations by year
- 2026: 22 · 2025: 43 · 2024: 61 · 2023: 72 · 2022: 62 · 2021: 48 · 2020: 32 · 2019: 12 · 2018: 1
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Työväentutkimus Vuosikirja (submittedVersion, other-oa)
- ELARTU (Ternopil National Technical University) (submittedVersion)
- Monash University Research Portal (Monash University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Educational Outcomes and Influences
- Keywords
- student performance prediction, academic performance, educational data mining, prediction algorithm, research quality, replication, reporting quality, educational outcomes
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