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Predicting academic performance: a systematic literature review
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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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Implicit Heterogeneous Features Embedding in Deep Knowledge Tracing
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Early Prediction of Student Profiles Based on Performance and Gaming Preferences
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The Experience and Persistence of College Students in STEM Majors
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Generating actionable predictive models of academic performance
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Modelling Engineering Student Academic Performance Using Academic Analytics
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Ensembling predictions of student knowledge within intelligent tutoring systems
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New CS1 pedagogies and curriculum, the same success factors?
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Modeling student retention in science and engineering disciplines using neural networks
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Neural Network and Linear Regression methods for prediction of students' academic achievement
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Predicting students' performance based on learning style by using artificial neural networks
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Can computational thinking predict academic performance?
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Achievement Goals in CS1: Replication and Extension
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Class result prediction using machine learning
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Extracurricular Activities as Dropout Prediction Factors in Higher Education Using Decision Trees
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Models for Early Identification of Struggling Novice Programmers
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Predicting student success based on prior performance
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Can Interaction Patterns with Supplemental Study Tools Predict Outcomes in CS1?
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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