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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Items 401–437 of 437
DeMonbrun, R., & Brown, M. (2018) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--28342 · Cited by 4
Predicting the performance of school: Case study in Sultanate of Oman
Almuniri, I., & Said, A. M. (2018) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/infoct.2018.8356833 · Cited by 4
Elias, E. M., & Lindsay, C. A. (1968) · Cited by 3
Ibrahim, N., Freeman, S. A., & Shelley, M. (2011) · International Journal of Adult Vocational Education and Technology · doi:10.4018/javet.2011100101 · Cited by 3
Gray, G., McGuinness, C., & Owende, P. M. O. (2013) · Educational Data Mining · Cited by 3
Efficient Graph Clustering Algorithm and its use in Prediction of Students Performance
Dhanpal, J., Perumal, T., & Udhayakumaran (2016) · doi:10.1145/2980258.2980343 · Cited by 3
Ho, C.-L., & Raubenheimer, D. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--17647 · Cited by 3
Haque, M. E. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--21256 · Cited by 3
Aggarwal, H. W., Bermel, P. A., Hicks, N., Douglas, K. A., Diefes‐Dux, H. A., & Madhavan, K. P. C. (2017) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2017.8190623 · Cited by 3
Investigating performance in a blended SPOC
Wan, H., Jun, D., Liu, K., & Xiaopeng, G. (2017) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale.2017.8252340 · Cited by 3
Mining Intelligence and Knowledge Exploration
(2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66187-8 · Cited by 3
Student Achievement Databases Assist Teaching Improvement
Xin-hui, W., Junping, Y., & Qin, C. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-28655-1_33 · Cited by 2
Aziz, F., Jusoh, A. W., & Abu, M. S. (2015) · AIP conference proceedings · doi:10.1063/1.4915667 · Cited by 2
Result prediction system for Multi-Tenant database of University
Tiwari, A. K., Pandey, A., Rohatgi, D., & Singh, A. K. (2013) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2013.6514423 · Cited by 2
Performance of engineering undergraduate students in mathematics: A case study in UniMAP
Saad, S. A., Azziz, N. H. A., Zakaria, S. A., & Yazid, N. M. (2015) · AIP conference proceedings · doi:10.1063/1.4937105 · Cited by 2
Exploring students' learning attributes in consecutive lessons to improve prediction performance
Sorour, S. E., & Mine, T. (2016) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/2843043.2843066 · Cited by 2
White, S. D., May, S., & Washburn, M. F. (1917) · The American Journal of Psychology · doi:10.2307/1412944 · Cited by 2
Montague, M., Reynolds, M. M., & Washburn, M. F. (1918) · The American Journal of Psychology · doi:10.2307/1414123 · Cited by 2
Kim, S., Kim, J. W., Park, J., & Oh, A. H. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2893406 · Cited by 2
Using a Lexical Analysis of Student's Self-Explanation to Predict Course Performance.
Rhodes, N. P., Ung, M. H., Zundel, A., Herold, J., & Stahovich, T. F. (2013) · Cited by 2
Prediction of Student Success Using Enrollment Data.
Cengiz, N., & Uka, A. (2014) · Cited by 2
(2016) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2016.06.136 · Cited by 2
Investigating Swarm Intelligence for Performance Prediction.
al‐Rifaie, M. M., Yee-King, M. J., & d’Inverno, M. (2016) · Goldsmiths (University of London) · Cited by 2
Effect of Freshman Chemistry on Student Performance in Sophomore Engineering Courses
Collura, M. A., Ciston, S., & Savage, N. O. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--17812 · Cited by 2
Retention: Quantifying the Apples and Oranges
Wolff, T., Cramer, S. C., & Masi, B. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--18500 · Cited by 2
Mosier, R. D., Reck, J., Yates, H. N., & Ramming, C. H. (2018) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--28839 · Cited by 2
Difficulty in Predicting Performance in a Project-Based Learning Program
Chan, L., Sleezer, R., Swanson, J., Ahrens, M., & Bates, R. A. (2018) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--28179 · Cited by 2
Rao, A. R. (2018) · IEEE Integrated STEM Education Conference (ISEC) · doi:10.1109/isecon.2018.8340466 · Cited by 2
Übi, J., Liiv, I., Übi, E., & Võhandu, L. (2013) · International Conference on E-Learning and E-Technologies in Education · doi:10.1109/icelete.2013.6644357 · Cited by 1
Application Of Emerging Knowledge Discovery Methods In Engineering Education
Tsalatsanis, A., Yalçin, A., & Kaw, A. K. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--5472 · Cited by 1
Academic "Predestination": Does It Exist?
Dacunto, P. (2016) · ASEE Annual Conference & Exposition · doi:10.18260/p.26488 · Cited by 1
Research in Engineering Education Symposium, REES 2013
Arsat, D. (2013) · Cited by 1
The Engineering Classroom is Still Relevant
Fitzsimmons, E. J., Kulesza, S., Li, X., Jeter, W. K., & Fallin, J. R. (2016) · ASEE Annual Conference & Exposition · doi:10.18260/p.26152 · Cited by 1
The Crucial Role Of Engineering Faculty On Student Performance
Vogt, C. M. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--1270 · Cited by 1
Experience does not Predict Performance: The Case of the Students' Academic Levels.
Dieste, Ó., Fonseca, R., Raura, G., & Ponce, A. (2017) · Conferencia Iberoamericana de Software Engineering · Cited by 1
Ye, N., Fok, T. Y., Wang, X., Collofello, J., & Dickson, N. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.03.085 · Cited by 1
Flagged first, then by citations
2 of the 439 references couldn’t be matched to a known paper.
Research works printed but not linked: 14
- L. Barba-Guamán and P. Valdiviezo-Díaz . 2017. Improve the performance of students in the mathematics learning through Bayesian model . 7th International Workshop on Computer Science and Engineering (WCSE) ( 2017 ), 349–354. L. Barba-Guamán and P. Valdiviezo-Díaz. 2017. Improve the performance of students in the mathematics learning through Bayesian model. 7th International Workshop on Computer Science and Engineering (WCSE) (2017), 349–354.
- 10.5555/2684939.2684975 · DOI
- Saylisse Dávila , Wandaliz Torres-García , and Viviana I Cesaní . 2015 . Mining the profile of successful IE students: Using historical data to drive curricular interventions . In IISE Annual Conference. Institute of Industrial and Systems Engineers (IISE), 2892 . Saylisse Dávila, Wandaliz Torres-García, and Viviana I Cesaní. 2015. Mining the profile of successful IE students: Using historical data to drive curricular interventions. In IISE Annual Conference. Institute of Industrial and Systems Engineers (IISE), 2892.
- José R Deliz , Rolando García Juan C Morales , and Gloribel Rivera . 2015 . Markov chain modeling: student flow through remedial Mathematics to sophomore ISERC . In IISE Annual Conference. Institute of Industrial and Systems Engineers (IISE), 3171 . José R Deliz, Rolando García Juan C Morales, and Gloribel Rivera. 2015. Markov chain modeling: student flow through remedial Mathematics to sophomore ISERC. In IISE Annual Conference. Institute of Industrial and Systems Engineers (IISE), 3171.
- A. Dinesh Kumar , R. Pandi Selvam , and K. Sathesh Kumar . 2018 . Review on prediction algorithms in educational data mining . International Journal of Pure and Applied Mathematics 118, Special Issue 8 (2018), 531 – 536 . A. Dinesh Kumar, R. Pandi Selvam, and K. Sathesh Kumar. 2018. Review on prediction algorithms in educational data mining. International Journal of Pure and Applied Mathematics 118, Special Issue 8 (2018), 531–536.
- M. Han , M. Tong , M. Chen , J. Liu , and C. Liu . 2017 . Application of Ensemble Algorithm in Students’ Performance Prediction. 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) ( 2017 ), 735–740. M. Han, M. Tong, M. Chen, J. Liu, and C. Liu. 2017. Application of Ensemble Algorithm in Students’ Performance Prediction. 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (2017), 735–740.
- Barbara Ann Kitchenham , David Budgen , and Pearl Brereton . 2015. Evidencebased software engineering and systematic reviews . Vol. 4 . CRC Press . Barbara Ann Kitchenham, David Budgen, and Pearl Brereton. 2015. Evidencebased software engineering and systematic reviews. Vol. 4. CRC Press.
- 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
- D.R. Raman and A.L. Kaleita . 2017. Enhancing student success by combining preenrollment risk prediction with academic analytics data . ASEE Annual Conference & Exposition ( 2017 ). D.R. Raman and A.L. Kaleita. 2017. Enhancing student success by combining preenrollment risk prediction with academic analytics data. ASEE Annual Conference & Exposition (2017).
- William E Sadler . 1997. Factors Affecting Retention Behavior: A Model To Predict At-Risk Students . In 37th Annual Forum of the Association for Institutional Research . AIR , Orlando, FL , 23. William E Sadler. 1997. Factors Affecting Retention Behavior: A Model To Predict At-Risk Students. In 37th Annual Forum of the Association for Institutional Research. AIR, Orlando, FL, 23.
- E. Sugiharti , S. Firmansyah , and F.R. Devi . 2017 . Predictive evaluation of performance of computer science students of unnes using data mining based on naÏve bayes classifier (NBC) algorithm . Journal of Theoretical and Applied Information Technology 95 , 4 (2017), 902 – 911 . E. Sugiharti, S. Firmansyah, and F.R. Devi. 2017. Predictive evaluation of performance of computer science students of unnes using data mining based on naÏve bayes classifier (NBC) algorithm. Journal of Theoretical and Applied Information Technology 95, 4 (2017), 902–911.
- 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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
