Predicting Student Exam Performance Using Explainable Regression Models: A Learning Analytics Study
DOI:
https://doi.org/10.29207/joseit.v3i3.8471Keywords:
learning analytics, student performance prediction, SHAP, random forest, explainable AIAbstract
Identifying which behavioral and demographic factors most strongly predict student academic performance is a long-standing goal of learning analytics. We apply linear regression, ridge regression, random forest, and gradient boosting to a 1,000-student behavioral and demographic dataset. Five-fold cross-validated R² shows that linear and ridge regression (R²=0.656) explain more variance than the ensemble methods (R²≈0.57), indicating a primarily additive structure in the data. To test this additive structure for residual non-linear or interaction effects and to obtain individual-level explanations, we apply SHapley Additive exPlanations (SHAP) to the random forest model via TreeExplainer, and cross-validate the resulting feature ranking against the standardized coefficients of the top-performing linear model. SHAP analysis identifies study time as the dominant predictor, with a mean |SHAP| of 4.93 points (approximately 4.1 exam-score points per additional study hour), followed by prior academic performance (mean |SHAP|=3.44) and attendance (mean |SHAP|=2.38). Internet access and part-time employment emerge as statistically meaningful binary factors (mean score gap: +5.0 and −3.3 points respectively), consistent with literature on the digital divide and academic workload. Parental education, gender, and extracurricular participation contribute negligible SHAP values (mean |SHAP| ≤0.16), a pattern that also holds for the corresponding linear coefficients. These results suggest that actionable behavioral variables – particularly study time and attendance – account for the bulk of explainable variance in examination outcomes, while fixed demographic characteristics contribute little incremental predictive power.
References
S. Batool, J. Rashid, M. W. Nisar, J. Kim, H.-Y. Kwon, and A. Hussain, “Educational data mining to predict students’ academic performance: A survey study,” Education and Information Technologies, vol. 28, no. 1, pp. 905–971, 2023.
Y. Chen and L. Zhai, “A comparative study on student performance prediction using machine learning,” Education and Information Technologies, vol. 28, no. 9, pp. 12039–12057, 2023.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30, 2017, pp. 4765–4774.
L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
C. Huang, “Meta-analysis of the relation between study time and academic achievement,” in Proc. IAFOR Intl. Conf. on Education, 2015.
M. Theobald, “Study longer or study effectively? Better study strategies can compensate for less study time and predict goal achievement and lower negative affect,” British Journal of Educational Psychology, vol. 95, no. 1, 2025.
G. Siemens and P. Long, “Distilling the essence of learning analytics,” International Journal of Distance Education Technologies, vol. 9, no. 2, pp. 57–68, 2011.
G. L. Pierce and P. F. Cleary, “The persistent educational digital divide and its impact on societal inequality,” PLOS ONE, vol. 19, no. 4, 2024, doi: 10.1371/journal.pone.0286795.
H. Patil, “Student Performance & Study Habits Dataset,” Kaggle, 2024. [Online]. Available: https://www.kaggle.com/datasets/harshadapatil31/student-performance-and-study-habits-dataset
C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601–618, 2010, doi: 10.1109/TSMCC.2010.2053532.
B. Albreiki, N. Zaki, and H. Alashwal, “A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques,” Education Sciences, vol. 11, no. 9, p. 552, 2021, doi: 10.3390/educsci11090552.
P. Balaji, S. Alelyani, A. Qahmash, and M. Mohana, “Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review,” Applied Sciences, vol. 11, no. 21, p. 10007, 2021, doi: 10.3390/app112110007.
G. Akçapınar, A. Altun, and P. Aşkar, “Using learning analytics to develop early-warning system for at-risk students,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 1–20, 2019, doi: 10.1186/s41239-019-0172-z.
A. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52138–52160, 2018, doi: 10.1109/ACCESS.2018.2870052.
M. T. Ribeiro, S. Singh, and C. Guestrin, ““Why Should I Trust You?”: Explaining the Predictions of Any Classifier,” in Proc. 22nd ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144, doi: 10.1145/2939672.2939778.
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee, “From local explanations to global understanding with explainable AI for trees,” Nature Machine Intelligence, vol. 2, no. 1, pp. 56–67, 2020, doi: 10.1038/s42256-019-0138-9.
M. Credé, S. G. Roch, and U. M. Kieszczynka, “Class Attendance in College: A Meta-Analytic Review of the Relationship of Class Attendance With Grades and Student Characteristics,” Review of Educational Research, vol. 80, no. 2, pp. 272–295, 2010, doi: 10.3102/0034654310362998.
K. Okano, J. R. Kaczmarzyk, N. Dave, J. D. E. Gabrieli, and J. C. Grossman, “Sleep quality, duration, and consistency are associated with better academic performance in college students,” npj Science of Learning, vol. 4, p. 16, 2019, doi: 10.1038/s41539-019-0055-z.
J. L. Vigdor, H. F. Ladd, and E. Martinez, “Scaling the digital divide: Home computer technology and student achievement,” Economic Inquiry, vol. 52, no. 3, pp. 1103–1119, 2014, doi: 10.1111/ecin.12089.
C. Callender, “The impact of term-time employment on higher education students’ academic attainment and achievement,” Journal of Education Policy, vol. 23, no. 4, pp. 359–377, 2008, doi: 10.1080/02680930801924490.
C. Y. Tan, M. Lyu, and B. Peng, “Academic Benefits from Parental Involvement are Stratified by Parental Socioeconomic Status: A Meta-analysis,” Parenting: Science and Practice, vol. 20, no. 4, pp. 241–287, 2020, doi: 10.1080/15295192.2019.1694836.
D. Voyer and S. D. Voyer, “Gender differences in scholastic achievement: A meta-analysis,” Psychological Bulletin, vol. 140, no. 4, pp. 1174–1204, 2014, doi: 10.1037/a0036620.
Á. Balaguer, E. Benítez, A. Albertos, and S. Lara, “Not everything helps the same for everyone: relevance of extracurricular activities for academic achievement,” Humanities and Social Sciences Communications, vol. 7, p. 79, 2020, doi: 10.1057/s41599-020-00573-0.
R. H. H. Groenwold, I. R. White, A. R. T. Donders, J. R. Carpenter, D. G. Altman, and K. G. M. Moons, “Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis,” CMAJ, vol. 184, no. 11, pp. 1265–1269, 2012, doi: 10.1503/cmaj.110977.
A. E. Hoerl and R. W. Kennard, “Ridge Regression: Biased Estimation for Nonorthogonal Problems,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970, doi: 10.1080/00401706.1970.10488634.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Systems Engineering and Information Technology (JOSEIT)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
