Citation :

Meenakshi Devi, Rakesh Kumar, "An Integrated Framework for Fair and Interpretable Predictive Modeling in Educational Analytics," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 10, pp. 128-137, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I10P110

Abstract

As learning analytics becomes more widely used in education, there is a need to develop predictive models that account for a range of real-world factors. However, existing predictive systems emphasize model performance more than the design of transparent, trustworthy models. This study combines model performance, fairness, and interpretability in a single pipeline. The integrated predictive framework evaluates five classification models under stratified cross-validation on the Open University Learning Analytics Dataset (OULAD). Two data files are selected from the dataset for model development. The processed data comprises 32,593 merged records with demographic and behavioral features of the learners. SVM-RBF and gradient boosting classifiers show similar, high predictive performance in mean Accuracy, AUC, and F1-score. Fairness is assessed using gender as the sensitive attribute. Random forest shows the lowest group disparities, with a mean Demographic Parity Difference (DPD) of 0.1147 and an Equalized Odds Difference (EOD) of 0.1610. Statistical analysis further identifies significant classifier differences in all metrics except DPD; however, pairwise tests show no significant difference for any metric. SHAP provides feature importance plots for model interpretation based on the sensitive attribute. They show that the behavioral features contribute more to predictions than demographics and primarily drive model decisions. Finally, the study offers practical guidance for developing transparent and fair educational AI systems.

Keywords

Educational analytics, Predictive modeling, Fairness in machine learning, Model interpretability, SHAP, Cross-validation.

References

1. Marcela Hernández-de-Menéndez et al., “Learning Analytics: State of the Art,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 16, no. 3, pp. 1209-1230, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
2. René F. Kizilcec, and Hansol Lee, Algorithmic Fairness in Education, 1^st ed., The Ethics of Artificial Intelligence in Education, Routledge, pp. 174-202, 2022.
   [Google Scholar] [Publisher Link]
3. Krzysztof Fiok et al., “Explainable Artificial Intelligence for Education and Training,” The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, vol. 19, no. 2, pp. 133-144, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Hassan Khosravi et al., “Explainable Artificial Intelligence in Education,” Computers and Education: Artificial Intelligence, vol. 3, pp. 1-22, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Ali Baniasadi et al., “Fairness in Classroom Assessment: A Systematic Review,” The Asia-Pacific Education Researcher, vol. 32, no. 1, pp. 91-109, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Bahar Memarian, and Tenzin Doleck, “Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and Higher Education: A Systematic Review,” Computers and Education: Artificial Intelligence, vol. 5, pp. 1-12, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Fan Ouyang et al., “Integration of Artificial Intelligence Performance Prediction and Learning Analytics to Improve Student Learning in Online Engineering Course,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, pp. 1-23, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Nabila Sghir, Amina Adadi, and Mohammed Lahmer, “Recent Advances in Predictive Learning Analytics: A Decade Systematic Review (2012-2022),” Education and Information Technologies, vol. 28, no. 7, pp. 8299-8333, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Ashwin Rachha, and Mohammed Seyam, “Explainable AI in Education: Current Trends, Challenges, and Opportunities,” SoutheastCon 2023, Orlando, FL, USA, pp. 232-239, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Gomathy Ramaswami et al., “Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies,” Technology, Knowledge and Learning, vol. 28, no. 3, pp. 959-980, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Yudish Teshal Badal, and Roopesh Kevin Sungkur, “Predictive Modelling and Analytics of Students’ Grades using Machine Learning Algorithms,” Education and Information Technologies, vol. 28, no. 3, pp. 3027-3057, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Pratiyush Guleria, and Manu Sood, “Explainable AI and Machine Learning: Performance Evaluation and Explainability of Classifiers on Educational data Mining Inspired Career Counseling,” Education and Information Technologies, vol. 28, no. 1, pp. 1081-1116, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Leonard Chukwualuka Nnadi et al., “Prediction of Students’ Adaptability using Explainable AI in Educational Machine Learning Models,” Applied Sciences, vol. 14, no. 12, pp. 1-26, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Fangyuan Chai et al., “Grading by AI Makes me Feel Fairer? How Different Evaluators Affect College Students’ Perception of Fairness,” Frontiers in Psychology, vol. 15, pp. 1-18, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Michael Sailer et al., “The End is the Beginning is the End: The Closed-Loop Learning Analytics Framework,” Computers in Human Behavior, vol. 158, pp. 1-19, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Qianhui Liu, Juan D. Pinto, and Luc Paquette, Applications of Explainable AI (XAI) in Education, Trust and Inclusion in AI-Mediated Education, Springer, pp. 93-109, 2024. 
    [CrossRef] [Google Scholar] [Publisher Link]
17. Mutlu Cukurova, “The Interplay of Learning, Analytics and Artificial Intelligence in Education: A Vision for Hybrid Intelligence,” British Journal of Educational Technology, vol. 56, no. 2, pp. 469-488, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Samuel Abiodun Oyedotun, Otega Prosper Ejenarhome, and Godfrey Perfectson Oise, “Learning Analytics and Predictive Modeling: Enhancing Student Success through Data-Driven Insights,” Journal of Science Research and Reviews, vol. 2, no. 3, pp. 42-51, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Sachini Gunasekara, and Mirka Saarela, “Explainable AI in Education: Techniques and Qualitative Assessment,” Applied Sciences, vol. 15, no. 3, pp. 1-16, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
20. George Raftopoulos, Gregory Davrazos, and Sotiris Kotsiantis, “Evaluating Fairness Strategies in Educational Data Mining: A Comparative Study of Bias Mitigation Techniques,” Electronics, vol. 14, no. 9, pp. 1-24, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Kadir Kesgin et al., “Beyond Performance: Explaining and Ensuring Fairness in Student Academic Performance Prediction with Machine Learning,” Applied Sciences, vol. 15, no. 15, pp. 1-25, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Sachini Gunasekara, and Mirka Saarela, “Trade-offs between Fairness and Performance in Educational AI: Analyzing Post-Processing Bias Mitigation on the OULAD,” Information and Software Technology, vol. 189, pp. 1-10, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Jakub Kuzilek, Martin Hlosta, and Zdenek Zdrahal, “Open University Learning Analytics Dataset,” Scientific Data, vol. 4, no. 1, pp. 1-8, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]