Abstract
This study applies a learning analytics approach and explainable models to identify the most difficult content areas in university mathematics based on interaction records from a practice and assessment platform. A secondary quantitative analysis of the MathE dataset was conducted, considering content variables (topic, subtopic, and keywords) and contextual variables (country and level). First, error rates were estimated by topic and subtopic, and patterns were synthesized through comparative visualizations. Then, complementary models were trained: a logistic regression model for its interpretability and a nonlinear gradient-boosted tree model to capture interactions, validating generalization through student-level partitioning. Explainability was addressed through contribution attribution to interpret factors associated with errors. The findings indicate greater difficulties in Differentiation, Functional Interpretation, and Probability, together with cross-cutting weaknesses in algebraic manipulation, with additional support needs in Numerical Methods and Integration.
References
[2] G. J. Hwang and Y. F. Tu, “Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review,” Mathematics 2021, Vol. 9, vol. 9, no. 6, Mar. 2021, doi: 10.3390/MATH9060584.
[3] H. Khosravi et al., “Explainable Artificial Intelligence in education,” Computers and Education: Artificial Intelligence, vol. 3, p. 100074, Jan. 2022, doi: 10.1016/J.CAEAI.2022.100074.
[4] B. F. Azevedo, M. F. Pacheco, F. P. Fernandes, and A. I. Pereira, “Dataset of mathematics learning and assessment of higher education students using the MathE platform,” Data Brief, vol. 53, p. 110236, Apr. 2024, doi: 10.1016/J.DIB.2024.110236.
[5] P. D. Long and G. Siemens, “Penetrare la nebbia: tecniche di analisi per l’apprendimento,” Revista Italiana de Tecnología Educativa, vol. 22, no. 3, pp. 132–137, Dec. 2014, doi: 10.17471/2499-4324/195.
[6] D. J. Lemay, C. Baek, and T. Doleck, “Comparison of learning analytics and educational data mining: A topic modeling approach,” Computers and Education: Artificial Intelligence, vol. 2, p. 100016, Jan. 2021, doi: 10.1016/J.CAEAI.2021.100016.
[7] A. Zeileis, “Examining Exams Using Rasch Models and Assessment of Measurement Invariance,” Austrian Journal of Statistics, vol. 54, no. 3, pp. 9–26, Sep. 2024, doi: 10.17713/ajs.v54i3.2055.
[8] Z. Zhang, “Model building strategy for logistic regression: purposeful selection,” Ann. Transl. Med., vol. 4, no. 6, p. 111, Mar. 2016, doi: 10.21037/ATM.2016.02.15.
[9] S. M. Lundberg and S. I. Lee, “A Unified Approach to Interpreting Model Predictions,” Adv. Neural Inf. Process. Syst., vol. 2017-December, pp. 4766–4775, May 2017, Accessed: Jan. 20, 2026. [Online]. Available: https://arxiv.org/pdf/1705.07874
[10] N. Bergdahl, M. Bond, J. Sjöberg, M. Dougherty, and E. Oxley, “Unpacking student engagement in higher education learning analytics: a systematic review,” International Journal of Educational Technology in Higher Education, vol. 21, no. 1, pp. 63-, Dec. 2024, doi: 10.1186/S41239-024-00493-Y/TABLES/6.
[11] D. Ifenthaler, · Jane, Y.-K. Yau, D. Ifenthaler, and Y.-K. Yau, “Utilising learning analytics to support study success in higher education: a systematic review,” Educational Technology Research and Development 2020 68:4, vol. 68, no. 4, pp. 1961–1990, Jun. 2020, doi: 10.1007/S11423-020-09788-Z.
[12] L. Paulsen and E. Lindsay, “Learning analytics dashboards are increasingly becoming about learning and not just analytics - A systematic review,” Education and Information Technologies 2024 29:11, vol. 29, no. 11, pp. 14279–14308, Jan. 2024, doi: 10.1007/S10639-023-12401-4.
[13] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016, pp. 785–794, Aug. 2016, doi: 10.1145/2939672.2939785.
[14] S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nature Machine Intelligence 2020 2:1, vol. 2, no. 1, pp. 56–67, Jan. 2020, doi: 10.1038/s42256-019-0138-9.
[15] A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, Jun. 2020, doi: 10.1016/J.INFFUS.2019.12.012.
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