Explainable multimodal learning analytics for model-based counterfactual decision support using literature-calibrated synthetic data

Predictive learning analytics can identify students who are at risk of failing, however, there is a lack of information to guide advisors on what they should do. This paper proposes an end-to-end synthetic framework using eight structured indicators of Learning Management System (LMS) and an aggregated VADER sentiment. The XGBoost pipeline model had accuracy of 80.7%, precision of 76.5%, recall of 90.3%, an F1 score of 0.828, and an ROC-AUC score of 0.905 at the fixed cut-point of 0.28; logistic regression had the best ROC-AUC score of 0.930.

With sentiment removal, the accuracy of XGBoost dropped to 76.7% and the value of ROC-AUC to 0.875. The constrained greedy counterfactual search screened 149 XGBoost cases separately, dropped 80 cases (53.7\%) below the target of 0.40, and reduced the score by a mean of 33.3% points. Threshold crossing rate for logistic regression and random forest were 100.0% and 41.4% respectively with the same search, highlighting model dependence.

A 27-configuration, training-only sensitivity analysis placed the fixed XGBoost configuration 12th by five-fold cross-validated ROC-AUC (0.929), compared with 0.933 for the best configuration. DiCE-random crossed the target for 74.5% of the same XGBoost-screened cases but required larger normalized changes and approximately four times the runtime of greedy search. These results characterize a reconstructed synthetic model and its search procedures; they do not establish educational validity, causal effectiveness, calibrated institutional risk, least-burden recourse, or deployment readiness.

This work was supported by the Dhofar University Research Grant (Application ID: DU/AY/2025–2026/DURG-8). Department of Information Technology, Dhofar University, Dhofar, Oman Anita Venugopal, Fatima Abdul Munaim Al Rawas & Ali Omar Al Minhali Poornima Institute of Engineering and Technology, Jaipur, Rajasthan, India The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material.

If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Venugopal, A., Al Rawas, F.A.M., Al Minhali, A.O. et al.