Personalized Stability Triggers: A Model-Agnostic Framework for Adaptive Early Prediction of At-Risk Students in Education

Qixiang Pang, Linrui Zhang, Shan Du
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:940-945, 2026.

Abstract

Early prediction of student performance and timely intervention are important in education. A fundamental challenge is the trade-off between temporal earliness (intervening sooner) and predictive reliability (waiting for sufficient data). Conventional machine learning models typically impose static observation windows that do not account for the heterogeneous behaviors of individual students. In this paper, we propose the Personalized Stability Trigger (PST), a novel dynamic framework that identifies the optimal inference moment for each student based on the stochastic convergence of model confidence. By leveraging the ensemble variance of a Random Forest estimator, PST detects an "information plateau" where further data collection yields marginal information gain. We validate this framework on two disparate educational datasets: ASSISTments (micro-scale problem solving) and OULAD (macro-scale course engagement). Experimental results demonstrate that PST reduces observation latency by up to 20% compared to fixed-window baselines while preserving >99.5% of prediction accuracy. These findings indicate that stability-driven triggers offer a scalable, robust, and model-agnostic solution for early prediction of at-risk students.

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-pang26a, title = {Personalized Stability Triggers: A Model-Agnostic Framework for Adaptive Early Prediction of At-Risk Students in Education}, author = {Pang, Qixiang and Zhang, Linrui and Du, Shan}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {940--945}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/pang26a/pang26a.pdf}, url = {https://proceedings.mlr.press/v318/pang26a.html}, abstract = {Early prediction of student performance and timely intervention are important in education. A fundamental challenge is the trade-off between temporal earliness (intervening sooner) and predictive reliability (waiting for sufficient data). Conventional machine learning models typically impose static observation windows that do not account for the heterogeneous behaviors of individual students. In this paper, we propose the Personalized Stability Trigger (PST), a novel dynamic framework that identifies the optimal inference moment for each student based on the stochastic convergence of model confidence. By leveraging the ensemble variance of a Random Forest estimator, PST detects an "information plateau" where further data collection yields marginal information gain. We validate this framework on two disparate educational datasets: ASSISTments (micro-scale problem solving) and OULAD (macro-scale course engagement). Experimental results demonstrate that PST reduces observation latency by up to 20% compared to fixed-window baselines while preserving >99.5% of prediction accuracy. These findings indicate that stability-driven triggers offer a scalable, robust, and model-agnostic solution for early prediction of at-risk students.} }
Endnote
%0 Conference Paper %T Personalized Stability Triggers: A Model-Agnostic Framework for Adaptive Early Prediction of At-Risk Students in Education %A Qixiang Pang %A Linrui Zhang %A Shan Du %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-pang26a %I PMLR %P 940--945 %U https://proceedings.mlr.press/v318/pang26a.html %V 318 %X Early prediction of student performance and timely intervention are important in education. A fundamental challenge is the trade-off between temporal earliness (intervening sooner) and predictive reliability (waiting for sufficient data). Conventional machine learning models typically impose static observation windows that do not account for the heterogeneous behaviors of individual students. In this paper, we propose the Personalized Stability Trigger (PST), a novel dynamic framework that identifies the optimal inference moment for each student based on the stochastic convergence of model confidence. By leveraging the ensemble variance of a Random Forest estimator, PST detects an "information plateau" where further data collection yields marginal information gain. We validate this framework on two disparate educational datasets: ASSISTments (micro-scale problem solving) and OULAD (macro-scale course engagement). Experimental results demonstrate that PST reduces observation latency by up to 20% compared to fixed-window baselines while preserving >99.5% of prediction accuracy. These findings indicate that stability-driven triggers offer a scalable, robust, and model-agnostic solution for early prediction of at-risk students.
APA
Pang, Q., Zhang, L. & Du, S.. (2026). Personalized Stability Triggers: A Model-Agnostic Framework for Adaptive Early Prediction of At-Risk Students in Education. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:940-945 Available from https://proceedings.mlr.press/v318/pang26a.html.

Related Material