# Toward Cyclic A.I. Modelling of Self-Regulated Learning: A Case Study with E-Learning Trace Data

arXiv: https://arxiv.org/abs/2507.02913 · HTML: https://mlsanity.com/paper/2507.02913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Algorithms to estimate Shapley value feature attributions](https://mlsanity.com/paper/2207.07605/) (2022)

## Abstract (arXiv)

Many e-learning platforms assert their ability or potential to improve students' self-regulated learning (SRL), however the cyclical and undirected nature of SRL theoretical models represent significant challenges for representation within contemporary machine learning frameworks. We apply SRL-informed features to trace data in order to advance modelling of students' SRL activities, to improve predictability and explainability regarding the causal effects of learning in an eLearning environment. We demonstrate that these features improve predictive accuracy and validate the value of further research into cyclic modelling techniques for SRL.