The Dependency Divide: An Interpretable Machine Learning Framework for Profiling Student Digital Satisfaction in the Bangladesh Context
MMM Fahim, H Ankona, MM Huq, MR Karim
arXiv preprint · 2026 · preprint · arXiv:2601.01231
TL;DR
High digital engagement makes students more vulnerable to infrastructure failures, not less — the 'Dependency Divide'. Targeted reliability improvements for heavy users yield 2× the return of blanket interventions.
Abstract
While digital access has expanded rapidly in resource-constrained contexts, satisfaction with digital learning platforms varies significantly among students with seemingly equal connectivity. This study introduces the 'Dependency Divide', a novel framework proposing that highly engaged students become conditionally vulnerable to infrastructure failures, challenging assumptions that engagement uniformly benefits learners in post-access environments. Using a cross-sectional study of 396 university students in Bangladesh, we apply K-prototypes clustering, profile-specific Random Forest models with SHAP and ALE analysis, and formal interaction analysis with propensity score matching. Three profiles emerged: Casually Engaged (58%), Efficient Learners (35%), and Hyper-Engaged (7%). A significant interaction between educational device time and internet reliability (β = 0.033, p = 0.028) confirmed the Dependency Divide: engagement increased satisfaction only when infrastructure remained reliable. Policy simulations demonstrated targeted reliability improvements for high-dependency users yielded 2.06× greater returns than uniform interventions.
BibTeX
@article{fahim2026the,
title = {The Dependency Divide: An Interpretable Machine Learning Framework for Profiling Student Digital Satisfaction in the Bangladesh Context},
author = {MMM Fahim and H Ankona and MM Huq and MR Karim},
year = {2026},
journal = {arXiv preprint},
eprint = {2601.01231},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2601.01231},
}