An explainable machine learning framework for academic stress classification among university students: a comparative multi-dataset study with ablation analysis and statistical significance testing
- DOI
- 10.3389/fcomp.2026.1886274
- Published
- 2026-07-08
- Container
- Frontiers in Computer Science
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fcomp.2026.1886274,
title = {An explainable machine learning framework for academic stress classification among university students: a comparative multi-dataset study with ablation analysis and statistical significance testing},
author = {Assel Omarbekova and Aizhan Nazyrova and Gulmira Bekmanova and Zhanar Lamasheva and Albina Gibadullina and Assem Abdikarim},
year = {2026},
journal = {Frontiers in Computer Science},
doi = {10.3389/fcomp.2026.1886274},
url = {https://doi.org/10.3389/fcomp.2026.1886274}
}RIS
TY - JOUR TI - An explainable machine learning framework for academic stress classification among university students: a comparative multi-dataset study with ablation analysis and statistical significance testing AU - Assel Omarbekova AU - Aizhan Nazyrova AU - Gulmira Bekmanova AU - Zhanar Lamasheva AU - Albina Gibadullina AU - Assem Abdikarim PY - 2026 JO - Frontiers in Computer Science DO - 10.3389/fcomp.2026.1886274 UR - https://doi.org/10.3389/fcomp.2026.1886274 ER -
APA
Omarbekova, A., Nazyrova, A., Bekmanova, G., Lamasheva, Z., Gibadullina, A., & Abdikarim, A. (2026). An explainable machine learning framework for academic stress classification among university students: a comparative multi-dataset study with ablation analysis and statistical significance testing. Frontiers in Computer Science. https://doi.org/10.3389/fcomp.2026.1886274
Source records
- crossref · retrieved 2026-11-10T12:03:12.558Z