# Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring

arXiv: https://arxiv.org/abs/2508.01105 · HTML: https://mlsanity.com/paper/2508.01105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review](https://mlsanity.com/paper/2209.15137/) (2022)
- [Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices](https://mlsanity.com/paper/2209.15146/) (2022)
- [Machine Learning Algorithms for Detecting Mental Stress in College Students](https://mlsanity.com/paper/2412.07415/) (2024)
- [Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings](https://mlsanity.com/paper/2401.05367/) (2023)

## Built on by (most cited first, 1 of 1)

- [An AI-Powered Culturally Aware Chatbot for Stress Detection and Wellness Support among Pakistani University Students Using NLP and Machine Learning](https://mlsanity.com/paper/2609.11199/) (2026)

## Abstract (arXiv)

Student mental health is an increasing concern in academic institutions, where stress can severely impact well-being and academic performance. Traditional assessment methods rely on subjective surveys and periodic evaluations, offering limited value for timely intervention. This paper introduces a context-aware machine learning framework for classifying student stress using two complementary survey-based datasets covering psychological, academic, environmental, and social factors. The framework follows a six-stage pipeline involving preprocessing, feature selection (SelectKBest, RFECV), dimensionality reduction (PCA), and training with six base classifiers: SVM, Random Forest, Gradient Boosting, XGBoost, AdaBoost, and Bagging. To enhance performance, we implement ensemble strategies, including hard voting, soft voting, weighted voting, and stacking. Our best models achieve 93.09% accuracy with weighted hard voting on the Student Stress Factors dataset and 99.53% with stacking on the Stress and Well-being dataset, surpassing previous benchmarks. These results highlight the potential of context-integrated, data-driven systems for early stress detection and underscore their applicability in real-world academic settings to support student well-being.