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

2025-08-01

AI Summary

Traditional subjective surveys and periodic assessments fail to enable real-time stress monitoring and timely intervention for students. To address this, we propose a context-aware ensemble learning framework. Our method integrates heterogeneous multimodal features—psychological, academic, environmental, and social—and employs a six-stage modeling pipeline incorporating SelectKBest and RFECV for feature selection, PCA for dimensionality reduction, and a hybrid voting-stacking ensemble of SVM, Random Forest, and XGBoost to explicitly model contextual dependencies and enhance generalization. Evaluated on two real-world educational datasets, the framework achieves classification accuracies of 93.09% and 99.53%, significantly outperforming existing baselines. Results demonstrate its efficacy and robustness in identifying students’ stress states and enabling early warning within dynamic academic environments.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Classifying student stress using context-aware machine learning
• Improving accuracy of stress detection with ensemble strategies
• Enabling early intervention for student mental health

Innovation

• Context-aware machine learning for stress classification
• Ensemble strategies enhance model performance significantly
• Six-stage pipeline with preprocessing and feature selection