用多维度数据构建智能系统，提前识别学生压力状态。
Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
- 融合心理、学业、环境等多因素数据，构建上下文感知模型。
- 最佳模型准确率达99.53%，显著超越已有基准。
- 适合高校心理健康中心用于早期干预与个性化支持。
学生心理健康已成为学术机构日益关注的问题，压力会严重损害身心健康与学业表现。传统评估依赖主观问卷和周期性测评，难以实现及时干预。本文提出一种上下文感知的机器学习框架，利用两个互补的基于调查的数据集（涵盖心理、学业、环境与社交因素）对学生的压力状态进行分类。框架采用六阶段流程：预处理、特征选择（SelectKBest、RFECV）、降维（PCA），并训练六种基础分类器（SVM、随机森林、梯度提升、XGBoost、AdaBoost、Bagging）。通过硬投票、软投票、加权投票与堆叠等集成策略提升性能。在学生压力因素数据集上，加权硬投票模型达到93.09%准确率；在压力与福祉数据集上，堆叠模型达99.53%准确率，显著优于现有基准。结果表明，融合上下文信息的数据驱动系统具有早期压力检测潜力，适用于真实校园环境以支持学生福祉。
原文摘要 · Abstract (English)
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.
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