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

• URL: http://arxiv.org/abs/2508.01105v1
• Date: Fri, 01 Aug 2025 22:52:25 GMT
• Title: Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
• Authors: Md Sultanul Islam Ovi, Jamal Hossain, Md Raihan Alam Rahi, Fatema Akter,
• Abstract summary: Student mental health is an increasing concern in academic institutions.<n>Traditional assessment methods rely on subjective surveys and periodic evaluations.<n>This paper introduces a context-aware machine learning framework for classifying student stress.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Test-time Offline Reinforcement Learning on Goal-related Experience [50.94457794664909]
  Research in foundation models has shown that performance can be substantially improved through test-time training.<n>We propose a novel self-supervised data selection criterion, which selects transitions from an offline dataset according to their relevance to the current state.<n>Our goal-conditioned test-time training (GC-TTT) algorithm applies this routine in a receding-horizon fashion during evaluation, adapting the policy to the current trajectory as it is being rolled out.
  arXiv  Detail & Related papers  (2025-07-24T21:11:39Z)
• Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data [0.0]
  This study introduces a dataset comprising physiological signals, electrodermal activity, heart rate and skin temperature.<n>To overcome these challenges, the dataset underwent preprocessing with the Synthetic Minority Over sampling Technique (SMOTE)<n>Advanced machine learning models including Random Forest, XGBoost and a Multi-Layer Perceptron (MLP) were evaluated and combined into a Stacking to leverage their collective predictive strengths.
  arXiv  Detail & Related papers  (2025-07-10T09:47:56Z)
• Improving Question Embeddings with Cognitive Representation Optimization for Knowledge Tracing [77.14348157016518]
  Research on KT modeling focuses on predicting future student performance based on existing, unupdated records of student learning interactions.<n>We propose a knowledge-tracking cognitive representation optimization (CRO-KT) model that uses dynamic programming algorithms to optimize the structure of cognitive representation.
  arXiv  Detail & Related papers  (2025-04-05T09:32:03Z)
• DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing [51.665582274736785]
  Knowledge Tracing (KT) predicts future performance by students' historical computation, and understanding students' affective states can enhance the effectiveness of KT.<n>We propose Affect Dynamic Knowledge Tracing (DASKT) to explore the impact of various student affective states on their knowledge states.<n>Our research highlights a promising avenue for future studies, focusing on achieving high interpretability and accuracy.
  arXiv  Detail & Related papers  (2025-01-18T10:02:10Z)
• Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach [4.132425356039815]
  This paper introduces a novel context-aware active learning (RL) algorithm for enhanced stress detection using smartwatches and contextual data from smartphones. Our approach dynamically selects optimal times for deploying EMAs, utilizing the user's immediate context to maximize label accuracy and minimize intrusiveness. This research marks a significant move towards personalized, context-driven real-time stress monitoring methods.
  arXiv  Detail & Related papers  (2024-07-11T06:33:11Z)
• Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings [2.650926942973848]
  We present a monitoring system that objectively tracks daily stress levels by utilizing both physiological and contextual data. We propose a three-tier Internet-of-Things-based system architecture to address the challenges.
  arXiv  Detail & Related papers  (2023-12-14T19:16:11Z)
• Detection and Analysis of Stress-Related Posts in Reddit Acamedic Communities [0.0]
  This study focuses on detecting and analyzing stress-related posts in Reddit academic communities. We classify text as stressed or not using natural language processing and machine learning classifiers. Key findings reveal that posts and comments in professors Reddit communities are the most stressful.
  arXiv  Detail & Related papers  (2023-12-02T07:34:03Z)
• Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care [46.2482873419289]
  We introduce a deep Q-learning approach to obtain more reliable critical care policies. We evaluate our method in off-policy and offline settings using simulated environments and real health records from intensive care units.
  arXiv  Detail & Related papers  (2023-06-13T18:02:57Z)
• Personalized Stress Monitoring using Wearable Sensors in Everyday Settings [9.621481727547215]
  We explore objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling.
  arXiv  Detail & Related papers  (2021-07-31T04:15:15Z)
• Online Coreset Selection for Rehearsal-based Continual Learning [65.85595842458882]
  In continual learning, we store a subset of training examples (coreset) to be replayed later to alleviate catastrophic forgetting. We propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting.
  arXiv  Detail & Related papers  (2021-06-02T11:39:25Z)
• An Analysis of Distributed Systems Syllabi With a Focus on Performance-Related Topics [65.86247008403002]
  We analyze a dataset of 51 current ( 2019-2020) Distributed Systems syllabi from top Computer Science programs. We study the scale of the infrastructure mentioned in DS courses, from small client-server systems to cloud-scale, peer-to-peer, global-scale systems.
  arXiv  Detail & Related papers  (2021-03-02T16:49:09Z)
• Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment [61.24399136715106]
  We consider the issue of strategic behaviour in various peer-assessment tasks, including peer grading of exams or homeworks and peer review in hiring or promotions. Our focus is on designing methods for detection of such manipulations. Specifically, we consider a setting in which agents evaluate a subset of their peers and output rankings that are later aggregated to form a final ordering.
  arXiv  Detail & Related papers  (2020-10-08T15:08:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.