Budget-Friendly and Transparent AI for Forecasting Student Stress in Colleg

Published Date : 04/11/2025 

This research aims to create a cost-effective, survey-based stress classification model using machine learning and Explainable Artificial Intelligence (XAI) to support transparent decision-making in educational settings. 

Student stress in higher education is a significant issue, yet many institutions lack the necessary tools to detect and manage it effectively. Traditional methods often rely on expensive physiological sensors and opaque machine learning models, which can be impractical in resource-limited environments. This research seeks to address these challenges by developing a cost-effective, survey-based stress classification model that leverages multiple machine learning algorithms and Explainable Artificial Intelligence (XAI).

The primary objective of this study is to create a transparent and actionable tool for predicting and managing student stress. By using a survey-based approach, the model aims to be more accessible and scalable compared to methods that require costly equipment. The research draws on a comprehensive dataset of university students to develop and validate the model.

The machine learning pipeline employed in this study involves the use of six classification algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, and XGBoost. These algorithms were chosen for their ability to handle complex data and provide accurate predictions. To ensure the best performance, each algorithm was optimized using grid search and cross-validation for hyperparameter tuning.

Explainable Artificial Intelligence (XAI) plays a crucial role in this research. XAI techniques allow the model's decisions to be interpreted and understood, making it easier for educators and administrators to take actionable steps based on the model's predictions. This transparency is essential for building trust and ensuring that the model's recommendations are implemented effectively.

The dataset used in this study was collected from a diverse group of university students, ensuring that the model can be applied across different educational settings. The data includes various demographic and behavioral variables, such as age, gender, academic performance, and extracurricular activities, which are known to influence stress levels. By analyzing these variables, the model can identify key factors that contribute to student stress and provide insights into potential interventions.

The research findings indicate that the model performs well in classifying stress levels, with high accuracy and interpretability. The use of XAI techniques has also been successful in providing clear explanations for the model's predictions, making it a valuable tool for educational institutions. The results suggest that this approach can be a cost-effective and transparent solution for managing student stress in higher education.

In conclusion, this research demonstrates the potential of combining survey-based data, machine learning, and XAI to develop a practical and effective tool for predicting and managing student stress. By providing transparent and actionable insights, this model can help educational institutions support the well-being of their students more effectively. 

Frequently Asked Questions (FAQS): 

Q: What is the main challenge in predicting student stress in higher education?

A: The main challenge is the lack of affordable and scalable tools that can detect and manage student stress effectively. Traditional methods often rely on expensive physiological sensors and opaque machine learning models, which can be impractical in resource-limited settings.

Q: What is the objective of this research?

A: The objective is to develop a cost-effective, survey-based stress classification model using multiple machine learning algorithms and Explainable Artificial Intelligence (XAI) to support transparent and actionable decision-making in educational environments.

Q: What machine learning algorithms were used in this study?

A: The study used six classification algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, and XGBoost.

Q: Why is Explainable Artificial Intelligence (XAI) important in this context?

A: XAI is important because it allows the model's decisions to be interpreted and understood, making it easier for educators and administrators to take actionable steps based on the model's predictions. This transparency is essential for building trust and ensuring that the model's recommendations are implemented effectively.

Q: What are the key findings of this research?

A: The key findings indicate that the model performs well in classifying stress levels, with high accuracy and interpretability. The use of XAI techniques has also been successful in providing clear explanations for the model's predictions, making it a valuable tool for educational institutions. 

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