Classification of Student Stress Levels Using Machine Learning Methods
Abstract views: 9
/ 
PDF downloads: 5
Keywords:
Stress, Student stress prediction, Student Stress Monitoring Dataset, Logistic Regression, Random Forest, XGBoostAbstract
Nowadays, students feel under pressure and experience stress for various reasons. These reasons
generally manifest as academic pressures, the social environment, and personal anxieties. Intense stress is
a significant factor that negatively impacts students' academic success, psychological health, and overall
quality of life. The inadequacy of traditional subjective assessment methods in determining stress levels
accurately and reliably has increased the need for objective, data-driven solutions. The main purpose of this
study is to automatically classify student stress levels (Low, Medium, High) using machine learning
algorithms and the Student Stress Monitoring Dataset from Kaggle. This dataset contains 1,100
observations, 21 features, and no missing values. In this study, Logistic Regression, Random Forest, and
XGBoost models were applied for classification. The accuracy of these models was measured as 88.1%,
86.2%, and 86.8%, respectively. The results show that machine learning methods can be used effectively
in predicting student stress levels.
Downloads
References
G. A. Gobena, "Effects of Academic Stress on Students' Academic Achievements and Its Implications for Their Future Lives," Anatolian Journal of Education, c. 9, sy. 1, ss. 113-130, 2024.
M. C. Pascoe, S. E. Hetrick and A. G. Parker, "The impact of stress on students in secondary school and higher education," International Journal of Adolescence and Youth, vol. 25, no. 1, pp. 104-112, 2020.
L. Rosenthal, S. Lee, P. Jenkins, J. Arbet, S. Carrington, S. Hoon, S. K. Purcell and P. Nodine, "A Survey of Mental Health in Graduate Nursing Students during the COVID-19 Pandemic," Nurse Educ., c. 46, sy. 4, ss. 215-220, 2021.
D. Bedewy and A. Gabriel, "Examining perceptions of academic stress and its sources among university students: The Perception of Academic Stress Scale," Health Psychology Open, vol. 2, no. 2, pp. 1-9, 2015.
M. G. I. Emran, S. Mahmud, A. H. Khan, N. N. Bristy, A. K. Das, R. Barma, A. Barma, M. H. Mita, L. Bosunia, M. Rahman and M. Roy, "Factors Influencing Stress Levels Among Students: A Virtual Exploration," European Journal of Medical and Health Sciences, c. 6, sy. 6, ss. 1-13, 2024.
N. Najafi, K. Movahed, Z. Barzegar and S. Samani, "Environmental Factors Affecting Students' Stress in the Educational Environment: A Case Study of Shiraz Schools," International Journal of School Health, c. 5, sy. 2, ss. 1-7, 2018.
S. Arya, A. Anju and N. A. Ramli, "Predicting the stress level of students using Supervised Machine Learning and Artificial Neural Network (ANN)," Indian Journal of Engineering, c. 21, ss. 1-24, 2024.
E. Abdelfattah, S. Joshi and S. Tiwari, "Machine and Deep Learning Models for Stress Detection Using Multimodal Physiological Data," IEEE Access, vol. 13, pp. 4597-4608, 2025.
E. Lazarou and T. P. Exarchos, "Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices," AIMS Neuroscience, vol. 11, no. 2, pp. 76-102, 2024.
R. V. Anand, A. Q. Md, S. Urooj, S. Mohan, M. A. Alawad and A. C., "Enhancing Diagnostic Decision-Making: Ensemble Learning Techniques for Reliable Stress Level Classification," Diagnostics, c. 13, sy. 22, ss. 3455, 2023.
M. S. I. Ovi, J. Hossain, M. R. A. Rahi and F. Akter, "Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring," arXiv preprint, arXiv:2508.01105, 2025.
M. P. LaValley, "Logistic regression," Circulation, vol. 117, no. 18, pp. 2395-2399, 2008.
S. Çelebioğlu and S. Sürücü, "Predicting the University Placement Status of University Students Using Artificial Intelligence," International Journal of Advanced Natural Sciences and Engineering Research, c. 7, sy. 2, ss. 1-4, 2023.
P. Probst, N. W. Marvin and A. Boulesteix, "Hyperparameters and tuning strategies for random forest," Wiley Interdisciplinary Reviews: data mining and knowledge discovery, vol. 9, sy. 3, 2019.
T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), San Francisco, CA, USA, Aug. 2016, pp. 785–794.
