Cluster Pattern Analysis of Students Stress using Machine Learning Algorithms with Feature Engineering

Students attending school experience stress due to a variety of factors that can originate either on campus or from home. In managing stress and other related issues, educational institutions now have counselling units that operate as centres. Moreover, several institutions have designated academic counsellors in departments to address the increasing demand for counselling services due to an expanding student population. Timely detection and proactive counselling of stress among students can help avert dropouts, health issues, and other learner behaviours that are detrimental to academic work. This study proposes two approaches to facilitate the automation of student counselling for stress management. We first implemented the K-means algorithm and formed clusters using the elbow and the Silhouette methods. The clusters formed reveal three groups of students. The stressors significantly affected one group, making it vulnerable. The stressors moderately impacted another group, while the final group experiences minimal stress. In the second part of the study, we proposed a classification model to identify the cluster group of any new student. The results of the classification show superior performance for the Decision Tree algorithm with an accuracy of 97.64%. The improvement in the efficiency of the classification algorithms was attained through feature engineering using the Chi-square method.