Clustering Undergraduate Students Based on Academic Burnout and Satisfaction from the Field Using Partitioning around Medoid

Abstract

Background. Academic satisfaction is known as one of the most important factors in increasing students’ efficiency, and academic burnout is one of the most significant challenges of the educational system, reducing student motivation and enthusiasm. Clustering methods try to categorize individuals into a number of homogenous groups. Aims. To cluster undergraduate students at Shahrekord University of Medical Sciences based on academic burnout and satisfaction with their field of study. Materials and Methods. The multistage cluster sampling method was used to select 400 undergraduate students from various fields in 2022. The data collection tool included a 15-item academic burnout questionnaire and a 7-item academic satisfaction questionnaire. The average silhouette index was used to estimate the number of optimal clusters. The NbClust package in R 4.2.1 software was used for clustering analysis based on the k-medoid approach. Results. The mean score of academic satisfaction was $17.70\pm 5.39$ , while academic burnout averaged $37.90\pm 13.27$ . The optimal number of clusters was estimated at two based on the average silhouette index. The first cluster included 221 students, and the second cluster included 179 students. Students in the second cluster had higher levels of academic burnout than the first cluster. Conclusion. It is suggested that university officials take measures to reduce the level of academic burnout through academic burnout training workshops led by consultants to promote the students’ interests.