ArticleComputational and mathematical methods in medicine2023
Clustering Undergraduate Students Based on Academic Burnout and Satisfaction from the Field Using Partitioning around Medoid.
Article in Computational and mathematical methods in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 6 citations in OpenAlex.
- Satisfaction level of dental graduates with the quality of clinical training in the undergraduate dental curriculum of Guilan Dental School.Journal of education and health promotion · 2025Article
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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 ± 5.39, while academic burnout averaged 37.90 ± 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.
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