Abstract

This study aims to investigate the potential of educational data mining (EDM) in addressing the issue of delayed completion in undergraduate student thesis courses. Delayed completion of these courses is a common issue that affects both students and higher education institutions. This study employed clustering analysis to create clusters of thesis topics. The research model was constructed using expert labeling to assign each thesis title to a computer science ontology standard. Cross-referencing was employed to associate supporting courses with each thesis title, resulting in a labeled dataset with three supporting courses for each thesis title. This study analyzed five different clustering algorithms, including K-Means, DBScan, BIRCH, Gaussian Mixture, and Mean Shift, to identify the best approach for analyzing undergraduate thesis data. The results demonstrated that k-means clustering is the most efficient method, generating five distinct clusters with unique characteristics. Furthermore, this study investigated the correlation between educational data, specifically GPA, and the average grades of courses that support a thesis title and the duration of thesis completion. Our investigation revealed a moderate correlation between GPA, thesis-supporting course average grades, and the time to complete the thesis, with higher academic performance being associated with shorter completion times. These moderate results indicate the need for further studies to explore additional factors beyond GPA and the average grades of thesis-supporting courses that contribute to delays in thesis completion. This study contributes to the understanding and evaluation of educational outcomes within study programs, as defined in the curriculum, particularly concerning the design and implementation of thesis topics. Additionally, the clustering results serve as a foundation for future research and offer valuable insights into the potential of EDM techniques to assist in selecting appropriate thesis topics, thereby reducing the risk of delayed completion.

Original languageEnglish
Pages (from-to)130072-130088
Number of pages17
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Keywords

  • Computing classification system
  • clustering analysis
  • k-means
  • ontology
  • undergraduate thesis

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