Clustering Student Understanding Levels In Software Engineering Courses

Martini Dwi Endah Susanti () and Rindu Puspita Wibawa
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Martini Dwi Endah Susanti: Universitas Negeri Surabaya, Informatics Department
Rindu Puspita Wibawa: Universitas Negeri Surabaya, Informatics Department

A chapter in Proceedings of the 2023 Brawijaya International Conference (BIC 2023), 2024, pp 693-703 from Springer

Abstract: Abstract The level of understanding in learning is one of the main things that influence the course of the process of learning activities. Software Engineering is a scientific discipline that addresses all aspects of software production starting from the early stages of system maintenance. In the Software Engineering course, each material is interrelated between one material and another. If students cannot understand the previous material, it will be difficult for them to understand the next material. Data mining technology can be used to understand some of the problems that arise in education management, including to analyze the level of students’ understanding of certain subjects. This study aims to determine the level of understanding clusters of students in Software Engineering courses using the K-means Clustering method. The results of this study are that student data is clustered into 2 clusters, namely the GOOD and POOR clusters. Evaluation was carried out using the Elbow method and calculating the Silhouette Score. The optimal number of clusters obtained from the elbow method is 2 clusters with a silhouette score of 0.836.

Keywords: clustering; k-means clustering; elbow method; silhouette score; data mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-525-6_76

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