Proceedings of the 2023 Brawijaya International Conference (BIC 2023)

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Clustering Student Understanding Levels In Software Engineering Courses

Authors
Martini Dwi Endah Susanti1, *, Rindu Puspita Wibawa2
1Informatics Department, Universitas Negeri Surabaya, Surabaya, Indonesia
2Informatics Department, Universitas Negeri Surabaya, Surabaya, Indonesia
*Corresponding author. Email: martinisusanti@unesa.ac.id
Corresponding Author
Martini Dwi Endah Susanti
Available Online 29 October 2024.
DOI
10.2991/978-94-6463-525-6_76How to use a DOI?
Keywords
clustering; k-means clustering; elbow method; silhouette score; data mining
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.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 Brawijaya International Conference (BIC 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
29 October 2024
ISBN
978-94-6463-525-6
ISSN
2352-5428
DOI
10.2991/978-94-6463-525-6_76How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Martini Dwi Endah Susanti
AU  - Rindu Puspita Wibawa
PY  - 2024
DA  - 2024/10/29
TI  - Clustering Student Understanding Levels In Software Engineering Courses
BT  - Proceedings of the 2023 Brawijaya International Conference (BIC 2023)
PB  - Atlantis Press
SP  - 693
EP  - 703
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-525-6_76
DO  - 10.2991/978-94-6463-525-6_76
ID  - Susanti2024
ER  -