Clusters of Success: Unpacking Academic Trends with K-Means Clustering in Education
DOI:
https://doi.org/10.21015/vtse.v11i4.1633Abstract
Integrating Educational Data Mining (EDM) into the sector of education has heralded a new era, profoundly impacting learning outcomes by analyzing student performance and preventing academic disengagement. Using the K-Means clustering approach, this study carefully examines the academic accomplishments of students at Mehran University of Engineering and Technology and offers a sophisticated view of student performance patterns through the rigorous analysis of their learning data. A dataset comprising of the academic data of three student batches of the Department of Software Engineering was broken down into the subject categories of Computer Core, General, and Mathematics. The approach of clustering was then applied to find distinct performance patterns across the three subject categories. The findings of the research reveal that students have the highest performance in the computer core category, followed by mathematics, while the weakest overall performance across all three batches was exhibited in the general subject category. The study highlights the disparities in academic performance across distinct clusters and adds to our understanding of academic success while also illuminating the complex interactions between student characteristics and educational outcomes, providing useful information for educators and policymakers.
References
A. S. Rao et al., "Student placement prediction model: a data mining perspective for outcome-based education system," International Journal, year.
M. Yagci, "Educational data mining: prediction of students’ academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, no. 1, pp. 11, 2022.
S. Batool, J. Rashid, M. W. Nisar, J. Kim, H.-Y. Kwon, and A. Hussain, "Educational data mining to predict students’ academic performance: A survey study," Education and Information Technologies, vol. 28, no. 1, pp. 905–971, 2023.
R. Asad, S. Arooj, and S. U. Rehman, "Study of educational data mining approaches for student performance analysis," Technical Journal, vol. 27, no. 01, pp. 68–81, 2022.
D. Hooshyar, Y. Yang, M. Pedaste, and Y.-M. Huang, "Clustering algorithms in an educational context: An automatic comparative approach," IEEE Access, vol. 8, pp. 146 994–147 014, 2020.
S. Križanić, "Educational data mining using cluster analysis and decision tree technique: A case study," International Journal of Engineering Business Management, vol. 12, pp. 1–9, 2020.
M. Z. Hossain, M. N. Akhtar, R. B. Ahmad, and M. Rahman, "A dynamic k-means clustering for data mining," Indonesian Journal of Electrical engineering and computer science, vol. 13, no. 2, pp. 521–526, 2019.
A. Qoiriah, R. Harimurti, A. I. Nurhidayat et al., "Application of k-means algorithm for clustering student’s computer programming performance in automatic programming assessment tool," in International Joint Conference on Science and Engineering (IJCSE 2020). Atlantis Press, 2020, pp. 421–425. DOI: https://doi.org/10.2991/aer.k.201124.075
P. Tang, Y. Wang, and N. Shen, "Prediction of college students’ physical fitness based on k-means clustering and svr," Computer Systems Science and Engineering, vol. 35, no. 4, pp. 237–246, 2020.
M. Silva, R. Rupasingha, and B. Kumara, "A study for determining students’ academic performance based on their activities using clustering approaches," 2022.
S. J. Sultan Alalawi, I. N. Mohd Shaharanee, and J. Mohd Jamil, "Clustering student performance data using k-means algorithms," Journal of Computational Innovation and Analytics (JCIA), vol. 2, no. 1, pp. 41–55, 2023.
I. Gunawan, D. E. Kusumaningrum, T. Triwiyanto, W. Zulkarnain, A. Nurabadi, M. F. A. Sanutra, N. S. Rosallina, M. A. Rofiq, F. Afiantari, K. P. Supriyanto et al., "Hidden curriculum and character building on self-motivation based on k-means clustering," in 2018 4th International Conference on Education and Technology (ICET). IEEE, 2018, pp. 32–35.
Q. Wang, "Application of the intra cluster, characteristic of k-means clustering method in english score analysis in colleges," in Journal of Physics: Conference Series, vol. 1941, no. 1. IOP Publishing, 2021, p. 012001.
A. Rauthan, A. S. Singh, N. Singh et al., "Impact on higher education in pandemic: analysis k-means clustering using urban & rural areas," in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021, pp. 1974–1980.
V. Bahel, S. Malewar, and A. Thomas, "student interest group prediction using clustering analysis: an edm approach," in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, 2021, pp. 481–484.
W. Purba, S. Tamba, and J. Saragih, "The effect of mining data k-means clustering toward students profile model drop out potential," in Journal of Physics: Conference Series, vol. 1007. IOP Publishing, 2018, p. 012049. DOI: https://doi.org/10.1088/1742-6596/1007/1/012049
O. J. Oyelade, O. O. Oladipupo, and I. C. Obagbuwa, "Application of k means clustering algorithm for prediction of students academic performance," arXiv preprint arXiv:1002.2425, 2010.
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, "K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data," Information Sciences, 2022.
R. Nainggolan, R. Perangin-angin, E. Simarmata, and A. F. Tarigan, "Improved the performance of the k-means cluster using the sum of squared error (sse) optimized by using the elbow method," in Journal of Physics: Conference Series, vol. 1361, no. 1, IOP Publishing, 2019, p. 012015.
J.-O. Palacio-Niño and F. Berzal, "Evaluation metrics for unsupervised learning algorithms," arXiv preprint arXiv:1905.05667, 2019.
Z. Gu, "Complex heatmap visualization," Imeta, vol. 1, no. 3, p. e43, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
