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Personalized Student Learning Mechanisms using K-Means and K-Medoids Clustering Algorithms based on Individual Preferences
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 46 |
| Year of Publication: 2023 |
| Authors: Rendy Wenda Dwi Kurniawan, Arief Hermawan |
10.5120/ijca2023923274
|
Rendy Wenda Dwi Kurniawan, Arief Hermawan . Personalized Student Learning Mechanisms using K-Means and K-Medoids Clustering Algorithms based on Individual Preferences. International Journal of Computer Applications. 185, 46 ( Nov 2023), 13-19. DOI=10.5120/ijca2023923274
Abstract
This research aims to minimize the mismatch between competencies and career interests, a common issue faced by students who often find themselves in the wrong field of study or working in jobs unrelated to their educational background. Determining an effective and efficient learning mechanism through precise student clustering to form optimal learning groups is an effort that can be made to better align with the individual preferences of each student. However, the process of clustering high school students encounters challenges such as resource constraints, time limitations, and the need for effective outcomes. Therefore, this research explores a solution by implementing a clustering process for high school students based on individual preferences, including subject interests, career aspirations, and preferred learning methods, using the K-Means and K-Medoids algorithms. Performance analysis of both algorithms reveals that K-Means outperforms in handling student preference data, resulting in an optimal number of clusters of 6. The model evaluation results indicate a Silhouette Coefficient of 0.786 for K-Means and a Davies-Bouldin Index of 0.334.
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