Optimizing Adaptive Learning: Insights from K-Means Clustering in Intelligent Tutoring Systems
Keywords:
Adaptive Learning, Intelligent Tutoring Systems, Clustering Algorithms, Evaluation Metrics.Abstract
In the realm of intelligent tutoring systems, the concept of clustering groups holds immense potential for enhancing the adaptability and efficacy of educational platforms. Clustering techniques play a pivotal role in organizing learners into meaningful groups based on various criteria, such as learning preferences, proficiency levels, and engagement patterns. In this groundbreaking research paper, we meticulously evaluate the performance of clustering algorithms within the context of intelligent tutoring systems. Our study employs three key metrics—Caliński-Harabasz (CH) Index, Silhouette Score, and Diversity Index—to assess the outcomes of clustering processes across diverse datasets. The investigation is specifically tailored to inform the clustering of learners into groups within intelligent tutoring systems. Our analysis spans datasets such as R15, Aggregation, D31, Pathbased, Jain, and Spiral, offering profound insights into the strengths and limitations of clustering methodologies in the context of educational adaptability. By elucidating optimal clustering scenarios, our findings aim to guide the creation of tailored learning groups, fostering personalized and efficient educational experiences for learners within intelligent tutoring systems. This research significantly advances the discourse on clustering strategies, providing valuable insights for the enhancement of intelligent tutoring systems.
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