Simulation of Student Study Group Formation Design Using K-Means Clustering

This research focuses on developing a simulation model for forming student study groups using an enhanced K-Means algorithm, addressing the challenge of optimizing group dynamics to improve learning outcomes. By analyzing the effectiveness of the formed study groups through RMSE (Root Mean Square Er...

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Bibliographic Details
Published in:	MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5; no. 2; pp. 598 - 608
Main Authors:	Putra, Yudistira Ardi Nugraha Setyawan, Margono, Hendro
Format:	Journal Article
Language:	English
Published:	21.03.2025
ISSN:	2797-2313, 2775-8575
Online Access:	Get full text
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Summary:	This research focuses on developing a simulation model for forming student study groups using an enhanced K-Means algorithm, addressing the challenge of optimizing group dynamics to improve learning outcomes. By analyzing the effectiveness of the formed study groups through RMSE (Root Mean Square Error) after dimensionality reduction with various regression models—including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor—we aim to provide educators with a robust tool for assessing group configurations. The study identifies four distinct clusters, revealing that "Previous_Score" and "Attendance" are critical variables, achieving a highest Silhouette Score of 0.64 with five selected features. The ridge regression model also yielded a low RMSE of 0.045, explaining 72.39% of the variance in "Exam_Score." The findings suggest that targeted interventions tailored to each cluster—yellow, purple, blue, and green—can enhance academic outcomes by addressing specific student needs. This data-driven approach optimizes group dynamics and fosters a more inclusive learning environment, enhancing academic performance and cultivating essential social skills. The study underscores the potential of machine learning techniques in education and suggests avenues for future research into alternative clustering methods and their long-term impact on student engagement and success.
ISSN:	2797-2313 2775-8575
DOI:	10.57152/malcom.v5i2.1795