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This study presents a clustering-based approach to enhance early student dropout prediction by analyzing log data from the Moodle Learning Management System. It employs unsupervised learning models, specifically BIRCH, DBSCAN, and GMM, to identify distinct student clusters based on engagement and academic performance, revealing a strong correlation between student activity and dropout rates. The findings support the feasibility of non-invasive data analysis for predicting at-risk students and highlight the

Author

mohithmohithsss4

Language

EN