Data Mining for Evaluating Student Academic Performance in the Context of Online Learning
Keywords:
data mining, student academic performance, ONLINE LEARNING disruptions, predictive modeling, machine learning.Abstract
This assessment investigates the appraisal of student academic execution in the setting of ONLINE LEARNING aggravations using data mining techniques. Using advanced computations including Direct Backslide, Decision Trees, Unpredictable Forest, and Mind Associations, it inspected an alternate dataset encompassing student records, fragment information, and responsibility estimations. Through exhaustive experimentation and assessment with related assessments, our disclosures display the ampleness of data mining in expecting student results. Specifically, Inconsistent Forest and Cerebrum Associations emerged as top-performing computations, achieving exactnesses of 85% and 90%, independently. Exactness, audit, and F1 scores have been also generally raised for Cerebrum Associations, showing their common insightful limits. These results feature the capacity of data mining ways to deal with significant encounters with the puzzling components of student learning amid pandemic-provoked aggravations.
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