About this document
Student Behavior Analysis for Online Learning by bziani is a document available to read on EtoBox.
This thesis investigates the correlation between student behavior and performance in online courses using machine learning techniques. By analyzing web logs and course syllabi, the study develops predictive models achieving an average accuracy of 87% in forecasting student performance without relying on score-related features. The research identifies significant student behaviors and combinations that contribute to academic success, enabling early interventions to enhance learning outcomes.
- Author
- bziani
- Language
- EN