Predicting Achievers in an Online Theatre Course Designed upon the Principles of Sustainable Education

The development of online courses aligned with sustainable education principles is crucial for equipping learners with 21st-century skills essential for a sustainable future. As online education expands, predicting achievers (in this research, students with a final grade of seven or higher) becomes essential for optimizing instructional strategies and improving retention rates. This study employs a Linear Discriminant Analysis (LDA) model to predict academic performance in an online theatre course rooted in sustainable education principles. Engagement metrics such as total logins and collaborative assignment completion emerged as decisive predictors, aligning with prior research emphasizing active learning and collaboration. The model demonstrated robust performance, achieving 90% accuracy, 80% specificity, and an 88% correct classification rate. These results underscore the potential of machine learning in identifying achievers while highlighting the significance of sustainable pedagogical components. Future research should explore emotional engagement indicators and multi-course validation to enhance predictive capabilities. By utilizing the e-learning system information, the presented methodology has the potential to assist institutional policymakers in enhancing learning outcomes, advancing sustainability goals, and supporting innovation across the educational and creative sectors.