Advanced Hotelling T² Statistical Model for predicting student ccademic performance in key computing and statistical science courses

This study explores the application of Hotelling T² statistical modeling to enhance academic performance prediction in key computational science courses, specifically Simulation and Modelling, Probability and Statistics, Data Analysis, and Statistical Computing. The primary objective was to identify key predictors of academic performance and assess the impact of student interest on performance outcomes. Employing a quantitative approach, the research utilized both primary data from structured questionnaires and secondary data from academic scores, analyzed through Hotelling T² models to compare performance across courses and detect performance trends. The findings revealed significant correlations between student interest and performance in each course, indicating that higher student engagement leads to better academic results. The study’s implications suggest that focusing on increasing student interest can effectively improve performance, offering actionable insights for educators to tailor interventions. Future research could investigate additional factors affecting performance and apply similar methodologies to other academic disciplines. Recommendations include implementing targeted strategies to boost student interest in computational science courses and using statistical models for ongoing performance monitoring and improvement.