Predicting Academic Performance in Computational Sciences: Utilising Naive Bayes and SVM Models with Student Interest and Course Data

Key Points

• Academic performance can be effectively predicted using naive bayes and SVM models, highlighting their utility in educational settings.
• Key evidence shows that incorporating student interest data enhances the predictive power of the models.
• Assessment using predictive modeling techniques demonstrated a significant correlation between course data and student success.
• This approach suggests that tailored academic strategies could improve performance, though further studies are necessary.