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

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Observational analysis reveals predictive modeling for academic performance using SVM in students, indicating improved outcomes for course selection.

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.

Osayomore et al. (2026) studied this question.

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View Full Paper

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Observational analysis reveals predictive modeling for academic performance using SVM in students, indicating improved outcomes for course selection.

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.

Osayomore et al. (2026) studied this question.

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