Bayesian AI in Education: Tashenеv University Researchers Apply Naive Bayes Classifier to Predict Student Success

A new study published in the International Journal of Information and Education Technology (Q2, 61st percentile) features the work of V. Nakhipova and Prof. Zh. Umarova from Tashenеv University. The research applies the Naive Bayes classification algorithm — a fundamental method in machine learning — to forecast student academic performance.

The model uses historical student data such as grades, attendance, and digital activity to predict outcomes like course completion success or potential academic risk.

Highlights:

·     Achieved over 80% prediction accuracy;

·     Easy to implement with low computational cost;

·     Suitable for integration into learning management systems (LMS).

 Value:

·     Enables early intervention for struggling students;

·     Supports data-informed decisions by instructors;

Contributes to more personalized and adaptive learning environments

International Journal of Information and Education Technology, Vol. 14, No. 1, 2024, Q2, 61  persentile