AI Meets Education: Tashenеv University Researchers Enhance Student Performance Prediction with Hybrid Algorithms

In a recent Q1 publication in the International Journal of Information and Communication Technology Education (CiteScore 4.2, 76th percentile), V. Nakhipova and Dr. Saule Botayeva from Tashenеv University present an innovative approach to predicting student academic performance using a combination of machine learning techniques.

The study integrates the Naive Bayes classification method with collaborative filtering — a strategy commonly used in recommendation engines like Netflix and Amazon — to improve the accuracy of student success forecasting.

Key contributions:

·     Analyzed student behavior data including attendance, grades, and LMS activity;

·     Early identification of at-risk students;

·     Support for personalized learning paths and academic intervention strategies.

Practical outcomes:

·     Helps instructors take timely action;

·     Enhances digital learning systems with predictive insights;

·     Can be embedded into educational platforms for real-time analytics.

This research represents a strong step toward intelligent, data-driven education and confirms the university’s commitment to innovation in teaching and learning.

International Journal of Information and Communication Technology Education (IJICTE). – 2024. – Vol. 20. – №1. – Р. 1-18.(Сitescore 2023-4.2; Q1, 76  persentile) https://doi.org/10.4018/IJICTE.352512