Use of the Naive Bayes Classifier Algorithm in Machine Learning for Student Performance Prediction Venera Nakhipova 1 , Yerzhan Kerimbekov 1 , Zhanat Umarova 2,* , Laura Suleimenova 1 , Saule Botayeva 3 , Almira Ibashova 1 , and Nurlybek Zhumatayev 4 1 Department of Computer Science, South Kazakhstan State Pedagogical University, Shymkent, Kazakhstan 2 Department of Information Systems and Modeling, Auezov University, Shymkent, Kazakhstan 3 Department of Information and Communication Technologies, Tashenev University, Shymkent, Kazakhstan 4 Department of Computer Engineering and Software, Auezov University, Shymkent, Kazakhstan E-mail: nakhipovavenera@mail.ru (V.N.); kerimbekov.yerzhan@okmpu.kz (Y.K.); Zhanat-u@mail.ru (Z.U.); Laurasuleimenova7174@gmail.com (L.S.); saule_bb@mail.ru (S.B.); almira_i@mail.ru (A.I.); nuralmiras@mail.ru (N.Z.) * Corresponding author Abstract—This study focused on the development and analysis of a methodological platform grounded in machine learning principles for evaluating learning processes and enhancing student outcomes. The aim of this research was to develop and test a method for evaluating students’ academic performance based on the Naive Bayes classifier. Also, an objective of this study was to create an efficient tool capable of automating and optimize the assessment of educational performance using contemporary machine learning methods and technologies. The study employed the Naive Bayes analysis technique to predict student achievements, with the algorithm being implemented in Python. Despite an emphasis on the development of a software product, the research primarily focused on the development and analysis of the method. Our findings underscore the novelty of this approach, which can serve as a valuable tool for educational institutions and educators. Keywords—machine learning, intelligent systems, naive bayes method, Educational Data Analysis (EDM), productivity, academic performance forecasting I. INTRODUCTION Education is a key driver of societal development and improved quality of life, and in today’s world, it has become more accessible and widespread than ever before. However, accurately evaluating learning processes and student’ achievement remains a difficult task for educational institutions and educators. Accurate assessment not only improves the quality of education but also helps optimize curricula, adapt teaching methodologies, and enhance educational accessibility for all students. In this study, we addressed the following questions: How can we effectively assess the processes of learning and the achievements of students using machine learning methods? How can we automate the measurement of academic performance, while providing accurate and objective evaluations? The proposed approach, based on machine learning and the Naive Bayes classifier, offers several key advantages that make it unique and valuable in the context of evaluating educational processes and student achievements. Automation and Objectivity: This approach automates the assessment of academic performance and learning processes, reducing the subjective influence of human factors. The Naive Bayes classifier analyses data and makes assessments based on probabilistic models, ensuring objective evaluation. Scalability: Machine learning methods can handle vast amounts of data, making them ideal for processing data related to educational processes, where large volumes of information about students and their achievements are collected. Pattern Discovery: Machine learning enables the identification of complex and indirect relationships in data that might go unnoticed using traditional assessment methods. This helps educational institutions understand which factors ad approaches truly impact student success. Personalized Approach: Machine learning methods allow the creation of personalized models for each student, that consider their unique characteristics and educational needs. We choose the Naive Bayes classifier because it is well- suited for classification tasks involving predicting student academic performance. Its advantages include high performance on large datasets and the ability to handle many features, which are often characteristic of education-related data. Furthermore, the Naive Bayes classifier can generalize information from past observations, making it a powerful tool for predicting student performance. The software product under development represents an innovative solution capable of efficiently processing and analyzing extensive datasets pertinent to learning processes and academic performance. The product boasts modern data processing algorithms and machine learning-based analytical techniques. With this software, users can effortlessly collect and store a wide array of data, including academic grades, student progress reports, and various information about educational processes. The program automatically handles these data, identifying patterns and trends, and assessing student progress and the effectiveness of teaching methods. Leveraging the outcomes of this analysis, the software generates valuable information and reports. These resources can enable educators, administrators, and other stakeholders, to make well-informed decisions aimed at enhancing educational processes. Consequently, educational institutions can optimize curricula, adapt teaching methodologies, and International Journal of Information and Education Technology, Vol. 14, No. 1, 2024 92 doi: 10.18178/ijiet.2024.14.1.2028 Manuscript received June 21, 2023; revised August 16, 2023; accepted October 23, 2023; published January 23, 2024 Speed and Efficiency: Automating the assessment process using machine learning significantly accelerates the generation of results and provides timely feedback, which can be critical for adapting educational programs.