Analysis of Factors Affecting Academic Success with Machine Learning: Data-Driven Inferences in Education

Abstract

With the digital transformation in education, big data analytics is increasingly being used to understand, monitor, and improve students' academic performance. Analyzing student behavior, engagement levels, prior achievements, and study habits enables the creation of more effective and personalized learning environments. This study aimed to predict academic achievement from student data using machine learning (ML) algorithms and to identify the factors affecting achievement. Seven different algorithms were implemented for this purpose: SVM, LR, KNN, RF, NB, DT, and LDA. The RF, SVM, and LDA algorithms achieved the highest accuracy rate of 91%. The LDA model was determined to be the most successful model in terms of accuracy and balance performance. Analysis revealed that variables such as class participation, study time, and prior achievement level significantly impact student achievement. The findings demonstrate that self-management, self-regulation, and intrinsic motivation skills play a critical role in academic success. Consequently, machine learning-based models have strong potential for predicting student achievement and identifying at-risk students early. This study highlights the importance of data-driven decision-making processes in education and guides future research on AI-supported applications.