WITH MACHINE LEARNING BASED ON EDUCATIONAL DATASTUDENT LEARNING PREDICTION MODEL (Universal International Scientific Journal, 2026)
This study aims to develop a model for predicting students’ knowledge levels usingmachine learning methods based on educational data. In digital learning environments, the growingvolume of educational data provides significant opportunities for personalizing the learning processand improving academic outcomes; however, these opportunities are not sufficiently utilized inpractice. Therefore, the development of intelligent models capable of predicting student learningperformance in advance remains a pressing research issue.Within the research methodology, a dataset reflecting the academic activities of students in ahigher education institution was constructed and subjected to initial preprocessing. Predictive modelswere developed using linear regression, decision trees, random forest, and artificial neural networkalgorithms. Model performance was evaluated and compared using accuracy and error metrics.The results indicate that ensemble-based models achieve higher accuracy in predicting students’knowledge levels. The developed model can be effectively applied to early identification of at-riskstudents, the design of adaptive learning strategies, and the improvement of educational quality. Thefindings provide a scientific and practical foundation for integrating artificial intelligence technologiesinto the education system and for supporting data-driven pedagogical decision-making
Keywords: Machine Learning; Educational Data Analysis; Student Knowledge Prediction