Educational Data Mining (EDM) is an emerging field that aims to better understand students' behavior patterns and learning environments by employing statistical and machine learning methods to analyze large repositories of educational data. Analysis of variable data in the early stages of a course might be used to develop a comprehensive prediction model to prevent students' failure or dropout. This will assist instructors in intervening effectively. This dissertation utilizes machine learning models to predict students' learning outcomes based on their interactions with online learning environments. It also focuses on students' learning style detection to enhance their academic performance and personalize learning resources in online learning environments
