STUDENT SUCCESS PREDICTION IN HIGHER EDUCATIONTHROUGH MACHINE LEARNING
Keywords:
Student Success Prediction, Higher Education, Machine Learning, Educational Data Mining, Predictive Modeling, Academic Performance, Student Retention, Intelligent SystemsAbstract
The purpose of this program is to predict the success of college students by utilizing machine learning techniques. These techniques involve the identification of patterns and variables that impact academic performance. Data-driven approaches are being used by educational institutions more and more. This is because algorithms like support vector machines, decision trees, and neural networks offer the possibility of early intervention and individualized help. The study looks at students' demographics, academic records, and behavioral traits in order to create predictive models that can group them into groups at risk of performing poorly. Teachers can improve students' performance, engagement, and retention by giving them the tools they need to make informed decisions. In addition to adding to the expanding body of knowledge in educational data mining, this research helps schools maximize the use of smart technologies to improve student performance.
