Student performance assessment using machine learning with decision tree

In the age of online learning, assessing student performance has emerged as a vital element for enhancing educational results and guaranteeing academic achievement. This project introduces an AI-driven analytical system that assesses student performance by examining assessment scores and submitted assignment data with the Decision Tree algorithm. The suggested system gathers student information including quiz/test results, assignment submission status, punctuality, and submission quality to create meaningful predictions regarding academic achievement. The Decision Tree algorithm is utilized for its clarity, precision, and capability to manage both categorical and numerical information. The system categorizes students into performance groups like High Performer, Average Performer, and Low Performer, utilizing training data and decision guidelines. It also pinpoints significant factors affecting student achievement or lack thereof, including regular late submissions or persistently low grades in particular subjects. By mapping out decision routes and results, educators can make informed choices like providing remedial classes or tailored learning pathways to improve student engagement and success. This system assists teachers in monitoring performance and facilitates early intervention for at-risk students, rendering it an essential asset in contemporary educational analytics.