User Evaluation of a Machine Learning-Based Student Performance Prediction Platform

Abstract Background/Purpose The integration of machine learning in education has opened new possibilities for predicting student performance and enabling early interventions. While most of the work has been focused on prediction algorithms design and evaluations, little work has been done on user-centric evaluations. Methodology This study evaluates a web-based platform designed for student performance prediction using various machine learning algorithms. Users, including students, professors, and career counselors, tested the platform and provided feedback on usability, accuracy, and recommendation likelihood. Results Results indicate that the platform is user-friendly, requires minimal technical support, and delivers reliable predictions. Conclusion Users strongly endorsed its adoption, highlighting its potential to assist educators in identifying at-risk students and improving academic outcomes.