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

Arbër H. Hoti; Xhemal Zenuni; Mentor Hamiti; Jaumin Ajdari

doi:10.2478/orga-2025-0018

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.

References

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