User Evaluation of a Machine Learning-Based Student Performance Prediction Platform
Publié en ligne: 12 août 2025
Pages: 296 - 310
Reçu: 04 janv. 2025
Accepté: 09 mai 2025
DOI: https://doi.org/10.2478/orga-2025-0018
Mots clés
© 2025 Arbër H. Hoti et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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.