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

Hoti Arbër H. (), Zenuni Xhemal, Hamiti Mentor and Ajdari Jaumin
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Hoti Arbër H.: South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia
Zenuni Xhemal: South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia
Hamiti Mentor: South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia
Ajdari Jaumin: South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia

Organizacija, 2025, vol. 58, issue 3, 296-310

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.

Keywords: Student performance; Machine learning; System evaluation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:organi:v:58:y:2025:i:3:p:296-310:n:1006

DOI: 10.2478/orga-2025-0018

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