An Analysis of Datasets for Student Performance Evaluation Based on Machine Learning
DOI:
https://doi.org/10.112222/ijits.v1.i1.19507Keywords:
Student performance prediction, data analysis, machine learning , data mining, student performance datasetAbstract
The prediction of student academic performance through data analysis and machine learning has become increasingly significant for improving educational systems and student outcomes. This study explores three publicly available datasets—Open University Learning Analytics Dataset (OULAD), xAPI-Edu-Data, and the UCI Student Performance dataset—to identify critical factors influencing academic success across secondary and higher education levels. These datasets include diverse data sources such as institutional records, behavioral engagement logs, and survey responses. The analysis employs various machine learning and deep learning techniques to evaluate the predictive performance of traditional models, ensemble methods, and hybrid architectures. An experimental validation was performed using a Random Forest classifier on the xAPI-Edu-Data dataset, achieving 80.56% accuracy and supporting the reliability of ensemble approaches. Results show that advanced ensemble and hybrid models like RF-SVM and ECNN-ResNet outperform classical methods in terms of accuracy, precision, recall, and F1-score. This research underscores the potential of intelligent systems to enable early identification of at-risk students, facilitate personalized learning interventions, and inform strategic academic decisions. These findings contribute to a growing body of work that promotes data-driven, equitable, and effective educational practices.
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Copyright (c) 2025 Husam Kadhim Gharkan, Mustafa Radif , Ali Hakem Alsaeedi
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