A Systematic Analysis of the Impact of Non-Academic Factors on Student Academic Performance Prediction Using Data Mining

Gabriella Caroline Prihayu Ningsih, Febri Liantoni, Yudianto Sujana

Abstract


This study investigates the prediction of students' academic performance using machine learning models through the analysis of 27 research articles. The primary objective is to identify a minimal set of essential features that significantly influence academic outcomes, aiming to optimize model performance and reduce data complexity. A Systematic Literature Review (SLR) was conducted following the PRISMA framework, focusing on key features such as midterm grades, faculty, department, demographic data, and, in some cases, behavioral attributes. The findings reveal that machine learning algorithms like Random Forest (RF) and Artificial Neural Network (ANN) consistently achieve high accuracy, surpassing 85% across various datasets, demonstrating their effectiveness in predicting academic performance. Feature selection methods, particularly filter-based techniques, were observed to significantly enhance the accuracy and efficiency of these models. Integrating diverse data, including dynamic learning behaviors, socio-economic factors, and campus attributes, is shown to further improve classification performance. Despite these advancements, challenges remain, particularly regarding the generalizability of machine learning models. Imbalanced datasets and limited dataset diversity often lead to reduced reliability when models are applied in broader contexts. Addressing these issues requires the development of more robust preprocessing techniques and advanced algorithms. The study also emphasizes the potential of deep learning models to further enhance predictive accuracy, as these approaches are capable of extracting more complex patterns from diverse datasets. Future research should prioritize expanding the scope of datasets to include a wider range of student populations and educational environments. These findings carry significant practical implications for educational institutions, enabling them to implement data-driven strategies for early intervention and personalized support. By identifying at-risk students and understanding factors influencing academic success, institutions can foster better educational outcomes and promote equitable learning opportunities.


Keywords


Data Mining; Academic Performance; Machine Learning; Feature Selection

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DOI: http://dx.doi.org/10.35671/telematika.v19i1.3085

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Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


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