<![CDATA[Research on student performance prediction has advanced rapidly in recent years, driven by the increasing volume of educational data generated by digital learning platforms. This data can be analyzed using Machine Learning (ML) and Deep Learning (DL) techniques, integrated with feature management strategies tailored to specific needs. However, selecting the most relevant features and optimizing predictive models remain significant challenges. Different studies apply various feature selection and engineering techniques, leading to inconsistent results and limited generalizability. This study conducts as a Systematic Literature Review (SLR) to explore ML and DL approaches for student performance prediction, emphasizing their relationship with feature management techniques. The reviewed studies span publications from 2019 to 2024. This SLR aims to assist researchers in identifying effective strategies for predicting student performance, including the selection of methods, datasets, or feature management techniques. Most studies utilized publicly available datasets due to their accessibility and ease of use. Among ML methods, Random Forest emerged as the most frequently applied, achieving an F-measure of 99.5% integration of filter-based and wrapper-based feature selection techniques. Among DL approaches, the ANN-PCACSN model, employing Principal Component Analysis (PCA) for dimensionality reduction, achieved the highest accuracy of 99.32%. These findings highlight the importance of aligning preprocessing strategies with dataset properties and algorithm capabilities to enhance predictive performances.]]>
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