A thematic review on predicting student performance using data mining

Student performance, a key criterion for academic placement and success, can be effectively predicted and potentially improved using advanced data mining techniques. However, more studies delve into patterns and methods for forecasting student performance in this context. This study presents a comprehensive thematic review of the literature published between 2016 and 2022 to highlight prevailing trends and underline the significant gaps in this rapidly growing research domain. The review emphasizes that the highest number of related publications were seen in 2021, with a clear focus on higher educational institutions. Two major factors were recurrent in these studies: academic records and demographic information of the students, widely recognized as significant determinants of academic success. This observation reaffirms previous research asserting the substantial role of academic records in predicting student performance. Our analysis expands the scope of prior literature reviews by incorporating studies from Mendeley and Science Direct databases. Interestingly, we discovered a glaring research gap in predicting performance in Malaysian secondary schools using locally relevant datasets. Therefore, further exploration in this niche could benefit this geographical context. Furthermore, our review identified the need to investigate other variables that may impact student performance. There is vast potential for unearthing novel insights that could substantially enhance our understanding and predictive accuracy. Consequently, this study emphasizes the importance of continuing research in this field, integrating various datasets, geographical contexts, and potential predictors to better anticipate and address student performance across various educational scenarios.

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