Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis

Raghul Gandhi Venkatesan (), Dhivya Karmegam () and Bagavandas Mappillairaju ()
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Raghul Gandhi Venkatesan: SRM Institute of Science and Technology
Dhivya Karmegam: SRM Institute of Science and Technology
Bagavandas Mappillairaju: SRM Institute of Science and Technology

Journal of Computational Social Science, 2024, vol. 7, issue 1, No 7, 196 pages

Abstract: Abstract Student dropout is non-attendance from school or college for an extended period for no apparent cause. Tending to this issue necessitates a careful comprehension of the basic issues as well as an appropriate intervention strategy. Statistical approaches have acquired much importance in recent years in resolving the issue of student dropout. This is due to the fact that statistical techniques can efficiently and effectively identify children at risk and plan interventions at the right time. Thirty-six studies in total were reviewed to compile, arrange, and combine current information about statistical techniques applied to predict student dropout from various academic databases between 2000 and 2023. Our findings revealed that the Random Forest in 23 studies and the Decision Tree in 16 studies were among the most widely adopted statistical techniques. Accuracy and Area Under the Curve were the frequently used evaluation metrics that are available in existing studies. However, it is notable that the majority of these techniques have been developed and tested within the context of developed nations, raising questions about their applicability in different global settings. Moreover, our meta-analysis estimated a pooled proportion of overall dropouts of 0.2061 (95% confidence interval: 0.1845–0.2278), revealing significant heterogeneity among the selected studies. As a result, this systematic review and meta-analysis provide a brief overview of statistical techniques focusing on strategies for predicting student dropout. In addition, this review highlights unsolved problems like data imbalance, interpretability, and geographic disparities that might lead to new research in the future.

Keywords: Dropout prediction; Supervised learning; Unsupervised learning; Meta-analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-023-00231-w

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