Abstract

A critical job for guaranteeing at-risk students' academic achievement and wellbeing in educational environments is to identify and support them. Traditional techniques of identifying at-risk students frequently rely on subjective evaluations which can be labour-intensive, time-consuming. Researchers have looked into the possibilities of deep learning models in analyzing and forecasting at-risk students in light of the introduction of cutting-edge technology and the availability of large-scale educational data. The purpose of this review is to offer a thorough overview of the research on improved deep learning models for identifying and forecasting at-risk students. The review's conclusions suggested that a variety of Deep Learning (DL) techniques are employed to comprehend and resolve these problems, including identifying at-risk students and dropout rates.

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