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A Review on Analyzing and Predicting At-risk Students by Means of Enhanced Deep Learning Models

by Reshma Nagawade, Nita Patil, Ajay S. Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 55
Year of Publication: 2025
Authors: Reshma Nagawade, Nita Patil, Ajay S. Patil
10.5120/ijca2025925933

Reshma Nagawade, Nita Patil, Ajay S. Patil . A Review on Analyzing and Predicting At-risk Students by Means of Enhanced Deep Learning Models. International Journal of Computer Applications. 187, 55 ( Nov 2025), 18-22. DOI=10.5120/ijca2025925933

@article{ 10.5120/ijca2025925933,
author = { Reshma Nagawade, Nita Patil, Ajay S. Patil },
title = { A Review on Analyzing and Predicting At-risk Students by Means of Enhanced Deep Learning Models },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 55 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number55/a-review-on-analyzing-and-predicting-at-risk-students-by-means-of-enhanced-deep-learning-models/ },
doi = { 10.5120/ijca2025925933 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:10:54.406832+05:30
%A Reshma Nagawade
%A Nita Patil
%A Ajay S. Patil
%T A Review on Analyzing and Predicting At-risk Students by Means of Enhanced Deep Learning Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 55
%P 18-22
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning (ML) prediction of at-risk students Deep Learning (DL)

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