Bakyalakshmi, V and Kanchana, S (2024) A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction. A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction, 31 (6). pp. 293-304. ISSN 1074-133X

[thumbnail of A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction.pdf] Text
A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction.pdf - Published Version
Download (387kB)

Abstract

Educational institutions are utilizing Deep Learning (DL) techniques to develop predictive
systems that identify students at risk of underperforming based on historical academic data
patterns, thereby enhancing their educational outcomes through targeted interventions.
From this outlook, an Ensemble Generative Adversarial Network with a Student
Accomplishment prediction using the Distinctive DL (EGAN-SADDL) model was
designed to generate large-scale student data and predict their academic achievements.
However, integrating heterogeneous kinds of student data into the SADDL model is a
complex task that, if not executed properly, may result in the model failing to capture
crucial data relationships, leading to lower performance. Hence, this paper proposes an
EGAN with Improved SADDL (EGAN-ISADDL) model based on multi-view learning for
predicting student academic performance. The main aim of this model is to learn features
from multiple sources, including academic records, demographic information, and social
media activity, using the multi-view learning approach. First, the academic and
demographic attributes of students are collected, along with the physiological features
extracted from the information posted on social media by students. Second, the Long Short
Term Memory with Deep Convolutional Neural Network (LSTM-DCNN) and Recursive
Neural Network (ReNN) models receive these features in parallel, extracting intermediate
features in multiple views. Third, a multi-view classifier jointly learns each set of features
to predict students' academic performance, enabling early identification of at-risk students
with high accuracy. Finally, experiments conducted on a dataset of 50,000 student records
demonstrate that EGAN-ISADDL attains 96.28% accuracy compared to the existing single
view learning models.

Item Type: Article
Uncontrolled Keywords: Academic performance prediction, EGAN-SADDL, Heterogeneous data, Multi-view learning, Recursive neural network.
Divisions: PSG College of Arts and Science > Department of Computer Science
PSG College of Arts and Science > Department of Software System
Depositing User: Dr. B Sivakumar
Date Deposited: 03 Dec 2025 09:43
Last Modified: 03 Dec 2025 09:43
URI: https://ir.psgcas.ac.in/id/eprint/2554

Actions (login required)

View Item
View Item