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Article

Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction

Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
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Future Internet 2024, 16(6), 206; https://doi.org/10.3390/fi16060206
Submission received: 27 April 2024 / Revised: 28 May 2024 / Accepted: 6 June 2024 / Published: 11 June 2024

Abstract

Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted, adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data are securely stored on a blockchain using the LWEA encryption method.
Keywords: E-FedCloud; Cycle General Adversarial Network (CGAN); Majority Voting-Based Multi-Objective Clustering (MV-MOC); Duelling Deep Q Network (ID2QN) E-FedCloud; Cycle General Adversarial Network (CGAN); Majority Voting-Based Multi-Objective Clustering (MV-MOC); Duelling Deep Q Network (ID2QN)

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MDPI and ACS Style

Bagunaid, W.; Chilamkurti, N.; Shahraki, A.S.; Bamashmos, S. Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction. Future Internet 2024, 16, 206. https://doi.org/10.3390/fi16060206

AMA Style

Bagunaid W, Chilamkurti N, Shahraki AS, Bamashmos S. Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction. Future Internet. 2024; 16(6):206. https://doi.org/10.3390/fi16060206

Chicago/Turabian Style

Bagunaid, Wala, Naveen Chilamkurti, Ahmad Salehi Shahraki, and Saeed Bamashmos. 2024. "Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction" Future Internet 16, no. 6: 206. https://doi.org/10.3390/fi16060206

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