Version 1
: Received: 28 April 2024 / Approved: 28 April 2024 / Online: 29 April 2024 (10:25:21 CEST)
How to cite:
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. Preprints2024, 2024041888. https://doi.org/10.20944/preprints202404.1888.v1
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. Preprints 2024, 2024041888. https://doi.org/10.20944/preprints202404.1888.v1
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. Preprints2024, 2024041888. https://doi.org/10.20944/preprints202404.1888.v1
APA Style
Bagunaid, W., Chilamkurti, N., Shahraki, A. S., & Bamashmos, S. (2024). Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction. Preprints. https://doi.org/10.20944/preprints202404.1888.v1
Chicago/Turabian Style
Bagunaid, W., 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" Preprints. https://doi.org/10.20944/preprints202404.1888.v1
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 is 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).
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
