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

Author

Listed:
  • Wala Bagunaid

    (Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia)

  • Naveen Chilamkurti

    (Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia)

  • Ahmad Salehi Shahraki

    (Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia)

  • Saeed Bamashmos

    (Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia)

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.

Suggested Citation

  • Wala Bagunaid & Naveen Chilamkurti & Ahmad Salehi Shahraki & Saeed Bamashmos, 2024. "Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction," Future Internet, MDPI, vol. 16(6), pages 1-26, June.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:6:p:206-:d:1412617
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    References listed on IDEAS

    as
    1. Aras Bozkurt & Abdulkadir Karadeniz & David Baneres & Ana Elena Guerrero-Roldán & M. Elena Rodríguez, 2021. "Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    2. Tribhuwan Kumar & K. Sakthidasan Sankaran & Mahyudin Ritonga & Shazia Asif & C. Sathiya Kumar & Shoaib Mohammad & Sudhakar Sengan & Evans Asenso & Mukesh Soni, 2022. "Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-7, August.
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    More about this item

    Keywords

    E-FedCloud; Cycle General Adversarial Network (CGAN); Majority Voting-Based Multi-Objective Clustering (MV-MOC); Duelling Deep Q Network (ID2QN);
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics

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