IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i6p206-d1412617.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/6/206/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/6/206/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gizéh Rangel-de Lázaro & Josep M. Duart, 2023. "You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    2. Dawool Jung & Sungeun Suh, 2024. "Enhancing Soft Skills through Generative AI in Sustainable Fashion Textile Design Education," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    3. Daina Gudoniene & Evelina Staneviciene & Vytautas Buksnaitis & Nicola Daley, 2023. "The Scenarios of Artificial Intelligence and Wireframes Implementation in Engineering Education," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    4. Jennie C. De Gagne, 2023. "The State of Artificial Intelligence in Nursing Education: Past, Present, and Future Directions," IJERPH, MDPI, vol. 20(6), pages 1-4, March.
    5. Aleksandra Klašnja-Milićević & Mirjana Ivanović, 2021. "E-learning Personalization Systems and Sustainable Education," Sustainability, MDPI, vol. 13(12), pages 1-6, June.
    6. Ajda Fošner, 2024. "University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices," Sustainability, MDPI, vol. 16(19), pages 1-15, October.
    7. Sunghwan Hwang, 2022. "Examining the Effects of Artificial Intelligence on Elementary Students’ Mathematics Achievement: A Meta-Analysis," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    8. Florin-Valeriu Pantelimon & Razvan Bologa & Andrei Toma & Bogdan-Stefan Posedaru, 2021. "The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(23), pages 1-10, December.
    9. Chih-Chang Yu & Yufeng (Leon) Wu, 2021. "Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    10. Rocsana Bucea-Manea-Țoniş & Valentin Kuleto & Simona Corina Dobre Gudei & Costin Lianu & Cosmin Lianu & Milena P. Ilić & Dan Păun, 2022. "Artificial Intelligence Potential in Higher Education Institutions Enhanced Learning Environment in Romania and Serbia," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    11. Rustam Shadiev & Wayan Sintawati & Nurassyl Kerimbayev & Fahriye Altinay, 2024. "Systematic Review (2003–2023): Exploring Technology-Supported Cross-Cultural Learning through Review Studies," Sustainability, MDPI, vol. 16(2), pages 1-36, January.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:6:p:206-:d:1412617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.