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TuneIn: Framework Design and Implementation for Education Using Dynamic Difficulty Adjustment Based on Deep Reinforcement Learning and Mathematical Approach

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Ad Hoc Networks and Tools for IT (ADHOCNETS 2021, TridentCom 2021)

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Abstract

Education, personal self-development, and overall learning have vastly changed over the years as a result of historical events, methodologies, and technologies. As students first, and then as educators, we have only seen slight changes in the delivery of educational content, with the most accepted model being “one system fits all”, we have seen content and delivery mediums, but little about differentiating or personalizing the education experience. We challenge this traditional model by implementing an Adaptive Training Framework based on AI techniques through a Dynamic Difficulty Adjustment agent. We have conducted a limited sample size experiment to prove that personalized content allows the learner to achieve more than a static model.

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Author information

Authors and Affiliations

  1. Deakin University, Burwood, VIC, 3125, Australia

    Alessio Bonti, Manas Palaparthi, Xuemei Jiang & Thien Pham

Authors
  1. Alessio Bonti
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  2. Manas Palaparthi
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  3. Xuemei Jiang
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  4. Thien Pham
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Corresponding author

Correspondence to Alessio Bonti .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Sydney, Camperdown, NSW, Australia

    Wei Bao

  2. Monash University, Clayton, VIC, Australia

    Xingliang Yuan

  3. School of Information Technology, Deakin University, Mont Albert, VIC, Australia

    Longxiang Gao

  4. School of Cyber Engineering, Xidian University, Xi'an, China

    Tom H. Luan

  5. School of Electronic Engineering, School of Computer Science and Engineering, Soongsil University, Dongjak, Seoul, Korea (Republic of)

    David Bong Jun Choi

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Bonti, A., Palaparthi, M., Jiang, X., Pham, T. (2022). TuneIn: Framework Design and Implementation for Education Using Dynamic Difficulty Adjustment Based on Deep Reinforcement Learning and Mathematical Approach. In: Bao, W., Yuan, X., Gao, L., Luan, T.H., Choi, D.B.J. (eds) Ad Hoc Networks and Tools for IT. ADHOCNETS TridentCom 2021 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-030-98005-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-98005-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98004-7

  • Online ISBN: 978-3-030-98005-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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