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

Version 2 2024-06-05, 12:38
Version 1 2022-04-12, 08:16
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posted on 2024-06-05, 12:38 authored by A Bonti, Manas PalaparthiManas Palaparthi, Xuemei JiangXuemei Jiang, Jason PhamJason Pham
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.

History

Volume

428

Chapter number

17

Pagination

229-241

ISSN

1867-8211

eISSN

1867-822X

ISBN-13

9783030980047

Language

eng

Publication classification

E1 Full written paper - refereed

Extent

23

Editor/Contributor(s)

Bao W, Yuan X, Gao L, Luan TH, Bong Jun Choi D

Publisher

Springer

Place of publication

Berlin, Germany

Title of book

Ad Hoc Networks and Tools for IT

Series

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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