Research Article
TuneIn: Framework Design and Implementation for Education Using Dynamic Difficulty Adjustment Based on Deep Reinforcement Learning and Mathematical Approach
@INPROCEEDINGS{10.1007/978-3-030-98005-4_17, author={Alessio Bonti and Manas Palaparthi and Xuemei Jiang and Thien Pham}, title={TuneIn: Framework Design and Implementation for Education Using Dynamic Difficulty Adjustment Based on Deep Reinforcement Learning and Mathematical Approach}, proceedings={Ad Hoc Networks and Tools for IT. 13th EAI International Conference, ADHOCNETS 2021, Virtual Event, December 6--7, 2021, and 16th EAI International Conference, TRIDENTCOM 2021, Virtual Event, November 24, 2021, Proceedings}, proceedings_a={ADHOCNETS \& TRIDENTCOM}, year={2022}, month={3}, keywords={Dynamic Difficulty Adjustment (DDA) DDA deep reinforcement learning Mathematical DDA DDA in education Gamification in education}, doi={10.1007/978-3-030-98005-4_17} }- Alessio Bonti
Manas Palaparthi
Xuemei Jiang
Thien Pham
Year: 2022
TuneIn: Framework Design and Implementation for Education Using Dynamic Difficulty Adjustment Based on Deep Reinforcement Learning and Mathematical Approach
ADHOCNETS & TRIDENTCOM
Springer
DOI: 10.1007/978-3-030-98005-4_17
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.
