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Published on: July 31, 2017
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Toward automatic motivator selection for autism behavior intervention therapy
1Faculty of Engineering and IT, British University in Dubai, Dubai, United Arab Emirates.
Summary
This study uses reinforcement learning to personalize motivators for children with autism spectrum disorder (ASD), improving academic engagement. The AI system adapts to individual needs, enhancing educational interventions for better outcomes.
Area of Science:
- Artificial Intelligence in Education
- Developmental Psychology
- Special Education Technology
Background:
- Children with autism spectrum disorder (ASD) often exhibit low interest in academics and disruptive behaviors during assignments.
- Motivational variables in interventions improve behavior and academic performance in children with ASD, but their effectiveness varies individually.
- Selecting the optimal motivator is crucial but challenging due to individual differences in response to contingent motivators.
Purpose of the Study:
- To address the challenge of selecting effective motivators for children with ASD by employing reinforcement learning.
- To develop an adaptive system that personalizes motivator selection based on influential factors and individual preferences.
- To integrate this motivator selection feature into a mobile application for special education plan coordination.
Main Methods:
- The problem of motivator selection was modeled as a Markov decision process (MDP).
- A Q-learning algorithm was utilized to solve the MDP, considering factors from applied behavior analysis and learner preferences.
- A mobile application was developed to implement the proposed solution and evaluated through a study with educators.
Main Results:
- Preliminary results showed that the motivator selection feature enhanced the usability of the mobile application.
- The Q-learning algorithm demonstrated promising performance, with recommendations improving over time.
- Educators found the feature helpful in their decision-making process for selecting motivators.
Conclusions:
- Reinforcement learning offers a viable approach to personalize motivator selection for children with ASD.
- The developed mobile application and its adaptive feature show potential for improving educational support for children with ASD.
- The system's ability to adapt and improve recommendations over time suggests a promising direction for individualized educational interventions.
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