Authors:
Zeina Thabet
;
Hurmat Ansari
;
Sara Albashtawi
;
Nur Siyam
and
Sherief Abdallah
Affiliation:
Faculty of Engineering and IT, British University in Dubai, Academic City, Dubai, U.A.E.
Keyword(s):
Special Education, Autism, Deep Reinforcement Learning, Behaviour Intervention, Mobile Application.
Abstract:
Apart from difficulties with social communication, children with autism spectrum disorder (ASD) tend to have limited interest in academic activities. The challenges faced by the educators of these students are abundant, including selecting motivating items or activities that can prompt them to complete a task. In addition to this challenge, the educators also face the issue of the lack of coordination between the teachers, therapists, and parents. This issue is imperative as significant learning opportunities are lost for lack of communication. To address these two issues, we have created a distributed system consisting of a mobile application that tracks the academic objectives and behavioural progress of the students which allows for a centralized place of information for easier coordination between educators, as well as suggesting effective motivators using a Deep Neural Network (DNN), specifically a Deep Q Network, to help autistic students regain their focus in the class. The De
ep Q Network is constructed with a custom environment that takes in the state as input and then, based on the current state, calculates the best motivator to suggest. The mobile application was created with an aim of assisting school educators in tracking a student’s progress. Moreover, the system includes a staff dashboard to manage users and provide visualizations depicting students’ progress. This project is the first of its kind and will help educators select effective motivators in moments that the students need them as well as aid the flow of information between the stakeholders.
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