Toward automatic motivator selection for autism behavior intervention therapy

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interven tions results in improvements in behavior and academic pe...

Full description

Saved in:
Bibliographic Details
Main Author: Siyam, Nur (author)
Other Authors: Abdallah, Sherief (author)
Published: 2022
Subjects:
Online Access:https://bspace.buid.ac.ae/handle/1234/3123
https://doi.org/10.1007/s10209-022-00914-7.
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1862980614941048832
author Siyam, Nur
author2 Abdallah, Sherief
author2_role author
author_facet Siyam, Nur
Abdallah, Sherief
author_role author
dc.creator.none.fl_str_mv Siyam, Nur
Abdallah, Sherief
dc.date.none.fl_str_mv 2022
2025-05-24T13:17:59Z
2025-05-24T13:17:59Z
dc.identifier.none.fl_str_mv “Toward automatic motivator selection for autism behavior intervention therapy” (2023) Universal Access in the Information Society, 22(4), pp. 1369–1391.
1615-5289, 1615-5297
https://bspace.buid.ac.ae/handle/1234/3123
https://doi.org/10.1007/s10209-022-00914-7.
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv ProQuest Central
dc.relation.none.fl_str_mv Universal Access in the Information Societyv22 n4 (Nov 2023): 1369-1391
dc.subject.none.fl_str_mv Special education · Autism · Markov decision processes · Reinforcement learning · Behavior intervention · Intervention therapy
dc.title.none.fl_str_mv Toward automatic motivator selection for autism behavior intervention therapy
dc.type.none.fl_str_mv Article
description Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interven tions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most infuential factors impacting the efectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the efectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.
id budr_17298790cf643830190b4e47ffa9cb78
identifier_str_mv “Toward automatic motivator selection for autism behavior intervention therapy” (2023) Universal Access in the Information Society, 22(4), pp. 1369–1391.
1615-5289, 1615-5297
language_invalid_str_mv en
network_acronym_str budr
network_name_str The British University in Dubai repository
oai_identifier_str oai:bspace.buid.ac.ae:1234/3123
publishDate 2022
publisher.none.fl_str_mv ProQuest Central
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling Toward automatic motivator selection for autism behavior intervention therapySiyam, NurAbdallah, SheriefSpecial education · Autism · Markov decision processes · Reinforcement learning · Behavior intervention · Intervention therapyChildren with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interven tions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most infuential factors impacting the efectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the efectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.ProQuest Central2025-05-24T13:17:59Z2025-05-24T13:17:59Z2022Article“Toward automatic motivator selection for autism behavior intervention therapy” (2023) Universal Access in the Information Society, 22(4), pp. 1369–1391.1615-5289, 1615-5297https://bspace.buid.ac.ae/handle/1234/3123https://doi.org/10.1007/s10209-022-00914-7.enUniversal Access in the Information Societyv22 n4 (Nov 2023): 1369-1391oai:bspace.buid.ac.ae:1234/31232025-05-24T13:23:56Z
spellingShingle Toward automatic motivator selection for autism behavior intervention therapy
Siyam, Nur
Special education · Autism · Markov decision processes · Reinforcement learning · Behavior intervention · Intervention therapy
title Toward automatic motivator selection for autism behavior intervention therapy
title_full Toward automatic motivator selection for autism behavior intervention therapy
title_fullStr Toward automatic motivator selection for autism behavior intervention therapy
title_full_unstemmed Toward automatic motivator selection for autism behavior intervention therapy
title_short Toward automatic motivator selection for autism behavior intervention therapy
title_sort Toward automatic motivator selection for autism behavior intervention therapy
topic Special education · Autism · Markov decision processes · Reinforcement learning · Behavior intervention · Intervention therapy
url https://bspace.buid.ac.ae/handle/1234/3123
https://doi.org/10.1007/s10209-022-00914-7.