The Effects of Self-Monitoring Strategy Based on Self-Determined Learning Model of Instruction on Academic on-task Behavior for a Student with Autism Spectrum Disorder

2021 ◽  
Vol 23 ◽  
pp. 207-228
Author(s):  
Eun Hee Park ◽  
Hee Jung Lim ◽  
Surn Hee Lee ◽  
Eunhee Paik
Keyword(s):  
Autism Spectrum Disorder ◽  
Learning Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Monitoring Strategy ◽  
Self Monitoring ◽  
Task Behavior ◽  
On Task Behavior

Culturally Focused Classroom Staff Training to Increase Praise for Students With Autism Spectrum Disorder in Ghana

2020 ◽  
pp. 109830072092935
Author(s):  
Ashley Elizabeth Knochel ◽  
Kwang-Sun Cho Blair ◽  
Rachel Sofarelli
Keyword(s):  
Autism Spectrum Disorder ◽  
Performance Feedback ◽  
Staff Training ◽  
Autism Spectrum ◽  
Self Monitoring ◽  
Task Behavior ◽  
Culturally Adapted ◽  
Students With Autism ◽  
On Task Behavior ◽  
And Performance

This study examined the impact of culturally focused classroom staff training on delivery of praise and its collateral effects on student on-task behavior. Training involved self-monitoring and performance feedback to promote staff delivery of culturally adapted praise to students. Four classroom staff and four students with autism spectrum disorder (ASD) in Ghana participated in the study. The outcomes of the study were evaluated using a concurrent multiple baseline across participants (dyads) design with an ABC sequence. Results indicated that staff training using self-monitoring and performance feedback procedures successfully increased staff delivery of behavior-specific praise, but the procedures did not produce desired student outcomes. Culturally relevant adaptations to the topography of praise and implementation support were necessary to improve on-task behavior. This experiment provides an impetus for further examination of how common behavior-analytic training procedures can be culturally adapted for children with ASD in non-Western contexts.

Using WatchMinder to Increase the On-Task Behavior of Students with Autism Spectrum Disorder

2014 ◽  
Vol 45 (5) ◽  
pp. 1408-1418 ◽  
Author(s):  
Lisa Finn ◽  
Rangasamy Ramasamy ◽  
Charles Dukes ◽  
John Scott
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Task Behavior ◽  
Students With Autism ◽  
On Task Behavior

scholarly journals Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning

2021 ◽  
Author(s):  
Xin Yang ◽  
Ning Zhang ◽  
Donglin Wang
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Dictionary Learning ◽  
Learning Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Functional Networks ◽  
Training Samples ◽  
Group Ica

The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically developing (TD) participants using the functional connectivity calculated from the derived functional networks. In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. We downloaded a total of 120 training samples, including 58 ASD and 62 TD participants, which were obtained from the public repository: Autism Brain Imaging Data Exchange I (ABIDE I). Our methodology and results have five main parts. First, we utilize a group ICA model to extract functional networks from the ASD group and rank the top 20 regions of interest (ROIs). Second, we utilize a dictionary learning model to extract functional networks from the ASD group and rank the top 20 ROIs. Third, we merged the 40 selected ROIs from the two models together as the ASD functional networks. Fourth, we generate three corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs selected from dictionary learning, and the 40 combined ROIs selected from both. Finally, we extract ROIs for all training samples using the above three masks, and the calculated functional connectivity was used as features for ASD and TD classification. The classification results showed that the functional networks derived from ICA and dictionary learning together outperform those derived from a single ICA model or a single dictionary learning model.

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