Effect of Classroom Modification on Attention and Engagement of Students With Autism or Dyspraxia

Kinnealey, Moya; Pfeiffer, Beth; Miller, Jennifer; Roan, Cecilia; Shoener, Rachel Freret; Ellner, Matt L.

doi:10.5014/ajot.2012.004010

Cited by 89 publications

(74 citation statements)

References 19 publications

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“…With respect to intervention studies, no differences in improvement were observed between ASD and DCD groups in attention (Kinnealey et al, 2012; Cosper et al, 2009) or IQ (Kozulin et al, 2010). It is possible that the capacity to train and improve in several domains, especially cognition, is similar for these conditions.…”

Section: Discussionmentioning

confidence: 88%

“…Kinnealey et al (2012) used a quantitative and qualitative mixed-method analysis to explore whether alterations to the sensory environment reduced “non-attending behaviors” in one student with dyspraxia/DCD and three students with ASD. Sound-absorbing walls and halogen lightning were installed in a classroom, and the authors analyzed the frequency of non-attending behaviors captured in videos of class segments.…”

Section: Resultsmentioning

confidence: 99%

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Behavioral comparisons in Autism Spectrum Disorder and Developmental Coordination Disorder: A systematic literature review

Caçola

Miller

Williamson

2017

Research in Autism Spectrum Disorders

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Background Autism Spectrum Disorder (ASD) and Developmental Coordination Disorder (DCD) are developmental disorders that, since the DSM-5, can be diagnosed as co-occurring conditions. While some recent studies suggest that ASD and DCD have similar traits, others show clear behavioral distinctions between the two conditions. By gathering all studies that included (1) an ASD group and a DCD group, (2) an ASD+DCD group and a DCD group, or (3) ASD, ASD+DCD, and DCD groups, we aimed to identify similarities and differences in behaviors between the two disorders. Method We used a systematic search of PubMed (1946 –), Scopus (1970 –), PsycINFO (via EBSCO, 1600 –), CINAHL (via EBSCO, 1937 –), SportDiscus (via EBSCO, 1985 –), and WorldCat (via FirstSearch) in addition to reference list and author name searching PubMed, Scopus, PsycINFO, CINAHL, SportDiscus, and WorldCat to identify original studies that met the following criteria: (1) an ASD group and a DCD group, (2) an ASD+DCD group and a DCD group, or (3) ASD, ASD+DCD, and DCD groups. Results From the 1,598 articles screened, 11 were included in the qualitative analysis. The articles included reported more differences than similarities in individuals with ASD and DCD, with clear distinctions for working memory ability, gestural performance, grip selection, and cortical thickness. Only two studies reported similarities in face processing abilities and perceived competence, and the interventional studies showed group similarities in behavior improvement, such as intelligence and attention. Conclusions Based on the articles reviewed, we conclude that while DCD and ASD share some behavioral symptoms, the symptom profiles of each disorder are unique and separable. We recommend that the evaluation of potential DCD in individuals with ASD be performed systematically and thoroughly, so as to distinguish this co-occurring condition from sensorimotor symptoms associated with ASD.

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Section: Discussionmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Behavioral comparisons in Autism Spectrum Disorder and Developmental Coordination Disorder: A systematic literature review

Caçola

Miller

Williamson

2017

Research in Autism Spectrum Disorders

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“…In the same context, Raca and Dillenbourg () used the synchronization of students' gaze direction and body postures for predicting their self‐reported attention. Attention has been found to be a strong construct of engagement (Kinnealey et al , ; Mundy, Acra, Marshall, & Fox, ). The results showed that students with lower levels of attention were slower in reacting to the teacher than the focused students.…”

Section: Related Work: Utilizing Multimodal Data To Predict Learning mentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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“…These strategies fall into three groupings. First, through modification of the sensory environment, such as changes to noise levels and classroom lighting (Kinnealey et al, 2012) and use of equipment to control sensory inputs, such as headphones (Ashburner, Rodger, Ziviani & Hinder, 2014). Second, task modification such as use of visual schedules (Knight, Sartini & Spriggs, 2015).…”

Section: Introductionmentioning

confidence: 99%

Listening to teachers: Views on delivery of a classroom based sensory intervention for students with autism

Mills

Chapparo

2017

Aus Occup Therapy J

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Background/aim Occupational therapists consider the impact of autism spectrum disorder on occupational performance at school. Occupational therapists work with teachers to support student participation. Atypical sensory processing is common in children with autism. Therefore, collaborating with teachers to enable students with autism to appropriately process sensory information within classrooms may be necessary. This qualitative pilot study aimed to capture teachers' perceptions of using a Sensory Activity Schedule, a sensory based intervention, in the classroom. Methods A qualitative descriptive approach was used to analyse semi‐structured interview responses from 19 qualified teachers who taught children with autism from seven different autism specific special schools in NSW. Teachers were asked about their motivation to complete the intervention as well as helpful and difficult aspects of the intervention. Findings Three main categories and eight sub‐categories were identified from the 19 respondents who reported that helping their students was an important motivation for using a Sensory Activity Schedule as well as the opportunity to evaluate whether sensory based intervention was beneficial. Teachers reported that learning new ideas, working with an occupational therapist and seeing an increase in concentration and a reduction in undesired behaviours were positive aspects of utilising the intervention. Timing, staffing and fidelity of the intervention were areas of concern. Conclusion Collaboration with classroom teachers is an essential part of school‐based occupational therapy. Insights from teachers who implemented a sensory based intervention in the classroom assist occupational therapists to better support students with autism spectrum disorder in schools.

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Effect of Classroom Modification on Attention and Engagement of Students With Autism or Dyspraxia

Cited by 89 publications

References 19 publications

Behavioral comparisons in Autism Spectrum Disorder and Developmental Coordination Disorder: A systematic literature review

Behavioral comparisons in Autism Spectrum Disorder and Developmental Coordination Disorder: A systematic literature review

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Listening to teachers: Views on delivery of a classroom based sensory intervention for students with autism

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