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Affect-Learn: An IoT-based Affective Learning Framework for Special Education
A learning disorder is associated with the ability of the child to process the information effectively. The purpose of special education is to provide equal access to education for all children to help them succeed in the regular curriculum through specialized services. Children with anxiety, hyperactive, or attention-deficit disorders require special assistance to help them stay at their normal level and thus effectively suit in a classroom setting. With the advancement in technology, the landscape of special education is rapidly changing. The motivation for this research is to develop an Internet of Things-based affective computing framework, Affect-learn, that can help teachers in identifying the hyperactivity or inattentiveness in children, and help them improve the overall learning outcomes. The proposed research is validated with the help of commercially available off the shelf components. The measure of success in this research is the response time of the proposed framework and the efficiency of emotion elicitation.
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- Award ID(s):
- 1924117
- PAR ID:
- 10157998
- Date Published:
- Journal Name:
- IEEE Virtual World Forum on Internet of Things 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Speech-language pathologists are familiar with eligibility criteria for school-based special education services under IDEA, the Individuals with Disabilities Education Act. In order for children with speech and language disorders to be eligible for services, they need to fit one of the thirteen categories of disabilities. However, these 13 categories do not always align well with current evidence-based diagnoses of neurodiverse conditions. It is because of these challenges that we, as members of the National Artificial Intelligence Institute for Exceptional Education, are particularly grateful to the US Department of Education's Office of Special Education programs for issuing new guidance on the use of DLD to accurately describe the speech and language needs of individual children, no matter what eligibility category they fall into. We are also grateful to members and leaders of the American Speech-Language-Hearing Association for their strong advocacy to raise the community's awareness of this new guidance. Therefore, our Institute will be another strong advocate for children with DLD so that they can eventually benefit from our Institute's research. We believe that the recognition of DLD as a disability can greatly help these children and their families.
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null (Ed.)Abstract: Educator preparation programs have moved away from o ering interest-based courses that prepare a teacher candidate on a more surface level and have opted to integrate more authentic experiences with technology that are infused into coursework. This research study focused on redesigning key courses in both the general and special education graduate-level educator preparation programs (EPPs) to infuse learning experiences through a simulated learning environment (Mursion) to help bridge teacher candidates’ coursework and field experiences, o ering them robust experience with high leverage practices and technology that increases their own competency. Data from this study demonstrated that preservice teacher candidate work within the Mursion simulated learning environment increased use of high leverage practices related to strategic teaching, collaboration, differentiation, and providing feedback. Implications for instructional coaching, microteaching, repeated practice, and closing the research to practice gap are discussed.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
