Abstract
This PhD project lays the groundwork for a future VLE that adaptively introduces discrepancies (i.e. novel or rule-violating occurrences) in order to support young children with autism spectrum conditions (ASC) in practicing foundational social skills. This paper suggests a taxonomy of discrepancy types and briefly summarises a completed analysis of discrepancy-detection in existing video data from 8 children with ASC using the ECHOES VLE. It then describes planned future work, which will explore possible types of discrepancies for exploratory social content (as present in ECHOES) and address other key questions about how they might impact this group of learners, and be incorporated into the design of a future VLE. It also considers how the current work relates to existing literature on metacognition and use of erroneous worked examples in tutoring systems.
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Alcorn, A.M. (2013). Discrepancy-Detection in Virtual Learning Environments for Young Children with ASC. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_137
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DOI: https://doi.org/10.1007/978-3-642-39112-5_137
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