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Hanna Weiers; Felicity Slocombe; Ella James-Brabham; Camilla Gilmore – Infant and Child Development, 2025
Individual differences in mathematical skills emerge early and are influenced by a range of cognitive and environmental factors. One of these is the Home Mathematics Environment (HME), which includes adult-child mathematics talk. Nevertheless, large variations in methods used to investigate and code adult-child mathematics talk exist. We conducted…
Descriptors: Interpersonal Relationship, Adults, Young Children, Correlation
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Alexandra L. Bey; Maura Sabatos-DeVito; Kimberly L. H. Carpenter; Lauren Franz; Jill Howard; Saritha Vermeer; Ryan Simmons; Jesse D. Troy; Geraldine Dawson – Journal of Autism and Developmental Disorders, 2024
Objective, quantitative measures of caregiver-child interaction during play are needed to complement caregiver or examiner ratings for clinical assessment and tracking intervention responses. In this exploratory study, we examined the feasibility of using automated video tracking, Noldus EthoVision XT, to measure 159 2-to-7-year-old autistic…
Descriptors: Autism Spectrum Disorders, Caregiver Child Relationship, Interaction, Video Technology
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Morgan M. Fong; David DeLiema; Virginia J. Flood; Oia Walker-van Aalst – International Journal of Computer-Supported Collaborative Learning, 2025
Working solutions to problems are not definitive end points. As a result, code that is technically correct can still be treated as needing revising -- a practice in computer programming known as refactoring. We document how late elementary to middle school students and their undergraduate instructors weigh the possibility of refactoring working…
Descriptors: Computation, Thinking Skills, Norms, Computer Science Education
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Eunhye Shin – Journal of Computer Assisted Learning, 2025
Background: Analysing classroom dialogue is a widely used approach for understanding students' learning, often requiring team-based collaborative research. This presents a challenge for single researchers due to the labour-intensive nature of the process. Emerging advancements in large language models (LLMs) such as ChatGPT, enhance qualitative…
Descriptors: Artificial Intelligence, Technology Uses in Education, Science Education, Coding
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David DeLiema; Jeffrey K. Bye; Vijay Marupudi – ACM Transactions on Computing Education, 2024
Learning to respond to a computer program that is not working as intended is often characterized as finding a singular bug causing a singular problem. This framing underemphasizes the wide range of ways that students and teachers could notice discrepancies from their intention, propose causes of those discrepancies, and implement interventions.…
Descriptors: Computer Software, Troubleshooting, Intention, Intervention