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There is a growing body of work to characterize elementary engineering classroom talk and its influence on students’ learning. One form of classroom talk is the whole-class conversation, which can be an important site for growth in students’ ideas and ways of thinking about engineering design problems and solutions. With intentional teacher facilitation, whole-class conversations can help students refine their engineering reasoning, consider new ideas, and make new connections between different ways of defining or solving a problem. Participating in these conversations can also help students expand their engineering thinking to include perspectives of care and socio-ethical deliberations. In a multi-year collaboration of classroom teachers and university researchers, we have been enacting and studying five different genres of whole-class engineering Design Talks in first-grade through sixth-grade classrooms: problem scoping talks, idea generation talks, design-in-progress talks, design synthesis talks, and impact talks. As a teacher-researcher community of practice, we have video recorded these “Design Talks” in teachers’ classrooms. These classroom video clips have helped us explore a range of questions about how to structure Design Talks. This paper reports on a qualitative study focused on teacher perceptions of their experiences with Design Talks in their classrooms. Specifically, we ask: How do elementary teachers perceive the benefits and challenges of intentionally facilitated whole-class conversations during engineering design units? Study participants were the six classroom teachers in our Design Talks community of practice. Data sources include field notes from teacher-researcher meetings over three years and teachers’ written responses to open-ended reflection questions. We applied thematic analysis techniques (Braun & Clarke, 2006), including initial coding followed by thematic mapping. We found four themes that characterize how teachers perceive the benefits and challenges of whole-class engineering design conversations. Teachers find that these conversations help them employ asset-based pedagogies while at the same time helping their students synthesize designs and their underlying concepts, take a perspective of care in engineering design, and learn to listen, empathize, and communicate. 

Abstract Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.