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  1. Abstract

    The Institute for Student‐AI Teaming (iSAT) addresses the foundational question:how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.

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    Free, publicly-accessible full text available March 1, 2025
  2. Rural students, schools, and communities have unique challenges that hinder academic achievement, growth, and opportunities, compared to other locales. While there is a need to study this community more, there is also a pressing need to bring the local community members together to support the future generation of learners in developing pathways that lead them to future career opportunities. This article focuses on how a Research Practice Partnership (RPP) can be developed in rural communities to support STEM pathways for local middle-school youth. RPPs are often described as long-term collaborations between both researchers and practitioners in which the participating partners leverage research to address specific persistent problems of practice. We present findings from a developing design-based RPP focused on bringing community members and organizations together to co-design opportunities for underserved youth in rural mountain communities. 
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  3. “Talk moves” are specific discursive strategies used by teachers and students to facilitate conversations in which students share their thinking, and actively consider the ideas of others, and engage in rich discussions. Experts in instructional practices often rely on cues to identify and document these strategies, for example by annotating classroom transcripts. Prior efforts to develop automated systems to classify teacher talk moves using transformers achieved a performance of 76.32% F1. In this paper, we investigate the feasibility of using enriched contextual cues to improve model performance. We applied state-of-the-art deep learning approaches for Natural Language Processing (NLP), including Robustly optimized bidirectional encoder representations from transformers (Roberta) with a special input representation that supports previous and subsequent utterances as context for talk moves classification. We worked with the publically available TalkMoves dataset, which contains utterances sourced from real-world classroom sessions (human- transcribed and annotated). Through a series of experimentations, we found that a combination of previous and subsequent utterances improved the transformers’ ability to differentiate talk moves (by 2.6% F1). These results constitute a new state of the art over previously published results and provide actionable insights to those in the broader NLP community who are working to develop similar transformer-based classification models. 
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  4. null (Ed.)
    This paper describes the design and classroom implementation of a week-long unit that aims to integrate computational thinking (CT) into middle school science classes using programmable sensor technology. The goals of this sensor immersion unit are to help students understand why and how to use sensor and visualization technology as a powerful data-driven tool for scientific inquiry in ways that align with modern scientific practice. The sensor immersion unit is anchored in the investigation of classroom data where students engage with the sensor technology to ask questions about and design displays of the collected data. Students first generate questions about how data data displays work and then proceed through a set of programming exercises to help them understand how to collect and display data collected from their classrooms by building their own mini data displays. Throughout the unit students draw and update their hand drawn models representing their current understanding of how the data displays work. The sensor immersion unit was implemented by ten middle school science teachers during the 2019/2020 school year. Student drawn models of the classroom data displays from four of these teachers were analyzed to examine students’ understandings in four areas: func- tion of sensor components, process models of data flow, design of data displays, and control of the display. Students showed the best understanding when describing sensor components. Students exhibited greater confusion when describing the process of how data streams moved through displays and how programming controlled the data displays. 
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  5. null (Ed.)
    Engaging in physical computing activities involving both hard- ware and software provides a hands-on introduction to computer science. The move to remote learning for primary and secondary schools during the 2020-2021 school year due to COVID-19 made implementing physical computing activities especially challenging. However, it is important that these activities are not simply eliminated from the curriculum. This paper explores how a unit centered around students investigating how programmable sensors that can support data-driven scientific inquiry was collaboratively adapted for remote instruction. A case study of one teacher’s experience implementing the unit with a group of middle school students (ages 11 to 14) in her STEM elective class examines how her students could still engage in computational thinking practices around data and programming. The discussion includes both the challenges and unexpected affordances of engaging in physical computing activities remotely that emerged from her implementation. 
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