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Effective feedback is essential for refining instructional practices in mathematics education, and researchers often turn to advanced natural language processing (NLP) models to analyze classroom dialogues from multiple perspectives. However, utterance-level discourse analysis encounters two primary challenges: (1) multi-functionality, where a single utterance may serve multiple purposes that a single tag cannot capture, and (2) the exclusion of many utterances from domain-specific discourse move classifications, leading to their omission in feedback. To address these challenges, we proposed a multi-perspective discourse analysis that integrates domain-specific talk moves with dialogue act (using the flattened multi-functional SWBD-MASL schema with 43 tags) and discourse relation (applying Segmented Discourse Representation Theory with 16 relations). Our top-down analysis framework enables a comprehensive understanding of utterances that contain talk moves, as well as utterances that do not contain talk moves. This is applied to two mathematics education datasets: TalkMoves (teaching) and SAGA22 (tutoring). Through distributional unigram analysis, sequential talk move analysis, and multi-view deep dive, we discovered meaningful discourse patterns, and revealed the vital role of utterances without talk moves, demonstrating that these utterances, far from being mere fillers, serve crucial functions in guiding, acknowledging, and structuring classroom discourse. These insights underscore the importance of incorporating discourse relations and dialogue acts into AI-assisted education systems to enhance feedback and create more responsive learning environments. Our framework may prove helpful for providing human educator feedback, but also aiding in the development of AI agents that can effectively emulate the roles of both educators and students. 

This study examined the ways in which an equity analytics tool — the SEET system — supported middle school science teachers’ reflections on the experiences of diverse students in their classrooms. The tool provides teachers with “equity visualizations” — disaggregated classroom data by gender and race/ethnicity — designed to support teachers to notice and reflect on inequitable patterns in student participation in classroom knowledge-building activities, as well as “whole class visualizations” that enable teachers to look at participation patterns. The visualizations were based on survey data collected from students reflecting on the day’s lessons, responding to questions aligned with three theoretical constructs indicative of equitable participation in science classrooms: coherence, relevance, and contribution. The study involved 42 teachers, divided into two cohorts, participating in a two-month professional learning series. Diary studies and semi-structured interviews were used to probe teachers’ perceptions of the visualizations’ usability, usefulness, and utility for supporting their reflections on student experiences and instructional practices. A key result is that only the “equity visualizations” prompted teacher reflections on diverse student experiences. However, despite the support equity visualizations provided for this core task, the teachers consistently ranked the whole class visualizations as more usable and useful. 

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. 

“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. 

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. 

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.