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An important dimension of classroom group dynamics & collaboration is how much each person contributes to the discussion. With the goal of distinguishing teachers' speech from children's speech and measuring how much each student speaks, we have investigated how automatic speaker diarization can be built to handle real-world classroom group discussions. We examined key design considerations such as the level of granularity of speaker assignment, speech enhancement techniques, voice activity detection, and embedding assignment methods to find an effective configuration. The best speaker diarization system we found was based on the ECAPA-TDNN speaker embedding model and used Whisper automatic speech recognition to identify speech segments. The diarization error rate (DER) in challenging noisy spontaneous classroom data was around 34%, and the correlations of estimated vs. human annotations of how much each student spoke reached 0.62. The accuracy of distinguishing teachers' speech from children's speech was 69.17%. We evaluated the system for potential accuracy bias across people of different skin tones and genders and found that the accuracy did not show statistically significantly differences across either dimension. Thus, the presented diarization system has potential to benefit educational research and to provide teachers and students with useful feedback to better understand their classroom dynamics. 

With the aim to provide teachers with more specific, frequent, and actionable feedback about their teaching, we explore how Large Language Models (LLMs) can be used to estimate "Instructional Support" domain scores of the CLassroom Assessment Scoring System (CLASS), a widely used observation protocol. We design a machine learning architecture that uses either zero-shot prompting of Meta's Llama2, and/or a classic Bag of Words (BoW) model, to classify individual utterances of teachers' speech (transcribed automatically using OpenAI's Whisper) for the presence of Instructional Support. Then, these utterance-level judgments are aggregated over a 15-min observation session to estimate a global CLASS score. Experiments on two CLASS-coded datasets of toddler and pre-kindergarten classrooms indicate that (1) automatic CLASS Instructional Support estimation accuracy using the proposed method (Pearson R up to 0.48) approaches human inter-rater reliability (up to R=0.55); (2) LLMs generally yield slightly greater accuracy than BoW for this task, though the best models often combined features extracted from both LLM and BoW; and (3) for classifying individual utterances, there is still room for improvement of automated methods compared to human-level judgments. Finally, (4) we illustrate how the model's outputs can be visualized at the utterance level to provide teachers with explainable feedback on which utterances were most positively or negatively correlated with specific CLASS dimensions. 

With the goal of supporting real-time AI-based agents to facilitate student collaboration, as well as to enable educational data-mining of group discussions, multimodal classroom analytics, and social network analysis, we investigate how to identify who-is-where-when in classroom videos. We take a person re-identification ( re-id ) approach, and we explore different methods of improving re-id accuracy in the challenging environments of school classrooms. Our results on a multi-grade classroom (MGC) dataset suggest that (1) fine-tuning off-the-shelf person re-id models such as AGW can deliver sizable accuracy gains (from 70.4\\% to 76.7\\% accuracy); (2) clustering, rather than nearest-neighbor identification, can yield accuracy improvements (76.7\\% to 79.4\\%) of identifying each detected person, especially when structural constraints are imposed; and (3) there is a strong benefit to re-id accuracy in obtaining multiple enrollment images from each student. 

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.