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Past research shows that teacher noticing matters for student learning, but little is known about the effects of AI-based tools designed to augment teachers’ attention and sensemaking. In this paper, we investigate three multimodal measures of teacher noticing (i.e., gaze, deep dive into learning analytics in a teacher tool, and visits to individual students), gleaned from a mixed reality teacher awareness tool across ten classrooms. Our analysis suggests that of the three noticing measures, deep dive exhibited the largest association with learning gains when adjusting for students’ prior knowledge and tutor interactions. This finding may indicate that teachers identified students most in need based on the deep dive analytics and offered them support. We discuss how these multimodal measures can make the constraints and effects of teacher noticing in human-AI partnered classrooms visible. 

We study students in a college physics lab course encountering a designed-for discrepancy between their data and the relevant physical model. We examine the moment-by- moment dynamics of the groups’ efforts at problematizing and sensemaking, with a specific focus on epistemic agency. Drawing from prior studies of agency – as it relates to learning (Damşa et al., 2010), inquiry (Keifert et al., 2018), and scientific practice (Pickering 1995) – we study the complex ways in which epistemic agency manifests (and doesn’t) in the students’ work. Our analysis suggests several influences on the students’ agency, including features of the social and material context as well as how the students frame what it is they are doing (Hammer et al., 2005), in the lab as a whole and in moments within it. The findings suggest implications and questions with respect to designing labs to support disciplinary practices. 

The study focuses on understanding the discourse, interaction, and problem-solving relating to pedagogical content knowledge (PCK) demonstrated by teachers in one professional training workshop on Computational Thinking (CT) and its implementations in classrooms. 

There are beginnings of research studying how college students manage the experience of not-knowing in instructional laboratories. Previous work in introductory physics focused on student problematizing has had mixed results in activities designed to guide students to recognize and address an apparent discrepancy. Here, we study an instance of a student’s successful problematizing, starting from her initial puzzlement during her group’s exploration of a Newton’s Cradle. We highlight the idiosyncrasy of the instance and suggest it raises questions for curriculum design. 

Although students’ self-regulated learning has been studied extensively, past research has not investigated students’ fine-grained, self regulated choice-making processes during learning with visual representations and strategies to support such processes. We conducted design and experimental studies with 148 students to develop and evaluate an intervention package for supporting students’ self-regulated choice-making in using diagrammatic scaffolding in algebra tutoring software. A classroom experiment showed that students with the intervention learned greater conceptual and procedural knowledge in algebra than students in the control condition whose choices were not supported. Also, students with the intervention chose to use diagrams less frequently overall but showed distinctive use patterns that changed over time, indicating a form of self-regulated diagram use. This study demonstrates the importance of understanding and supporting choice behaviors that change over time during learning, going beyond simply measuring the frequency of choice behaviors and encouraging students to engage in these behaviors more frequently. 

Though the medium of computational modeling presents unique opportunities and challenges for science learning, little research examines how teachers can effectively support students in this work. To address this gap, we investigate how an experienced 6th grade teacher guides her students through programming computational, agent-based models of diffusion. Using interaction analysis of whole-class videos, we define a construct we call ontological alignment in which the teacher facilitates discourse to surface, highlight, connect and seek supporting or contradictory evidence for student ideas in ways that align with the level of analysis available in the modeling tool. We identify two practices reflecting this construct; the teacher 1. primes students to orient to interactions between particles and 2. strategically selects evidence to help discern between student theories. We discuss the pedagogical value of ontological alignment and suggest the identified practices as exemplary for supporting students’ learning through computational modeling.