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In introductory physics laboratory instruction, students often expect to confirm or demonstrate textbook physics concepts. This expectation is largely undesirable: labs that emphasize confirmation of textbook physics concepts are generally unsuccessful at teaching those concepts and even in contexts that do not emphasize confirmation, such expectations can lead to students disregarding or manipulating their data in order to obtain the expected result. In other words, when students expect their lab activities to confirm a known result, they may relinquish epistemic agency and violate disciplinary practices. We present a contrasting case where, we claim, confirmatory expectations can actually support productive disciplinary engagement. In this case study, we analyze the complex dynamics of students’ epistemological framing in a lab where students’ confirmatory expectations support and even generate epistemic agency and disciplinary practices, including developing original ideas, measures, and apparatuses to apply to the material world. Published by the American Physical Society2024 

Qualitative analysis of verbal data is of central importance in the learning sciences. It is labor-intensive and time-consuming, however, which limits the amount of data researchers can include in studies. This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning. We start with a set of lab reports from an undergraduate biology course, scored by a four-level scheme that considers the complexity of argument structure, the scope of evidence, and the care and nuance of conclusions. Using this set of labeled data, we show that a popular natural language modeling processing pipeline, namely vector representation of words, a.k.a word embeddings, followed by Long Short Term Memory (LSTM) model for capturing language generation as a state-space model, is able to quantitatively capture the scoring, with a high Quadratic Weighted Kappa (QWK) prediction score, when trained in via a novel contrastive learning set-up. We show that the ML algorithm approached the inter-rater reliability of human analysis. Ultimately, we conclude, that machine learning (ML) for natural language processing (NLP) holds promise for assisting learning sciences researchers in conducting qualitative studies at much larger scales than is currently possible. 

Abstract While research shows that responsive teaching fosters students' disciplinary learning and equitable opportunities for participation, there is yet much to know about how teachers come to be responsive to their students' experiences in the science classroom. In this work, we set out to examine whether and how engaging teachersas learnersin doing science may support responsive instructional practices. We draw on data from a year‐long blended‐online science professional development (PD) program that began with an emphasis on teachers' doing science and progressed to supporting their attention to their students' doing science. By analyzing videos from teachers' classrooms collected throughout the PD, we found that teachers became more stable in attending and responding to their students' thinking. In this article, we present evidence from teachers' reflections that this stability was supported by the teachers' intellectual and emotional experiences as learners. Specifically, we argue that engaging in extended scientific inquiry provided a basis for the teachers havingepistemic empathyfor their students—their tuning into and appreciating their students'intellectualandemotionalexperiences in science, which in turn supported teachers' responsiveness in the classroom.