Marcos Daniel Caballero

Equity-focused coaching—coaching that centers racism, patriarchy, and other oppressive systems—is a promising approach to supporting teachers in expanding learning opportunities for minoritized students. However, despite its known challenges, details of equity-focused coaching remain underexamined. This article presents an analysis of 27 equity-focused coaching sessions with ten secondary physics teachers, which incorporated classroom videos and EQUIP analytics. The analysis applied theoretical constructs from conversation analysis: attribution of responsibility and attribution of knowledge in interaction. Findings show that whereas coaches tended to explain inequitable patterns in terms of broader social patterns, teachers tended to base their interpretations on the perceived deficits of minoritized students. The turn-by-turn analysis illustrates fine-grained details of discursive moves coaches leveraged to successfully facilitate shifts in teachers’ interpretative orientations. These findings highlight that steering coaching conversations toward equity-focused interpretations is an essential element of equity-focused coaching, and such work comprises subtle and skillful discursive moves.

Modern science fields rely on computation to explore and make sense of the natural world. Practices of computation—such as modeling complex systems and mining data as a primary tool of discovery—ha...

The Partnership for Integration of Computation into Undergraduate Physics (PICUP) was founded in the mid-2010s to assist educators with the challenges of integrating computation into physics curricula. In addition to in-person trainings and hosted educational materials, PICUP uses a Slack Workspace to continue collaboration and discussion off-line. In this work, we use social network analysis (SNA) to study the communication patterns of PICUP and assess if PICUP is meeting their goals in the Slack environment. Through our analysis, we discuss PICUP’s community structure and define a conceptual framework to evaluate if the goals are being met through SNA metrics. We present a comprehensive analysis of eight channels in the Slack Workspace using various SNA metrics, identifying three distinct levels of user engagement. We conclude with implications for PICUP and provide recommendations for the community.

About 15 years ago, education researchers conducted a systematic literature review (SLR) on change strategies for improving undergraduate STEM education instruction. Analyzing 191 articles from 1995 to 2008, the researchers identified four broad categories of change strategies through a comprehensive interdisciplinary literature review: (1) disseminating curriculum and pedagogy, (2) developing reflective teachers, (3) enacting policy, and (4) developing a shared vision. With recent developments in STEM education practices, it is imperative to conduct a follow-up SLR comparing the effects of change strategies and associated student success. Similarly, with the influx of scientific articles published in recent decades, it is time consuming to conduct comprehensive SLRs without the assistance of machine learning (ML) analysis techniques. Working with qualitative researchers, we investigate the impact and ability of using ML to assist in these analyses. While most ML approaches can be easily written with a few lines of code, using them to extract meaningful information for literature reviews is challenging. In this work, we describe our experience with integrating machine learning techniques into the analysis pipeline of SLRs. Specifically, this poster will: (1) share results from clustering analysis to identify themes of the chosen abstracts, (2) explore the effects of data bias on found clusters, (3) present the challenges of adding machine learning into SLRs, and (4) assess if the addition of ML in SLRs aligns with the expected goals of qualitative researchers.

The Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE) was designed to measure students’ proficiency with measurement uncertainty concepts and practices across ten different assessment objectives to help facilitate the improvement of laboratory instruction focused on this important topic. To ensure the reliability and validity of this assessment, we conducted a comprehensive statistical analysis using classical test theory. This analysis includes an evaluation of the test as a whole, as well as an in-depth examination of individual items and assessment objectives. We make use of a previously reported on scoring scheme involving pairing items with assessment objectives, creating a new unit for statistical analysis referred to as a “couplet.” The findings from our analysis provide evidence for the reliability and validity of SPRUCE as an assessment tool for undergraduate physics labs. This increases both instructors’ and researchers’ confidence in using SPRUCE for measuring students’ proficiency with measurement uncertainty concepts and practices to ultimately improve laboratory instruction. Additionally, our results using couplets and assessment objectives demonstrate how these can be used with traditional classic test theory analysis. Published by the American Physical Society 2024

Concepts and practices surrounding measurement uncertainty are vital knowledge for physicists and are often emphasized in undergraduate physics laboratory courses. We have previously developed a research-based assessment instrument—the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE)—to examine student proficiency with measurement uncertainty along a variety of axes, including sources of uncertainty, handling of uncertainty, and distributions and repeated measurements. We present here initial results from the assessment representing over 1500 students from 20 institutions. We analyze students’ performance pre- and postinstruction in lab courses and examine how instruction impacts students with different majors and gender. We find that students typically excel in certain areas, such as reporting the mean of a distribution as their result, while they struggle in other areas, such as comparing measurements with uncertainty and correctly propagating errors using formulas. Additionally, we find that the importance that an instructor places in certain areas of measurement uncertainty is uncorrelated with student performance in those areas. Published by the American Physical Society 2024

In recent years, much of the emphasis for transformation of introductory STEM courses has focused on “active learning”, and while this approach has been shown to produce more equitable outcomes for students, the construct of “active learning” is somewhat ill-defined and is often used as a “catch-all” that can encompass a wide range of pedagogical techniques. Here we present an alternative approach for how to think about the transformation of STEM courses that focuses instead on what students should know and what they can do with that knowledge. This approach, known as three-dimensional learning (3DL), emerged from the National Academy’s “A Framework for K-12 Science Education”, which describes a vision for science education that centers the role of constructing productive causal accounts for phenomena. Over the past 10 years, we have collected data from introductory biology, chemistry, and physics courses to assess the impact of such a transformation on higher education courses. Here we report on an analysis of video data of class sessions that allows us to characterize these sessions as active, 3D, neither, or both 3D and active. We find that 3D classes are likely to also involve student engagement (i.e. be active), but the reverse is not necessarily true. That is, focusing on transformations involving 3DL also tends to increase student engagement, whereas focusing solely on student engagement might result in courses where students are engaged in activities that do not involve meaningful engagement with core ideas of the discipline.