Increased emphasis has been given to students' engagement with experimental data as reform efforts have continued to transform the landscape of introductory physics laboratory courses by providing greater opportunities for authentic scientific inquiry and student agency. As a result, students become the primary driving forces of their own experimentation, and the manner in which they engage with experimental data becomes more dynamic and nonlinear. This study presents ongoing efforts to illuminate the nuanced ways students enact various data-based actions when engaging in physics laboratory experiments. In this paper, we present a single case-study analysis of a student group engaging in an inquiry-based physics laboratory to highlight the dynamic and iterative ways the group shifts between multiple data-based actions when expected to be engaging in a single laboratory task. Research data comes from audio and video files of students' computers while they engaged in lab experimentation, coded using a constructivist grounded theory approach to identify multiple data-based actions performed by the students. Results of this case study show that students oftentimes shift between multiple data-based actions on short timescales and that these shifts can take place with implicit iterative patterns, even when the instructional setting is structured for a single experimental task.

Three-dimensional learning (3DL) is an approach to science instruction that was developed for K-12 science education and that can provide guidance for improving undergraduate physics laboratories. In this paper, we describe efforts to comprehensively integrate 3DL into a sequence of undergraduate introductory physics for life sciences (IPLS) laboratory courses. This paper is tailored for introductory physics faculty interested in advancing their course's learning goals by simultaneously engaging students in experimental practices, scientific reasoning, and conceptual knowledge. We first review how several well-known laboratory curricula are already implicitly aligned with 3DL. We then describe our IPLS course sequence and show how each 3DL dimension—science and engineering practices, disciplinary core ideas, and crosscutting concepts—is integrated throughout the curriculum. To support implementation, we provide samples of our course documentation, a detailed account of our 3DL integration efforts, a guide to training and supporting teaching and learning assistants in a 3DL course, and a sample set of activities to guide students in participating in 3DL instruction in the supplementary material.

Generating graphical representations is an essential skill for productive student engagement in physics laboratory settings, and is a key component in developing representational competency (RC). As physics lab courses have been reformed to prioritize student engagement in authentic scientific skills and practices, students experience additional freedom to decide what data to include in graphs and what types of graph(s) would allow for appropriate sensemaking towards answering experimental questions. With this, however, there is a dearth of PER literature highlighting the strategies students use while working to generate graphs using their own experimental data. This paper presents a case study analysis of a student group's lab investigation to call attention to how students enact various productive strategies when working towards generating graphical representations in an introductory physics laboratory course. Results of this case study analysis identify three productive strategies students enact when working to generate graphs in lab settings, each of which is related to aspects of representational competency (RC): 1) identifying (potential) covarying quantities; 2) choosing representative data subsets suitable for representation; and 3) iteratively reducing data and generating graphs to assess graph's viability in answering research questions. Our analysis also shows how students frequently refer back to their experimental goals and hypotheses when deciding what strategies to enact to generate graphs.

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