arXiv subjects

Justin Gambrell

Publications and source records attributed to Justin Gambrell.

7 recordsLinked to original sources

arXiv2026

Strategies for Collecting Multi-Institutional Data in Discipline-Based Education Research

Multi-institutional studies are critical for advancing discipline-based education research (DBER) because they allow us to determine where and for whom research findings are applicable. Despite this benefit, such studies remain relatively rare due to the complexities of coordinating data collection across different institutions. In this paper, we describe key challenges and propose actionable strategies for implementing multi-institutional DBER studies. We focus on navigating Institutional Review Board procedures, recruiting participants from a range of institution types, standardizing data sources across institutions, and managing logistics. We also provide an applied example of these strategies from a national research project in which we collected concept inventory data, social network surveys, and classroom observations from 31 introductory physics instructors at 28 institutions in the United States.

physics.ed-ph↗

arXiv2025

Measuring fidelity of implementation of named active learning methods in physics

Various active learning methods have been developed for introductory physics, and these methods are increasingly being adopted by instructors. However, instructors often do not implement these methods exactly as was originally intended by the developers, as they may face issues related to funding and institutional support for active learning and/or have different instructional contexts (e.g., student populations) and environments (e.g., physical classroom layouts) than the developers. Existing research does not sufficiently capture the range of variation in instructor implementation of established active learning methods, especially in comparison to high-fidelity implementations. In this study, we first identify the critical components (i.e., components without which the active learning method cannot be said to have been implemented) of three named active learning methods: SCALE-UP, ISLE, and Tutorials. We then evaluate the fidelity with which 18 different introductory physics instructors implement these methods by analyzing classroom observations and comparing the extent to which these broader implementations use each critical component in their classroom to high-fidelity implementations. We find across all three active learning methods that broader implementations spend similar amounts of class time on the critical components as high-fidelity implementations. At the same time, we observe substantial variation in the specific styles that broader implementers operationalize these critical components (e.g., doing a few long activities versus many short activities). Finally, we find no clear relationship between fidelity of implementation and student conceptual learning gains for our study's sample of instructors, providing preliminary evidence that different ways of implementing the critical components of active learning method may all effectively improve student understanding.

physics.ed-ph↗

arXiv2025

Beyond named methods: A typology of active learning based on classroom observation networks

A growing number of introductory physics instructors are implementing active learning methods in their classrooms, and they are modifying the methods to fit their local instructional contexts. However, we lack a detailed framework for describing the range of what these instructor adaptations of active learning methods look like in practice. Existing studies apply structured protocols to classroom observations and report descriptive statistics, but this approach overlooks the complex nature of instruction. In this study, we apply network analysis to classroom observations to define a typology of active learning that considers the temporal and interactional nature of instructional practices. We use video data from 30 instructors at 27 institutions who implemented one of the following named active learning methods in their introductory physics or astronomy course: Investigative Science Learning Environment (ISLE), Peer Instruction, Tutorials, and Student-Centered Active Learning Environment with Upside-down Pedagogies (SCALE-UP). We identify five types of active learning instruction: clicker lecture, dialogic clicker lecture, dialogic lecture with short groupwork activities, short groupwork activities, and long groupwork activities. We find no significant relationship between these instruction types and the named active learning methods; instead, implementations of each of the four methods are spread across different instruction types. This result prompts a shift in the way we think and talk about active learning: the names of developed active learning methods may not actually reflect the specific activities that happen during instruction. We also find that student conceptual learning does not vary across the identified instruction types, suggesting that instructors may be flexible when modifying these methods without sacrificing effectiveness.

physics.ed-ph↗

arXiv2025

Multi-institutional assessment of Peer Instruction implementation and impacts using the Framework for Interactive Learning in Lectures

Substantial research indicates that active learning methods improve student learning more than traditional lecturing. Accordingly, current studies aim to characterize and evaluate different instructors' implementations of active learning methods. Peer Instruction is one of the most commonly used active learning methods in undergraduate physics instruction and typically involves the use of classroom response systems (e.g., clickers) where instructors pose conceptual questions that students answer individually and/or in collaboration with nearby peers. Several research studies have identified that different instructors vary in the ways they implement Peer Instruction (e.g., the time they give students to answer a question and the time they spend explaining the correct answer); however, these studies only take place at a single institution and do not relate the implementation of Peer Instruction to student learning. In this study, we analyze variation in both the implementation and impacts of Peer Instruction. We use classroom video observations and conceptual inventory data from seven introductory physics instructors across six U.S. institutions. We characterize implementation using the Framework for Interactive Learning in Lectures (FILL+), which classifies classroom activities as interactive (e.g., clicker questions), vicarious interactive (e.g., individual students asking a question), or non-interactive (e.g., instructor lecturing). Our preliminary results suggest that instructors who use both interactive and vicarious interactive strategies may exhibit larger student learning gains than instructors who predominantly use only one of the two strategies.

physics.ed-ph↗

arXiv2025

Evaluating recognition and recall formats of social network surveys in physics education research

An increasing number of studies in physics education research use social network analysis to quantify interactions among students. These studies typically gather data through online surveys using one of two different survey formats: recognition, where students select peers' names from a provided course roster, and recall, where students type their peers' names from memory as an open response. These survey formats, however, may be subject to two possible systematic errors. First, students may report more peers' names on a recognition survey than a recall survey because the course roster facilitates their memory of their interactions, whereas they may only remember a subset of their interactions on the recall format. Second, recognition surveys may be subject to name order effects, where students are more likely to select peers' names that appear early on in the roster than those that appear later on (e.g., due to survey fatigue). Here we report the results of two methodological studies of these possible errors in the context of introductory physics courses: one directly comparing 65 student responses to recognition and recall versions of the same network survey prompt, and the other measuring name order effects on 54 recognition surveys from 27 different courses. We find that students may report more peer interactions on a recognition survey than a recall survey and that most recognition surveys are not subject to significant name order effects. These results help to inform survey design for future network studies in physics education research.

physics.ed-ph↗

arXiv2025

Relative benefits of different active learning methods to conceptual physics learning

It has been shown that active learning methods are more effective than traditional lecturing at improving student conceptual understanding and reducing failure rates in undergraduate physics courses. Researchers have developed distinct, active learning methods that are now widely implemented in introductory physics. However, the relative benefits of these methods remain unknown. Here we present a multi-institutional comparison of the impacts of four well-established active learning methods -- Peer Instruction, Investigative Science Learning Environment (ISLE), Tutorials and Student-Centered Active Learning Environment with Upside-Down Pedagogies (SCALE-UP) -- on conceptual learning. We find measurable increases in student conceptual learning in all four active learning methods, and significantly larger gains in SCALE-UP than in either Peer Instruction or ISLE. Student development of peer networks is similar across the four methods, but classroom activities differ. In many of the observed Peer Instruction and ISLE courses, instructors lecture for a large fraction of class time. In Tutorials and SCALE-UP courses, instructors dedicate most in-class time to student-centred activities such as worksheets and laboratory work. These results prompt future work to identify causal mechanisms between specific classroom activities and conceptual learning and to examine additional factors related to variation in student learning across different methods.

physics.ed-ph↗

arXiv2023

Towards a Generalized Assessment of Computational Thinking for Introductory Physics Students

Computational thinking in physics has many different forms, definitions, and implementations depending on the level of physics, or the institution it is presented in. In order to better integrate computational thinking in introductory physics, we need to understand what physicists find important about computational thinking in introductory physics. We present a qualitative analysis of twenty-six interviews asking academic (N=18) and industrial (N=8) physicists about the teaching and learning of computational thinking in introductory physics courses. These interviews are part of a longer-term project towards developing an assessment protocol for computational thinking in introductory physics. We find that academic and industrial physicists value students' ability to read code and that Python (or VPython) and spreadsheets were the preferred computational language or environment used. Additionally, the interviewees mentioned that identifying the core physics concepts within a program, explaining code to others, and good program hygiene (i.e., commenting and using meaningful variable names) are important skills for introductory students to acquire. We also find that while a handful of interviewees note that the experience and skills gained from computation are quite useful for student's future careers, they also describe multiple limiting factors of teaching computation in introductory physics, such as curricular overhaul, not having "space" for computation, and student rejection. The interviews show that while adding computational thinking to physics students repertoire is important, the importance really comes from using computational thinking to learn and understand physics better. This informs us that the assessment we develop should only include the basics of computational thinking needed to assess introductory physics knowledge.

physics.ed-ph↗