arXiv subjects

Meagan Sundstrom

Publications and source records attributed to Meagan Sundstrom.

16 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↗

arXiv2026

Predictive Modeling for High Impact Active Learning Classrooms

Over the past several decades, a large body of research has shown that undergraduate science students learn more and more equitably in active learning classrooms; however, the term "active learning" lacks definition and little research has examined which types and combinations of active learning strategies are most effective. In this study, we use a dataset representing over 10,000 students and 24 institutions to create a predictive model that maps classroom time spent on different activities to student conceptual learning. We find that four variables -- classroom time spent on lecture, group worksheets, clicker questions, and student questions -- are sufficient to reliably predict student learning, as measured by concept inventory scores. We identify one type of class that consistently demonstrates exceptional student learning gains (effect sizes greater than 2): those that spend 10-20% of class time on group worksheets, 20-40% of class time on group clicker questions, and average two or more student questions per hour of class time. We also find that classes which do not utilize group worksheets consistently have learning outcomes comparable to fully lecture classes. These results provide testable recommendations for future controlled studies to investigate effective active learning implementation in undergraduate physics courses.

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

Advancing clustering methods in physics education research: A case for mixture models

Clustering methods are often used in physics education research (PER) to identify subgroups of individuals within a population who share similar response patterns or characteristics. K-means (or k-modes, for categorical data) is one of the most commonly used clustering methods in PER. This algorithm, however, is not model-based: it relies on algorithmic partitioning and assigns individuals to subgroups with definite membership. Researchers must also conduct post-hoc analyses to relate subgroup membership to other variables. Mixture models offer a model-based alternative that accounts for classification errors and allows researchers to directly integrate subgroup membership into a broader latent variable framework. In this paper, we outline the theoretical similarities and differences between k-modes clustering and latent class analysis (one type of mixture model for categorical data). We also present parallel analyses using each method to address the same research questions in order to demonstrate these similarities and differences. We provide the data and R code to replicate the worked example presented in the paper for researchers interested in using mixture models.

stat.ME↗

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

Bias in physics peer recognition does not explain gaps in perceived recognition

Gaining recognition as a physics person by peers is an important contributor to undergraduate students' physics identity and their success in physics courses. Previous research has separately demonstrated that women perceive less recognition from peers than men in their physics courses (perceived recognition) and that women receive fewer nominations from their peers as strong in their physics course than men (received recognition). The relationship between perceived and received peer recognition for men and women, however, is not well understood. Here we test three plausible models for this relationship. We conduct a large-scale, quantitative study of over 1,700 students enrolled in introductory physics courses at eight institutions in the United States. We directly compare student gender, perceived recognition, and received recognition, controlling for other student demographics and course-level variability. Results show with high precision that, for students receiving the same amount of recognition, and having the same race or ethnicity, academic year, and major, women report significantly lower perceived recognition than men. These findings offer important implications for testable instructional interventions.

physics.ed-ph↗

arXiv2023

Investigating peer recognition across an introductory physics sequence: Do first impressions last?

Students' beliefs about the extent to which meaningful others, including their peers, recognize them as a strong science student are correlated with their persistence in science courses and careers. Yet, prior work has found a gender bias in peer recognition, in which student nominations of strong peers disproportionately favor men over women, in some instructional contexts. Researchers have hypothesized that such a gender bias diminishes over time, as determined by students' academic year: studies have found a gender bias in science courses aimed at first-year students, but not in science courses aimed at beyond first-year students. This hypothesis that patterns of peer recognition change over time, however, has yet to be tested with longitudinal data--previous studies only examine snapshots of different students in different science courses. In this study, we isolate the effect of time on peer recognition by analyzing student nominations of strong peers across a two-semester introductory physics course sequence, containing the same set of students and the same instructor in both semesters, at a mostly-women institution. Using a combination of social network analysis and qualitative methods, we find that while many students receive similar levels of peer recognition over time, the four most highly nominated students--the recognition celebrities--exhibit some change between semesters even in this highly controlled setting. Furthermore, we observe that these changes in the celebrities track closely with changes in student outspokenness and that being outspoken is likely more important for gaining recognition than earning a high grade in the class. These findings lend support to prior work's hypothesis that peer recognition changes over time, but also challenge the generalizability of previous results (i.e., that patterns of recognition are related to students' academic year).

physics.ed-ph↗

arXiv2023

What topics of peer interactions correlate with student performance in physics courses?

Research suggests that interacting with more peers about physics course material is correlated with higher student performance. Some studies, however, have demonstrated that different topics of peer interactions may correlate with their performance in different ways, or possibly not at all. In this study, we probe both the peers with whom students interact about their physics course and the particular aspects of the course material about which they interacted in six different introductory physics courses: four lecture courses and two lab courses. Drawing on methods in social network analysis, we replicate prior work demonstrating that, on average, students who interact with more peers in their physics courses have higher final course grades. Expanding on this result, we find that students discuss a wide range of aspects of course material with their peers: concepts, small-group work, assessments, lecture, and homework. We observe that in the lecture courses, interacting with peers about concepts is most strongly correlated with final course grade, with smaller correlations also arising for small-group work and homework. In the lab courses, on the other hand, small-group work is the only interaction topic that significantly correlates with final course grade. We use these findings to discuss how course structures (e.g., grading schemes and weekly course schedules) may shape student interactions and add nuance to prior work by identifying how specific types of student interactions are associated (or not) with performance.

physics.ed-ph↗

arXiv2023

Who and what gets recognized in peer recognition

Previous work has identified that recognition from others is an important predictor of students' participation, persistence, and career intentions in physics. However, research has also found a gender bias in peer recognition in which student nominations of strong peers in their physics course disproportionately favor men over women. In this study, we draw on methods from social network analysis and find a consistent gender bias in which men disproportionately under-nominate women as strong in their physics course in two offerings of both a lecture course (for science and engineering, but not physics, majors) and a distinct lab course (for science, engineering, and physics majors). We also find in one offering of the lecture course that women disproportionately under-nominate men, contrary to what previous research would predict. We expand on prior work by also probing two data sources related to who and what gets recognized in peer recognition: students' interactions with their peers (who gets recognized) and students' written explanations of their nominations of strong peers (what gets recognized). Results suggest that the nature of the observed gender bias in peer recognition varies between the instructional contexts of lecture and lab. In the lecture course, the gender bias is related to who gets recognized: both men and women disproportionately over-nominate their interaction ties to students of their same gender as strong in the course. In the lab course, the gender bias is also related to what gets recognized: men nominate men more than women because of skills related to interactions, such as being helpful. These findings illuminate the different ways in which students form perceptions of their peers and add nuance to our understanding of the nature of gender bias in peer recognition.

physics.ed-ph↗

arXiv2023

Instructing nontraditional physics labs: Toward responsiveness to student epistemic framing

Research on nontraditional laboratory (lab) activities in physics shows that students often expect to verify predetermined results, as takes place in traditional activities. This understanding of what is taking place, or epistemic framing, may impact their behaviors in the lab, either productively or unproductively. In this paper, we present an analysis of student epistemic framing in a nontraditional lab to understand how instructional context, specifically instructor behaviors, may shape student framing. We present video data from a lab section taught by an experienced teaching assistant (TA), with 19 students working in seven groups. We argue that student framing in this lab is evidenced by whether or not students articulate experimental predictions and by the extent to which they take up opportunities to construct knowledge (epistemic agency). We show that the TA's attempts to shift student frames generally succeed with respect to experimental predictions but are less successful with respect to epistemic agency. In part, we suggest, the success of the TA's attempts reflects whether and how they are responsive to students' current framing. This work offers evidence that instructors can shift students' frames in nontraditional labs, while also illuminating the complexities of both student framing and the role of the instructor in shifting that framing in this context.

physics.ed-ph↗

arXiv2022

Understanding interaction network formation across instructional contexts in remote physics courses

Engaging in interactions with peers is important for student learning. Many studies have quantified patterns of student interactions in in-person physics courses using social network analysis, finding different network structures between instructional contexts (lecture and lab) and styles (active and traditional). Such studies also find inconsistent results as to whether and how student-level variables (e.g., grades and demographics) relate to the formation of interaction networks. In this cross-sectional research study, we investigate these relationships further by examining lecture and lab interaction networks in four different remote physics courses spanning various instructional styles and student populations. We apply statistical methods from social network analysis -- exponential random graph models -- to measure the relationship between network formation and multiple variables: students' discussion and lab section enrollment, final course grades, gender, and race/ethnicity. Similar to previous studies of in-person courses, we find that remote lecture interaction networks contain large clusters connecting many students, while remote lab interaction networks contain smaller clusters of a few students. Our statistical analysis suggests that these distinct network structures arise from a combination of both instruction-level and student-level variables, including the learning goals of each instructional context, whether assignments are completed in groups or individually, and the distribution of gender and major of students enrolled in a course. We further discuss how these and other variables help to understand the formation of interaction networks in both remote and in-person physics courses.

physics.ed-ph↗

arXiv2022

Introductory physics students' recognition of strong peers: Gender and racial/ethnic bias differ by course level and context

Researchers have pinpointed recognition from others as one of the most important dimensions of students' science and engineering identity. Studies, however, have found gender biases in students' recognition of their peers, with inconsistent patterns across introductory science and engineering courses. Toward finding the source of this variation, we examine whether a gender bias exists in students' nominations of strong peers across three different remote, introductory physics courses with varying student populations (varying demographics, majors, and course levels). We also uniquely evaluate possible racial/ethnic biases and probe the relationship between instructional context (whether lecture or laboratory) and recognition. Some of our results replicate previous findings (such as the the association of course grade and small class section enrollment with nominations), while others offer contradictions. Comparing across our three courses and the prior work, results suggest that course level (whether first-year students or beyond-first-year students) might be more associated with a gender bias in peer recognition than other variables. Surprisingly, we also find instances of racial/ethnic biases in favor of students from backgrounds historically underrepresented in science. Finally, we find that the nomination patterns differ when students nominate individuals strong in the lecture material versus laboratory material. This work serves as an important step in determining which courses and contexts exhibit biases in peer recognition, as well as how students' perceptions of one another form in remote teaching environments.

physics.ed-ph↗

arXiv2021

Not engaging with problems in the lab: Students' navigation of conflicting data and models

With the adoption of instructional laboratories (labs) that require students to make their own decisions, there is a need to better understand students' activities as they make sense of their data and decide how to proceed. In particular, understanding when students do not engage productively with unexpected data may provide insights into how to better support students in more open-ended labs. We examine video and audio data from groups within a lab session where students were expected to find data inconsistent with the predictions of two models. In prior work, we examined the actions of the four groups that productively grapple with this designed problem. Here, we analyze the engagement of the three groups that do not. We conducted three phases of analysis: 1) documenting large scale behaviors and time spent in on-topic discussion, 2) analyzing interactions with the teaching assistant, and 3) identifying students' framing--their expectations for what is taking place--when they were discussing their data. Our Phase 1 and 2 analysis show only minor differences between the groups that engaged with the problem and those that did not. Our Phase 3 analysis demonstrated that the groups that did not engage with the problem framed the lab activity as about confirming a known result or as a series of hoops to jump through to fulfill assignment requirements. Implications for instruction include supporting teaching assistants to attend to students' framing and agency within laboratory classrooms.

physics.ed-ph↗