Eric Brewe

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.

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.

Introductory university physics courses often face the dual challenge of introducing students to new physics concepts while also addressing their preconceived notions that conflict with scientific principles. Active learning pedagogical approaches, which employ constructivist principles and emphasize active participation in the learning process, have been shown to be effective in teaching complex physics concepts. However, while the behavioral effects of constructivist methodologies are largely understood, the neurobiological underpinnings that facilitate this process remain unclear. Using functional magnetic resonance imaging (fMRI), we assessed students enrolled in either an active learning or lecture-based physics course before and after a 15-week semester of learning and examined changes in hippocampal whole-brain connectivity. We focused on the hippocampus given its critical role in learning and memory. Our findings revealed that hippocampal connectivity with brain regions in the frontal and parietal lobes decreased over time, regardless of instructional approach. Results also indicated that active learning students exhibited increased hippocampal connectivity with parietal, cerebellar, and frontal regions, reflecting experiential learning based on constructivist principles, whereas lecture-based students exhibited increased hippocampal connectivity with the fusiform gyrus, suggesting learning through passive observation. Our findings demonstrate that while some aspects of hippocampal functional connectivity may decrease over time, active vs. passive learning may preferentially enhance hippocampal connectivity during physics learning.

Academic institutions are increasingly adopting active learning methods to enhance educational outcomes. Using functional magnetic resonance imaging (fMRI), we investigated neurobiological differences between active learning and traditional lecture-based approaches in university physics education. Undergraduate students enrolled in an introductory physics course underwent an fMRI session before and after a 15-week semester. Coactivation pattern (CAP) analysis was used to examine the temporal dynamics of brain states across different cognitive contexts, including physics conceptual reasoning, physics knowledge retrieval, and rest. CAP results identified seven distinct brain states, with contributions from frontoparietal, somatomotor, and visuospatial networks. Among active learning students, physics learning was associated with increased engagement of a somatomotor network, supporting an embodied cognition framework, while lecture-based students demonstrated stronger engagement of a visuospatial network, consistent with observational learning. These findings suggest significant neural restructuring over a semester of physics learning, with different instructional approaches preferentially modulating distinct patterns of brain dynamics.

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.

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.

Extensive research has demonstrated 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 several distinct active learning methods that are
now widely implemented in introductory physics; however, the relative benefits
of these methods remain unknown. Here we present the first multi-institutional
comparison of the impacts of four well-established active learning methods
(ISLE, Peer Instruction, Tutorials, and SCALE-UP) on conceptual learning. We
also investigate student development of peer networks and the activities that
take place during instruction to explain differences in these impacts. Data
include student concept inventory scores, peer network surveys, and classroom
video recordings from 31 introductory physics and astronomy courses at 28
different institutions in the United States containing a total of 2,855
students. We find measurable increases in student conceptual learning in all
four active learning methods (ranging from 2.09-sigma to 6.22-sigma differences
from a null effect), and significantly larger conceptual learning gains in
SCALE-UP than in both ISLE (2.25-sigma difference) and Peer Instruction
(2.54-sigma difference). Conceptual learning gains in Tutorials are not
significantly different from those in the other three methods. Despite the
hypothesized benefits of student interactions, student development of peer
networks is similar across the four methods. Instead, we observe differences in
classroom activities; in many of the observed ISLE and Peer Instruction
courses, instructors lecture for a large fraction of class time. In Tutorials
and SCALE-UP courses, instructors dedicate most in-class time to
student-centered activities such as worksheets and laboratory work.

This study uses an initial attitudinal survey and a subsequent follow-up survey to assess which qualities are correlated with the persistence of women in physics. The initial survey was given to participants at the Conference for Undergraduate Women and Gender Minorities in Physics, and the follow-up survey was given years later, after they graduated; 478 matched surveys were used in this work. Using these surveys, we study how attitudes correlate with retention. First, we examine whether affective factors, mainly situated in identity theory, have any clear correlation with persistence in physics. Then, we study individual questions of the survey, such as a student’s interest in research specifically, to determine more fine-grained correlations with retention. Both of these were examined using analyses of variance (ANOVAs) with post hoc Tukey tests. From studying the overarching factors, we find that respondents who remained in physics after completing their bachelor’s degree reported higher interest than respondents who left physics at earlier points, with a medium effect size. We also find small evidence of associations between sense of belonging and correlation; respondents who leave physics before obtaining a bachelor’s degree tend to have a higher sense of belonging than other groups, while respondents who stay in physics until graduating, then leave for another science, technology, engineering, and mathematics field tend to have a lower sense of belonging. This association, however, shows a small effect size and should be examined more carefully to draw definitive conclusions. We find that identity, perceived recognition, performance competence, success mindset, and sense of community show no significant correlation with long-term persistence. When examining individual questions, we find that people who remain in physics tend to have a higher interest specifically in research, as well as career aspirations of going to graduate school and becoming a professor in physics.

This project aims to understand physics faculty responses to transitioning to online teaching during the COVID-19 pandemic. We surveyed 662 physics faculty from the United States following the Spring 2020 term; of these, 258 completed a follow-up survey after the Fall 2020 term. We used natural language processing to measure the sentiment scores of 364 Spring 2020 responses and another 134 Fall 2020 responses of physics faculty who completed an optional written prompt. Additionally, we determined the change in sentiment scores of the 100 individuals who responded to both surveys. These sentiment scores measured between -1 and 1 for completely negative and completely positive, respectively. Sentiment scores after Spring 2020 were slightly positive with a median value of 0.2347. The distribution of sentiment changes was approximately normally distributed with a mean centered near zero. Analysis suggests the average sentiment did not change from the initial to follow-up surveys. To identify major topics within the responses for both surveys, latent Dirichlet allocation analysis was applied to the data. The topic distribution for the initial survey is given as course modifications and technology, negative aspects of the transition -primarily with labs and cheating, exam and evaluation difficulties, and difficulties with student understanding. The topics were noticeably different in the follow-up survey with differences between Fall and Spring, cooperative learning strategies, strategies that worked in the remote space, and benefits of in -person labs.