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It has been extensively documented that introductory physics students tend to struggle with adding and subtracting vectors. The results of a multiple-choice assessment, given to students in large-enrollment, algebra-based courses, were analyzed to identify groups of incorrect responses that were commonly given together. Following the analysis using both module analysis and modified module analysis, the stability of the groups over multiple semesters was examined, and yielded modules of responses that were most closely linked together. These modules were compared across the two analysis methods and classified to identify the underlying mistakes. We now present implications for classroom applications, including the relative prevalence of each kind of mistake, as well as the identification of modules with a high number of contributions per student and modules with the highest number of students contributing. 


AAPT SM 21

Implications of module analysis for instruction of one-dimensional vector manipulation

vectors

network analysis

introductory

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technical paper

We report on three separate cases applying a metric to compare two populations' item response curves (IRCs). Each IRC plots the percentage of students selecting a particular answer choice on an item as a function of their overall score on the concept inventory. First, we compare the IRCs of different demographic groups using data from the Science Literacy Concept Inventory. Next, we use the metric to compare American and Japanese students' IRCs used in Ishimoto, Davenport, and Wittmann (2017) for the Force and Motion Conceptual Evaluation. We also compare the IRCs of each group to the IRCs of a separate American data set. Lastly, we compare the pre-instruction IRCs to the post-instruction IRCs for a matched data set of students completing the Force Concept Inventory. The metric is a measure of the IRCs' similarity, and we used it to identify items that may exhibit bias or demonstrate differences between populations.

An approach for comparing student populations using item response curves

In the spring and summer of 2020, we collected data on faculty experiences during their transition to onlineteaching as a result of the COVID-19 pandemic. Data on the participants’ institutions, job security and position, and preparation time were collected, as well as a free text response to add anything the participants felt wasrelevant. In total 364 text responses were collected. Using natural language processing tools, sentiment scoreswere calculated for each response. The sentiment was found to be overall positive. Then, a machine learning model was created using Keras, which was trained on various data for 75% of the responses. The remaining 25% were used for predicting sentiment scores, to identify which data from the survey, if any, were potentialpredictors of participant sentiment. The score predictions were used to determine if any data on participants’institutions, positions, or transition time were correlated with positive or negative experiences.

Predictors of faculty sentiment on their transition to online teaching

This project aims to understand physics faculty responses to transitioning to online teaching during the COVID-19 pandemic. We surveyed 662 physics faculty following the spring 2020 term, and 258 completed a follow up survey after the fall 2020 term. We used natural language processing to measure the change in sentiment scores of one hundred physics faculty from the United States that completed an optional written prompt on both the initial and follow up 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 followup surveys.

Sentiment Analysis of Faculty Responses COVID Transition to Online Learning

To scaffold GTAs’ use of evidence-based teaching practices, we incorporated four training sessions with a mixed-reality simulator into physics GTA training during the Spring 2020 semester. Three sessions were held and facilitated in-person, while the final session was done remotely due to the COVID-19 pandemic. We decided to hold the final session remotely to balance health risks while closing the loop for GTAs’ professional development and the research project. This remote style mimicked the remote teaching GTAs would do to complete the Spring 2020 semester and have continued through the Summer 2021 semester. Here, we present how two physics GTAs used the pedagogical skills during the remote training session. We also discuss implications and provide considerations for the future direction of physics GTA professional development.

GTAs' Use of Pedagogical Skills in Remote Mixed-reality Training Session

Understanding how experienced and novice teachers can facilitate each other’s professional development can help us to develop a supportive community of practice. In summer 2020, teachers participated in a professional development workshop as part of the Illinois Physics and Secondary Schools (IPaSS) partnership program. During the workshop, pairs of teachers discussed a set of problems focused on conceptual reasoning about energy. One pair consisted of an experienced teacher and a novice teacher. We analyzed the dynamics of the two teachers as they established rapport and created a space where both felt comfortable sharing their ideas and expressing uncertainty. We interviewed these teachers using a video-stimulated recall technique to gain further insight into their perceptions of the interaction, and we will report the results of these interviews in this talk.

Interviews on professional development interactions between experienced and novice teachers

The Department of Physics at University of Connecticut launched a studio physics program in fall 2019, to provide an engaging introductory physics education experience to thousands of undergraduate students annually. Prior to the launch, most of these introductory courses were taught using traditional lecture. A main point of tension between these two instructional modes is the role of the instructor. We noticed that instructors from a range of backgrounds experience difficulties supporting students in effectively engaging with group work. These difficulties were exacerbated when the courses were taught in a distance-based learning format. As part of a larger project to develop guides for instructors to effectively support inclusive group work, we conducted a literature review through the lenses of cognitive science, inclusive teaching, and research-based instructional strategies. In this talk, we will discuss the studio program at UConn, the findings of our literature review, and the group work guides. 


Improving group work in studio-style physics courses

Research in physics education has shown that poor student performance on certain physics tasks may stem primarily from domain-general reasoning phenomena rather than a lack of conceptual understanding. The observed reasoning patterns are consistent with dual-process theories of reasoning (DPToR). Efforts are ongoing to design strategies to guide the development of research-based curriculum to help students strengthen their reasoning skills and support cognitive reflection. In one intervention focused on supporting analytical processing, students are asked to set aside their own reasoning and engage in alternative lines of reasoning. Students first respond to a qualitative physics task, then construct reasoning chains in support of answers given by fictitious students, and finally revisit the original physics task. In this talk, we will discuss intervention results in the context of kinematics, examine factors possibly related to its effectiveness, and highlight the implications of our findings for future DPToR-aligned curriculum development.


*This material is based upon work supported by the National Science Foundation under Grant Nos. DUE-1821390, DUE-1821123, DUE-1821400, DUE-1821511, and DUE-1821561.

Supporting Student Construction of Alternative Lines of Reasoning

An emerging body of research suggests that the nature of human reasoning itself may impact student performance on physics questions. Analysis of student reasoning patterns through the lens of dual-process theories of reasoning (DPToR) indicates that students may struggle to engage analytical processing productively when responding to physics questions containing salient distracting features (SDFs). While students may reason correctly on one question (screening question), they may abandon that same line of reasoning on an analogous question containing an SDF (target question). As part of a larger effort to investigate and support student reasoning in physics, we have designed and tested an intervention that explicitly guides students to apply the reasoning they successfully used on the screening question to the target question in order to help students reconsider their default responses. In this talk, preliminary results will be presented and implications for research-based curriculum development will be discussed.

This material is based upon work supported by the National Science Foundation under Grant Nos. DUE-1821390, DUE-1821123, DUE-1821400, DUE-1821511, and DUE-1821561.

Supporting Students in Exploring Alternative Lines of Reasoning

Whether it is rearranging a kinematics equation or applying Newton’s second law, introductory physics students are often asked to solve problems that require the manipulation of symbols. After analyzing nearly 7,000 mathematics diagnostics administered at four large state universities, we find that many introductory physics students struggle with basic algebra to a degree that could impact their course performance. Correct-response rates on algebra problems drop significantly when numeric coefficients are replaced with symbols. Even the replacement of one type of symbol (Latin letters) with a less-familiar type (Greek letters) seems to have a significant negative effect on performance. We also find that performance on symbolic-type algebra problems is highly correlated with performance on trigonometry, geometry, and graphing problems. We will report our analysis of these results while providing an overview of our recent findings.

Symbolic Manipulation Fluency Predicts Introductory Physics Students’ Mathematical Preparedness

We are interested in trying to understand ways that students collaborate to solve physics problems. One mechanism we have recently observed in the context of spherical unit vectors in upper-division E&M, is that students first co-construct a visual representation, which we call a shared resource, that they then use to solve the problem. While the resources framework as applied in physics education has usually focused on the thinking of individuals, in this work we expand it to look at that of a small group instead. In this first of two related talks, using data from a think-out-loud interview, we illustrate what we mean by the theoretical lens of shared resources: what they are, how they are shared, and what role they can play in helping students make sense of a difficult physics topic. We also describe how we identified a particular shared resource by analyzing student writing, utterances, and behavior.

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Implications of module analysis for instruction of one-dimensional vector manipulation

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