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Computer-based graphing and analysis of lab experiments at the high school/introductory level can be challenging. Spreadsheet programs are not designed for scientific graphing, while software from equipment vendors can be costly. In either case, valuable classroom time may be spent learning how to use the tool. nPlot is a free, web-based graphing and analysis program that runs on any platform. It offers a simple user interface that students learn quickly. Features include robust auto-scaling, easy selection among standard fitting models, a curve tangent finder, and error bar plotting. Up to three data sets can be displayed at once to compare different iterations of the same experiment. The graphs produced are professional quality, and can include Greek letters and sub- and super-scripts. The finished plots can be saved on the student’s device, or downloaded as an image for inclusion in a lab report.

AAPT SM 22

Graphing and Curve Fitting for Introductory Physics

AAPT meetings are a great way to reflect on your teaching, recommit to best practices and re-energize your career with new research, products, and techniques. Listen to internationally known plenary speakers, attend sessions, workshops, and special activities. This meeting is an excellent opportunity to share your expertise and learn from and network with leaders in the field.



technical paper

Project-based labs provide significant opportunities for students to engage in authentic experimental practices. Over the past several years, we have been shifting from concept-based, verification labs toward project-based labs. Our approach is to choose a sandbox in which students have a strong enough foundation of principles to develop a model but in which there are outstanding questions to be explored. I will discuss our approach to identifying these sandboxes and how we adapt course materials and equipment to support student exploration.

Choosing a sandbox for project-based labs

The Next Generation Science Standards call for science teachers to integrate engineering into science instruction. Many teachers address this using integrated STEM units built around engineering design challenges. An important challenge in these units is making sure that students draw meaningful connections between their science learning and the engineering design challenges. In this talk, I will share a framework for evidence-based reasoning, based on the claim-evidence-reasoning framework, that I use to support students in explaining how their engineering design decisions are supported by their science learning. I will also share research on integrated STEM instruction that informed the design of this framework as well as how I apply it in my classroom. When using this framework, I have found students see the engineering design challenges as a meaningful opportunity to apply their science learning.

Evidence-Based Reasoning for Integrated STEM

Complex multiple-choice questions are commonly used to test for physics knowledge, appearing on course exams, concept inventories, and the physics GRE. These questions are assumed to measure more complex thinking than standard multiple choice questions do because students must determine which set of statements is true (e.g., A only, A and C, etc.). Our previous work found students in STEM courses answer these questions incorrectly more often than standard multiple-choice questions. Here, we examine whether there are disparities in who answers these questions correctly in introductory physics compared to other introductory STEM courses. Our data comes from an online, “points-free” test bank where students practice for their upcoming exams using actual questions from previous exams. As expected, results showed significant variation between students in who answers complex multiple-choice questions correctly, which can inform whether using these types of questions might be introducing unintended systematic grade penalties in course exams.

Who Answers Complex Multiple-choice Questions in Physics Correctly?

The Energy and Momentum Conceptual Survey (EMCS) is a multiple-choice survey that contains a variety of energy and momentum concepts at the level of introductory physics used to help inform instructors of student mastery of those concepts. Prior studies suggest that many concepts on the survey are challenging for introductory physics students and the average student scores after traditional instruction are low. The research presented here compares the performance of students in introductory, upper-level, and graduate-level physics courses on five EMCS questions to understand the cross-sectional evolution of student understanding of these concepts from the introductory to upper-level to graduate-level. We discuss five EMCS questions that remain challenging for many upper-level and graduate students.

Evolution in Student Conceptual Understanding of Energy and Momentum

Learning is a process that involves mutual responsibility (students’ relying on others’ feedback on their learning) and practice adjustments (changes in a student’s approach to a course based on their progress). However, in introductory physics, many students struggle to adjust their practices in response to formative assessments. We studied these difficulties in an introductory physics course by assigning a weekly reflection journal with questions about the students’ experience in the course, including the challenges they encountered and the study habits they practiced. We reviewed these students’ final reflection assignment and recorded themes that emerged: These students talked about collaboration, course difficulty, responsibility, self-managed growth, and a lack of change in academic success or practices. These insights into the student learning experience will be used to generate course improvements.

Reflective Writing in Physics I

We demonstrate how freely-available, cloud-based, IBM quantum hardware can be used to perform several types of quantum dynamics simulations. Though current devices are far from capable of tackling truly difficult problems which cannot be attacked classically, these noisy, intermediate-scale quantum (NISQ) devices are ideally suited for investigating quantum dynamics in small systems. Simulations of one or two spins with fairly arbitrary interactions and external fields are shown to agree well with theory, but even a system consisting of three spins is shown to be quite difficult to simulate due to noise and errors. These investigations are shown to be well-suited as a computational and experimental supplement to the standard undergraduate quantum mechanics curriculum.

Simulating quantum dynamics with IBM quantum computers

Just-in-Time Teaching (JiTT) is an instructional strategy involving feedback from students on pre-lecture activities in order to design in-class activities to build on the feedback. We investigate the effectiveness of a JiTT approach, which included in-class concept tests using clickers, in an upper-division quantum mechanics course. We analyze student performance on pre-lecture reading quizzes and in-class clicker questions answered individually and then again after group discussion and compare those performances with open-ended retention quizzes administered after completing instruction on the concepts. In general, compared to the reading quizzes, student performance improved when individual clicker questions were posed after lectures that focused on students' identified difficulties. The performance on the clicker questions after group discussions following individual clicker question responses also improved, as did the performance on retention quizzes administered at a later time. We discuss some possible reasons for the improved performance at various stages of instruction.

Impact of pre-class reading and peer instruction in quantum mechanics

A feeling of isolation can contribute to leaks in the pipeline. Being “the only one” certainly leads to a sense of isolation, whether as a woman, as a racial minority, or as the sole physics instructor in the institution. Sixty percent of two-year colleges (TYCs) that offer physics classes have no or one full-time physics instructor (aip.org/statistics). Isolation is a critical issue for physics faculty at TYCs, and for both full-time and part-time instructors. This is even more critical in the era of COVID. Isolation for TYC physics faculty may be mitigated by creating virtual-meeting mentorship groups. In this presentation, I will share the development and implementation of a pilot project for TYC physics faculty, adapted from the e-Alliance mutual mentorship program for women in physics.

Using Mutual Mentoring to Mitigate Isolation for TYC Physics Faculty

Since the beginning of the COVID-19 pandemic, faculty members have faced a changed and challenging environment. They were faced with reallocating their time across various teaching, research, and service responsibilities; adjusting their teaching format, testing, and labs; and dealing with reduced access to the resources they needed to teach and/or conduct research, all while trying to maintain the quality of their work and personal well-being. Using data from the American Institute of Physics’ Statistical Research Center 2021 Faculty Member Survey, we will dissect some of these changes and discuss how they may (or many not) vary based on faculty members’ gender identity and parenting status.

Faculty Members’ Experiences During the COVID-19 Pandemic

Using data from the American Institute of Physics’ Statistical Research Center 2011-12 Survey of Physics in Two-Year Colleges, we will look back at various facets of diversity in physics in two-year colleges including students and faculty members (people), academic units (places), and variety in course offerings (pedagogy). Using more recent data from the National Science Foundation (NSF) and the US Department of Education’s Integrated Postsecondary Education System (IPEDS), we will examine trends in two-year college student diversity since 2012. In addition, using data from College Board, we will explore the impact of the pandemic on enrollments and retention for students at two- and four-year colleges.

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Choosing a sandbox for project-based labs

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