United States

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.

AAPT SM 21

Sentiment Analysis of Faculty Responses COVID Transition to Online Learning

online

covid

sentiment

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technical paper

We will present the design and performance of a complete measurement of a damped harmonic oscillation with the Pasco Smart Cart. Using the position and force sensors from the cart on a steeply inclined track (about 10 degrees from vertical), we measure the displacement, velocity, acceleration and the restoring force of the oscillator, which is the cart on the end of a spring. The students get a full understanding of these quantities in an oscillation, together with the concepts of period and frequency, both graphically, numerically and analytically. With added weights to the cart, the students can measure the spring constant in the system. By fitting to the reduction in the oscillation amplitude, the students can obtain the attenuation from friction. With one instrument, this new lab provides the students with a full range of training in harmonic oscillation. Measurement results and data analysis will be shown in the presentation.

Complete measurement of a damped harmonic oscillation with the Smart-Cart

Entering 2021, COVID-19 still threatens to require remote laboratoryoperation, and new longer-term solutions need to be developed to forstudents to do this. Many kits have been developed for the introduc-tory level during 2020. However, very little has been developed forthe advanced physics laboratory. We have developed a kit that al-lows advanced lab students to perform a set of mini-experiments usingPYTHON, a Raspberry Pi, Arduinos, and Arduino compatible sen-sors. Students are permitted to take the kit home and only need toprovide a few common items, e.g. a ruler, to perform the experiment.This experiment serves as a nice introduction to microcontrollers.The overarching goal is for students to gain familiarity with the Rasp-berry Pi, Arduino, and it’s sensors by performing basic experimentsinwhich the physics is well known and the students learn how toacquire data with these microcontrollers. Students perform mini-experiments from basic kinematics to determining Planck’s constantand using a Geiger-Mueller counter. Students use analog-to-digitalconverters(ADCs), digit-to-analog converters (DACs), accelerometers,and more complex sensors.We give details on the different mini-experiments that the studentsperform. We also discuss some of the learning outcomes as well as howthe experiment can easily be performed at the university or at home.

A Modern Remotely Operable Arduino Approach for Advanced Physics Laboratory

“Experimental Techniques” is an intermediate laboratory course designed in part to help students decide whether they want to pursue experimental physics. Key to this goal are meaningful opportunities for creative exploration and problem solving which mimic real-world research. Despite the COVID-19 pandemic, by building an interferometer at home using inexpensive department-provided materials, students were afforded opportunities for real creativity while proving that changes in optical path length – not just arm length – lead to fringe evolution. Though the physics involved was significantly simpler than in other experiments, students developed a better understanding of optical path length and interferometry while being introduced to instrumentation and some of the fundamental skills used by experimentalists in the lab. Members of the class also gained a meaningful sense of accomplishment and excitement. Consequently, we are now considering implementing this experiment as part of the in-person version of the course.

Less is More: At-Home Interferometry in Undergraduate Laboratory Course

As the interests of science and industry continue to push toward the very small, an understanding of advanced imaging methods has become an increasingly competitive skill for young physicists and optical engineers. Yet many undergraduate curricula have been slow to integrate concepts such as Fourier optics and coherent diffraction imaging (CDI) into their courses. We present a simple and portable experiment that allows students to explore the basic principles of Fourier optics, diffraction, digital signal processing, sampling, and coherent imaging in a take home laboratory kit.

Principles of Fourier optics in a take home undergraduate laboratory

As part of a research project to examine whether students can develop scientific habits of mind through video-based experimentation, we created video-based Investigative Science Learning Environment learning cycles (v-ISLEs), where students investigated phenomena using interactive videos. We implemented these cycles, twice, once in Fall 2018 and again in Spring 2019. We divided a 96-student algebra-based ISLE physics class into sections where some students used only ISLE apparatus-based labs, while some students had a mixture of v-ISLEs and apparatus labs. At the beginning and end of the semester, students answered the E-CLASS, a survey that assesses students’ attitudes about a variety of scientific laboratory practices. We will present how the different lab settings affect students' views about their strategies and habits of mind when doing experiments.

ISLE-based apparatus and video labs: student’s beliefs about experimental physics

One of the goals of physics instruction is to help students develop reasoning skills in the context of physics. As conceptual understanding is required to reason productively, it is necessary to design an instrument that disentangles (to the degree possible) a conceptual understanding from reasoning approaches. To address this challenge, we have been developing sequences of screening-target questions: screening questions probe conceptual understanding, while target questions require students to apply this understanding in situations that present reasoning challenges. We will discuss the validation process involving cluster analysis, logistic regression, and classical test theory item analysis such as item difficulty and discrimination. Current results suggest that most items are appropriate for calculus-based introductory students and are functioning as expected.

Development of an Instrument to Assess Student Reasoning

Distractors (incorrect responses) on research-designed multiple choice instruments like the FCI reflect prevalent student misconceptions. Hence analyzing all student responses on an equal footing should yield information on actual student beliefs, whereas total score only sets a threshold for “Newtonian Thinking”. We developed a Multi-dimensional Nominal Item Response Theory that yields a “weight matrix” acting on a student’s responses to give various dimensions of ability. Analyzing 3 large colleges called State, State University, and University in order of increasing posttest score on the FCI, we find that Dimension 1 correlates with score at the 0.98 level. The weight matrix elements for the second dimension correlate at ~0.73 among these colleges and positive ability#2 represents understanding N’s 3rd but not his 2nd law vs the reverse for negative ability#2. The third dimension shows correlations of ~ 0.4 and we find that this dimension has partially different implications across the colleges.

FCI Higher Dimensions Determined by Analyzing Right and Wrong Responses

The effectiveness of peer review sessions in introductory physics laboratory courses has been measured. To implement the peer review sessions, the laboratory course has been adjusted from twelve somewhat connected experiments to three sets of experiments that all focus on a different concept within introductory physics: linear motion, springs, and collisions. The students prepare a rough draft for the paper for each set, which is then given to other students for them to critique and give feedback. An evaluation protocol to measure a given paper’s effectiveness in multiple aspects has also been developed. Comparing the results to a preliminary study completed multiple years ago, we can see the true effect that the changes to the laboratory class have made.

Effectively Implementing Peer Review into the Introductory Physics Laboratory Course

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.

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