United States

Despite limiting access to applicants from underrepresented racial and ethnic groups, the practice of using hard or soft GRE cutoff scores in physics graduate program admissions is still a popular method for reducing the pool of applicants. The present study considers whether the undergraduate institutions of applicants have any influence on the admissions process by modelling a physics GRE cutoff score with application data from admissions offices of five Midwestern universities. Two distinct approaches based on inferential and predictive modelling are conducted. While there is some disagreement regarding the relative importance between features, the two approaches largely agree that to include institutional information significantly aids the analysis. Both models identify cases where the institutional effects are comparable to factors of known importance such as gender and undergraduate GPA. As the results are stable across many cutoff scores, we advocate against the practice of employing physics GRE cutoff scores in admissions.

AAPT SM 20

How do undergraduate institutions matter for physics GRE cutoff scores?

cutoff-score

physics-gre

institutions

modelling

machine learning

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. The program featured plenary speakers, sessions, workshops and many special activities.

For more information please visit the 2020 AAPT Virtual Summer Meeting page at: https://www.aapt.org/Conferences/SM2020/index.cfm.


The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. 

technical paper

During the Spring 2020 quarter, the COVID-19 pandemic precluded access to our acoustics laboratory. In response, I redeveloped the experiments of our acoustics lab course in several ways. For some introductory activities, I employed videos to show basic concepts and methods in place of lab demonstrations and used applets to maintain some student interactivity. However, for most of the specialized experiments, I was able to draw upon past student work archived in electronic laboratory notebooks to extract photos, setup details, and experimental data sets. I then used that information to write activities that described the experimental methods, but required the current students to visualize, analyze, and interpret the data. To facilitate the course remotely, I used electronic laboratory notebooks to deliver and receive content. Preliminary indications are that the students are obtaining a similar understanding of the experimental methods and possibly better understanding of the data analysis than previous students.

Teaching an Advanced Acoustics Laboratory Course without a Laboratory

Lawrence’s Advanced Laboratory course trains students in experimental physics. Using research equipment, students engage deeply with real physics while learning scientific communication. Students worked on three self-selected projects using analog and digital electronics, ranging from single photon detectors to chaotic dynamics. Each student created an extension to one project, consisting of design and prototyping as well as collection and analysis of data. One student built an analog electronics neuron, firing upon exposure to a trumpet played at 392 Hz. Students shared project results with their scientific community via a scientific symposium (after students attended the APS Virtual April Meeting), an issue of Advanced Laboratory Letters at Lawrence (after PRL-based journal club discussions), and video demonstrations. Join us for a discussion of our approaches to evaluation and feedback, inclusive strategies, and course (and Zoom-related) fatigue. We will present what worked, what fell flat, and what surprised us along the way.

Advanced Laboratory at Home: From Single Photons to Chaos

Introducing SU(3) color charge in undergraduate quantum mechanics

Preliminary Results on Students in Dispersed Remote Telescope Observing Teams

Each year, International Masterclasses (IMC) bring authentic particle physics data analysis to high school students and teachers. We had just started for 2020 when the pandemic came. By mid-March all IMC videoconferences were canceled, thus ending masterclasses for thousands worldwide. A group in QuarkNet responded by building and facilitating a masterclass to be done on a large scale with a core analysis that students could perform remotely with coaching by their teachers and online support from staff. We called it the Big Analysis of Muons in CMS (BAMC). Two rounds of BAMC were accomplished: one in April with students of mostly QuarkNet teachers and an international round in May. Between them, an estimated 700 students participated from around the world.

QuarkNet creates a big online physics event (twice)

QuarkNet is an NSF-funded professional development (PD) program that provides science teachers the means to develop their skills to bring authentic research and data analysis into the classroom. We will discuss some of our challenges and opportunities in leading PD during this time of remote learning, the technologies we are using, and some best practices in leading PD remotely.

Leading Effective Professional Development Using Zoom

Goes like thinking refers to the way that physics experts quickly relate the behavior of one changing quantity to that of another, e.g., "the electric field goes like one over R squared.'" This statement is deeply meaningful and intuitive to experts, who understand why other quantities such as charge are omitted; however, it is not clear that students interpret this phrase the same way. One application of "goes like" reasoning is data linearization---choosing quantities to use for the independent and dependent axes to produce a linear graph whose slope has meaning. Preliminary work suggests that students may not understand the value and meaning of data linearization. We aim to describe our attempts to design and assess online lab curriculum in an introductory physics course addressing data linearization, and discuss some of the ways students make sense of their data analysis using "goes like" reasoning.

Exploring student use of "goes like" thinking when linearizing data

In recognition of the need for additional opportunities for remote learning for teachers and students, QuarkNet established the QuarkNet Wednesday Webinar Series, a set of five talks from physicists and educators offered via Zoom webinar through May and early June. We will discuss our experience coordinating and moderating these sessions and share lessons learned.

QuarkNet Wednesday Webinars

Background: In the present paper, we are reviewing the interest statistics in the field of physics at the undergraduate level. Materials/Methods: We reviewed the data from various credible sources regarding information from the past three decades and analyzed this data. Results/Discussion: We reviewed the independent variables such as the number of students majoring in physics during their junior and senior years in their undergraduate degree. Another independent variable is examining the undergraduate graduation rate with a major in physics. In the present work, we reviewed and reported the data into 3 segments: 1975-90, 1990-2005, and 2005 – 2020. Data was collected from different reporting sources of all Indian and U.S. universities. Statistical analysis was conducted on this data and the results were discussed.

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