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AAPT SM 20

Learning Assistants, Failure Rates, and Equity in Physics

learning assistants

equity

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. 

poster

The University of Maryland’s First-Year Innovation & Research Experience (FIRE) is a 3-semester gen-ed program with a first-semester enrollment of more than 600 undergraduates. FIRE provides faculty-mentored research experience and career-readiness with more than 15 diverse research streams available to its students in their second and third semesters. In 2019, I launched a FIRE stream called “Simulating Particle Detection” (SPD), to introduce undergraduate students to the field of experimental high energy particle physics. The research concentrates on computing and data analysis using simulations of novel detectors, specifically, the upgrade calorimeters of the CMS experiment at CERN. While high energy physics experiments are at the forefront of large collaborative research, large-size, university-wide, course-based research experiences are not as common. There are challenges to be addressed to serve about thirty undergraduates every year, such as adaptation of a high-level research topic into a course curriculum, physical and digital research setting logistics, and mentoring of students from different disciplines. I will share my experiences from SPD so far, highlighting not only research but also pedagogical methods and outcomes concerning the above challenges.

Particle Physics Research within UMD’s FIRE Program

Simple epidemiological models are introduced using finite difference methods in Excel. The SIR model explains the initial exponential growth of COVID-19, the effects of social distancing in the US during early April 2020 and successfully predicts the continued spread of the virus during late April and May 2020. The SIR model is the origin of the basic reproduction number R0 and herd immunity. It also predicts what will happen if social distancing is lifted prematurely. A wide range of student research projects are possible for modeling and making predictions based on real data for US states and other countries. See http://circle4.com/biophysics for free sample chapters and videos.

Birds, Bats, & Lego Limbs: Modeling the Variable Moment of Inertia During Locomotion in a Lab for IPLS Students

A surface seiche is a regular oscillation of the surface of an enclosed body of water such as a lake or bay. This project examines the formation of a seiche in a small circular above-ground pool. Analyzing observations from an ultrasonic range sensor, we determine the initial amplitude, period and decay constant of the seiche to be 6.2 cm, 1.975 s and 340 s, respectively. Since the decay time constant is much larger than the period, frictional effects should have a relatively small impact on the period of the seiche. We use a finite difference scheme to solve the wave equation in cylindrical coordinates for the surface height. We find that the theoretical period agrees with the observations if a wave speed consistent with the pool dimensions is used instead of the wave speed provided by the shallow wave approximation.

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Learning Assistants, Failure Rates, and Equity in Physics

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Particle Physics Research within UMD’s FIRE Program

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