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AAPT SM 20

Presidents Town Hall

Join AAPT and Physics Educators worldwide in developing and strengthening your professional connections by attending the 2021 AAPT Virtual Summer Meeting. 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. Attending the virtual summer meeting is an excellent opportunity to share your expertise and learn from and network with leaders in the field.

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AAPT SM 21

technical paper

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. 

Computation is a critical part of professional physics practice and has been shown again and again to be useful for helping student build physics understanding. But, what are the essential elements that allow physics students to productively use computation? I argue that we can use the theory of computational literacy to answer this question. According to diSessa (2000) and Berland (2016), computational literacy consists of three main elements, or pillars: 1) The material pillar, which involves writing and understanding computer code; 2) The cognitive pillar, which involves breaking down problems into a form that can be addressed with computation (also known as computational thinking); 3) The social pillar, which involves communicating with and about computation. Using the physics department of the University of Oslo, Norway, as a case study, I describe what each of these elements can look like and how we can help our students achieve computational literacy in physics

Computational Literacy and Computational Essays at the University of Oslo

Understanding and supporting graduate education policies and practices is critical to the success of future physicists and astronomers and to supplying well-trained science, technology, engineering and mathematics (STEM) professionals to the US workforce. As such, it is essential that graduate programs study and promote practices that support equity and inclusion. During the 2018-19 academic year, admissions into the Physics and Astronomy graduate program at the University of Utah was halted while a complete overhaul of all aspects of the program was initiated by the Department. The program reform included the recruitment and admissions process, orientation and teaching assistant training, advising practices and other student support, the curriculum, and the exam structure. This presentation will outline our process of change and highlight results from the first year of new policy implementations.

Graduate Program Reform at the University of Utah

We have previously established a ranking of incorrect responses to FMCE questions using Item Response Theory. In Bock’s Nominal Response Model (NRM), each student has an associated latent trait (i.e., understanding of mechanics), and each response is represented by two parameters. One of the parameters is reported as showing the strength of correlation between that response choice and the latent trait. Bock does not interpret the meanings of these parameters; however, the mathematical form of the NRM is identical to the probability of a system being in a discrete degenerate energy state in the canonical ensemble. Through analogy we may interpret the latent trait as relating to the system temperature, and explain why one parameter (“energy”) is related to the latent trait but the other parameter (“degeneracy”) is not. We may also gain additional information by extending this analogy to consider macroscopic quantities such as average energy and entropy.

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Computational Literacy and Computational Essays at the University of Oslo

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