Remote teaching data-driven physical modeling through a COVID-19 open ended data challenge

TL;DR

This paper describes a remote teaching approach using a COVID-19 data challenge to teach data-driven physical modeling, fostering active learning and problem-solving skills in physics students during the pandemic.

Contribution

It introduces an interdisciplinary, open-ended COVID-19 data challenge as a novel teaching method for computational physics courses during remote instruction.

Findings

Open-ended COVID-19 data problems enhance active learning.

Remote teaching fosters student engagement and problem-solving.

Qualitative improvements over traditional close-ended projects.

Abstract

Physics can be seen as a conceptual approach to scientific problems, a method for discovery, but teaching this aspect of our discipline can be a challenge. We report on a first-time remote teaching experience for a computational physics third-year physics laboratory class taught in the first part of the 2020 COVID-19 pandemic (March-May 2020). To convey a ``physics of data" approach to data analysis and data-driven physical modeling we used interdisciplinary data sources, with an openended ``COVID-19 data challenge" project as the core of the course. COVID-19 epidemiological data provided an ideal setting for motivating the students to deal with complex problems, where there is no unique or preconceived solution. Our results indicate that such problems yield qualitatively different improvements compared to close-ended projects, as well as point to critical aspects in using these problems as a teaching strategy. By breaking the students' expectations of unidirectionality, remote teaching provided unexpected opportunities to promote active work and active learning.