Turning Science Into Practice: Using Computational Modeling to Learn About COVID-19

• In Event: Infectious Learning and Teaching: Opportunities and Challenges of Integrating COVID-19 Into K–16 Education

Abstract

OBJECTIVE: The COVID-19 pandemic has amplified the critical need for science literacy for an informed citizenry. Computational modeling provides an excellent vehicle for raising scientific awareness of the emergent understanding of COVID-19. In order to both encourage computational thinking (CT) skills and build scientific knowledge of the COVID-19 pandemic, we have created a series of programming activities through which students construct their own computational models based on the emerging scientific consensus around COVID-19. The presentation focuses on the use of scientific modeling to help students understand different factors that impact the spread and containment of the pandemic.
THEORETICAL FRAMEWORK: In this project, we use a theoretical framework of model competence (Grunkorn et al., 2014), to glean students' understanding of the purpose of models, and also the nature of scientific models. This unit was adopted from a unit on epidemic modeling developed as part of an NSF-funded CT integration project (Cateté et al., 2018; Lytle et al., 2019). The models utilize an agent-based modeling approach (Wilensky & Rand, 2015) where a conceptual scientific model is re-represented by students as an interactive block-based simulation of the interaction of agent-people who are in various physiological states. Students are able to model everyday situations such as being in a crowded area or going to stores while unknowingly infected, and immediately see the consequences of those actions. Students can explore the differences in direct versus indirect transmission, as well as symptom severity, and their effects on the model outcomes. By including accurate scientific variables such as the reproductive number of the virus, incubation period, and period of communicability, students are able to create their own epi-curves that demonstrate the severity of the disease and provide students with visual representation of how quickly COVID-19 spreads. We also use the scientific model and associated modeling activities to reinforce best practices at home and in the community.
METHODS. We ran our study with two 8th grade teachers, each teaching 5 hybrid virtual classes. The 260 students completed days 1 and 3 synchronously, and days 2 and 4 asynchronously. Additionally, one-third of the students were in-person the week of the activity following the same asynchronous/synchronous schedule.
DATA. We use students’ programming code traces, exit tickets, and teacher and researcher observations (Lytle et al., 2019b) to evaluate student understandings and progress during the 4 day activity.
RESULTS. Data from 40 assenting students are used in our results. We found that across the days of our activity students gravitated toward a level 2 or 3 in understanding the purpose of models (explaining and predicting). However, we also found that a majority of students (92%) had a level 2 understanding of the nature of models, believing them to be idealized representations, rather than theoretical reconstructions.
SIGNIFICANCE: We find that we can use COVID-19 as a relatable model for students to build greater understandings of scientific modeling, while also helping students demonstrate the effects of best practices for containing the spread of disease.

Authors

• Veronica Catete, North Carolina State University

• Madeline Hinckle, North Carolina State University

• Tiffany Barnes

• Eric N. Wiebe, North Carolina State University