Date of Award:

12-2024

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

School of Teacher Education and Leadership

Committee Chair(s)

Kimberly Lott

Committee

Kimberly Lott

Committee

Max L. Longhurst

Committee

Sarah Braden

Committee

Marla Robertson

Committee

Karen Kapheim

Abstract

Science education researchers have called for increased attention to the science practice of modeling to engage students in authentic scientific inquiry. A major challenge to integrating the modeling practice into science instruction is that students tend to conceptualize models as static end-products of research rather than generative tools for theory-building. Engaging students in using computer models, specifically agent-based computer models (ABMs), to explain and predict scientific phenomena could improve students' understanding of models in science. In this design-based research project I investigate the effects of an ABM-supported learning environment on high-school biology students' understanding of the COVID-19 pandemic and the purpose of models in science. First, I report the results of two iterations of a curricular intervention and discuss how revisions made to the curriculum in Year 2 resulted in improved student learning outcomes. Second, I describe the results of a teaching experiment in Year 2 in which students participated in the curricular intervention either with or without an ABM computer model of disease spread. The data provide evidence that using an ABM, explicitly to test ideas and make predictions about the COVID-19 pandemic, improved students' understanding of pandemics and the purpose of models in science. Based on the findings, I identify general design principles for the implementation and development of ABM-supported learning environments.

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