TY - GEN
T1 - Investigating Student Learning about Disease Spread and Prevention in the Context of Agent-Based Computational Modeling
AU - Wu, Siyu
AU - Swanson, Hillary Lucille
AU - Sherin, Bruce
AU - Wilensky, Uri
N1 - Funding Information:
This work was supported by the National Science Foundation (1842375).
Publisher Copyright:
© ISLS.
PY - 2022
Y1 - 2022
N2 - COVID-19 has brought increased attention to the importance of health literacy, including understanding of the transmission and prevention of disease. This study presents data from a project aimed at developing a computational modeling microworld to help middle school students learn about these topics. Specifically, the microworld is meant to help students model and test their ideas about how a disease spreads through a population and how an epidemic can be prevented. The paper analyzes one student's knowledge refinement through the building, testing, and debugging of a disease spread and prevention model. We model student refinement of thinking through steps of building initial models and predicting results, testing initial models and making sense of the results, debugging and retesting models, observing final models, and explaining results. Our findings suggest adolescents can learn about strategies for disease prevention through computational modeling.
AB - COVID-19 has brought increased attention to the importance of health literacy, including understanding of the transmission and prevention of disease. This study presents data from a project aimed at developing a computational modeling microworld to help middle school students learn about these topics. Specifically, the microworld is meant to help students model and test their ideas about how a disease spreads through a population and how an epidemic can be prevented. The paper analyzes one student's knowledge refinement through the building, testing, and debugging of a disease spread and prevention model. We model student refinement of thinking through steps of building initial models and predicting results, testing initial models and making sense of the results, debugging and retesting models, observing final models, and explaining results. Our findings suggest adolescents can learn about strategies for disease prevention through computational modeling.
UR - http://www.scopus.com/inward/record.url?scp=85145775484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145775484&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85145775484
T3 - Proceedings of International Conference of the Learning Sciences, ICLS
SP - 1245
EP - 1248
BT - International Collaboration toward Educational Innovation for All
A2 - Chinn, Clark
A2 - Tan, Edna
A2 - Chan, Carol
A2 - Kali, Yael
PB - International Society of the Learning Sciences (ISLS)
T2 - 16th International Conference of the Learning Sciences, ICLS 2022
Y2 - 6 June 2022 through 10 June 2022
ER -