Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWu, Siyu
dc.contributor.authorSwanson, Hillary
dc.contributor.authorSherin, Bruce
dc.contributor.authorWilensky, Uri
dc.identifier.citationWu, S., Swanson, H., Sherin, B., & Wilensky, U. (2022). Investigating student learning about disease spread and prevention in the context of agent-based computational modeling. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1245-1248). International Society of the Learning Sciences.en
dc.descriptionShort Paperen
dc.description.abstractCOVID-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.en
dc.publisherInternational Society of the Learning Sciences
dc.relation.ispartofProceedings of the 16th International Conference of the Learning Sciences - ICLS 2022, pp. 1245-1248
dc.subjectLearning Sciencesen
dc.titleInvestigating Student Learning About Disease Spread and Prevention in the Context of Agent-Based Computational Modelingen
Appears in Collections:ISLS Annual Meeting 2022

Files in This Item:
File SizeFormat 
ICLS2022_1245-1248.pdf366.62 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.