Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning
Name
BJET ABM for Mechanistic Reasoning June 2019.pdf
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689.98 KB
Format
Adobe PDF
Checksum (MD5)
636665911c7dc90bb7f7182b8d381007
Author(s)
Hsiao, Ling
Lee, Irene
Klopfer, Eric
Date Issued
July 2019
Journal
British Journal of Educational Technology
Publisher
Wiley
Citation
Ling, Hsiao et al. "Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning." British Journal of Educational Technology 50, 5 (July 2019): 2203-2216 © 2019 British Educational Research Association
Version
Author's final manuscript
Abstract
Computer modeling promotes mechanistic reasoning when learners build and analyze models of complex systems to explore causal mechanisms and use models to generate patterns. StarLogo Nova (SLN), an agent‐based modeling (ABM) environment, enables novice programmers to model a system's individual components and investigate its emergent, collective behavior. Through case analysis of teachers using SLN, we demonstrate how ABM advances thinking about mechanisms generating phenomenon. Teachers who used simulation combined with the decoding of SLN models utilized mechanistic reasoning to make sense of how and why complex phenomenon emerged.
MIT Department
Massachusetts Institute of Technology. Program in Comparative Media Studies/Writing
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1111/bjet.12844
