Resource Image

ModelSim Population Biology Unit

Author(s): Uri Wilensky1, Paulo Blikstein2, Corey Brady3, David Figlio1, Pratim Sengupta4, Jason Bertsche1, Yu (Bryan) Guo1, Nathan Holbert1, Michael Novak1, Firat Soylu1, Aditi Wagh1, Gokul Krishnan4, Brogan Miller2, Shima Salehi2

1. Northwestern University 2. Stanford University 3. Inquire Learning 4. Vanderbilt University

1318 total view(s), 240 download(s)

0 comment(s) (Post a comment)

Summary:
This resource provides rich student modeling activities and materials, teacher resources, and much more, in the realm of agent based models applied to population biology.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 30 Oct 2018 doi:10.25334/Q4714C - cite this

Description

"The ModelSim project approaches its four core curriculum areas (Evolution, Population Biology, Electricity, and the Particulate Nature of Matter) with a distinctive and unified approach: in each of these subject areas, we encourage students to conceive of fundamental real-world phenomena in new ways.  And in each case, we provide learning environments for exercising this new way of thinking, which build on students’ intuitions to develop powerful and grounded understandings. 

This fundamentally new way of thinking is called “agent-based modeling.”  Within this perspective, complex phenomena are viewed as emerging from interactions between systems of simple elements, or “agents.”  By focusing on the behavior of these simple agents, and by using computational modeling to simulate interactions between many of these agents, scientists in a wide range of disciplines are introducing powerful new research methods."

This program is funded by NSF and run by researchers and designers from Northwestern University's Center for Connected Learning and Computer Based Modeling, where NetLogo was developed, Mind, Matter & Media Lab at Vanderbilt University, Transformative Learning Technologies Lab at Stanford University, and Inquire Learning. This is an interdisciplinary team with two decades of experience in computer-based modeling and curriculum development.

Cite this work

Researchers should cite this work as follows: