Unsilencing Critical Conversations in Social-Studies Teacher Education using Agent-based Modeling

ABSTRACT

In this article, we argue that when complex sociopolitical issues such as ethnocentrism and racial segregation are represented as complex, emergent systems using agent-based computational models (in short agent-based models or ABMs), discourse about these representations can disrupt social studies teacher candidates' dispositions of teaching social studies without engaging in critical conversations about race and power. Our study extends the literature on agent-based computing to the domain of social studies education, and demonstrates how preservice teachers' participation in agent-based modeling activities can help them adopt a more critical stance toward designing learning activities for their future classrooms.

Acknowledgements

We would like to gratefully acknowledge Dr. Pallavi Banerjee for her contributions to the theoretical framework of the paper, and Dr. Kevin O'Neill, Executive Editor, whose overall insights and commentary greatly improved the paper.

Additional information

Funding

The research reported here was funded in part by the US National Science Foundation's NSF CAREER Award (#1150230) and a grant from the Imperial Oil Foundation, Canada, both awarded to Pratim Sengupta.