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Contact Information

Email
(615) 936-3513
Wyatt Center 245
VU Mailbox: 230

Education

Ph.D., Northwestern University, 2009

Curriculum Vitae


Pratim Sengupta

Assistant Professor, Dept. of Teaching & Learning

I am an Assistant Professor in the following PhD programs at Vanderbilt University: Learning Sciences & Learning Environments Design (LSLED), and Mathematics & Science Education (MSE).

I direct the Mind, Matter & Media Lab.

My research is funded by several grants from the National Science Foundation, including an NSF CAREER Award.

I design and develop agent-based computational technologies for learning (Strand 1), models of how people think and learn (Strand 2), and multi-agent models of complex social and and natural phenomena (Strand 3).

Strand 1: I design and develop agent-based, visual and tangible programming languages and modeling platforms for a wide age group of learners. Much of this work is for K12 students, to help them develop two kinds of expertise: scientific modeling, and computational thinking. I am particularly passionate about supporting long-term development of generative representational practices in children - a focus that (unfortunately) has largely been missing in the field of learning technologies.

Strand 2: Along the cognition strand, I build models of how people (experts, professionals, students) learn, represent, and reason about complex phenomena. I use a combination of theoretical, empirical and computational approaches to do so. Note that Strands 1 and 2 are often deeply intertwined.

Strand 3: I began my research life as an upper-undergraduate Physics student with an interest in non-linear dynamical systems. After nearly two decades of metamorphosis, I still find myself deeply interested in complexity. Since 2004, when I formally shifted from Physics to Learning Sciences, my work has been shaped by multi-agent systems, which I find to be both delightful and powerful as a modeling paradigm. As a result, along with my colleagues and students, I continue to build agent-based models in domains as diverse as political economy, environmental engineering, and art, including a crowdsourced model of artist networks that was exhibited at the MoMA, NYC. I find that this exercise is not only intellectually enriching, but that it also helps me develop better tools for learning.



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