The rise of the super experiment

Conference paper (2012)

Authors

John C. Stamper Carnegie Mellon University
J.D. Lomas Carnegie Mellon University
Dixie Ching New York University
Steve Ritter Carnegie Learning, Inc.
Kenneth R. Koedinger Carnegie Mellon University
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Published Date

01-01-2012

Language

English

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Abstract

Traditional experimental paradigms have focused on executing experiments in a lab setting and eventually moving successful findings to larger experiments in the field. However, data from field experiments can also be used to inform new lab experiments. Now, with the advent of large student populations using internet-based learning software, online experiments can serve as a third setting for experimental data collection. In this paper, we introduce the Super Experiment Framework (SEF), which describes how internet-scale experiments can inform and be informed by classroom and lab experiments. We apply the framework to a research project implementing learning games for mathematics that is collecting hundreds of thousands of data trials weekly. We show that the framework allows findings from the lab-scale, classroom-scale and internet-scale experiments to inform each other in a rapid complementary feedback loop.