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ERIC Number: ED537230
Record Type: Non-Journal
Publication Date: 2012-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 7
ISBN: N/A
ISSN: N/A
The Rise of the Super Experiment
Stamper, John C.; Lomas, Derek; Ching, Dixie; Ritter, Steve; Koedinger, Kenneth R.; Steinhart, Jonathan
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012)
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. (Contains 2 figures and 2 tables.) [This research was supported by Next Generation Learning Challenge, Carlow University, and Pellisippi State University. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: Elementary Education
Audience: N/A
Language: English
Sponsor: Pittsburgh Science of Learning Center
Authoring Institution: International Educational Data Mining Society