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dc.rights.licenseUsers may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
dc.contributorGray, Wayne D., 1950-
dc.contributorSchoelles, Michael J.
dc.contributorFajen, Brett R.
dc.contributor.authorLindstedt, John K.
dc.date.accessioned2021-11-03T08:06:39Z
dc.date.available2021-11-03T08:06:39Z
dc.date.created2014-04-14T10:31:15Z
dc.date.issued2013-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1019
dc.descriptionDecember 2013
dc.descriptionSchool of Humanities, Arts, and Social Sciences
dc.description.abstractThe identification of expertise in a complex task is trivial when all data is in after the fact. To achieve a better understanding of the nature of expertise my first goal, in this work, is to identify the behaviors most predictive of different levels of expertise (novice to expert) in the video game Tetris. The second goal is to examine how little data is required to accurately predict levels of expertise. The present study analyzes potential behavioral indicators of expertise in Tetris at three levels (global, local, and immediate) and under four levels of data quantity. Presented are statistical models using the established metrics of behavior to predict overall output performance. Results indicate mild success in predicting performance, offering a starting point for analyzing other complex and dynamic tasks with explicit criteria for successful performance.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectCognitive science
dc.titleIdentifying expertise : data exploration in Tetris
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid170813
dc.digitool.pid170814
dc.digitool.pid170815
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Cognitive Science


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