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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorGray, Wayne D., 1950-
dc.contributorSchoelles, Michael J.
dc.contributorReid, Larry D.
dc.contributor.authorSibert, Catherine
dc.date.accessioned2021-11-03T08:26:00Z
dc.date.available2021-11-03T08:26:00Z
dc.date.created2015-06-09T13:48:53Z
dc.date.issued2015-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/1471
dc.descriptionMay 2015
dc.descriptionSchool of Humanities, Arts, and Social Sciences
dc.description.abstractComputer based tutoring systems have largely been focused on static tasks, where the tutor guides the student through a single solution. Tutors for dynamic tasks are much more difficult to create, as the solution constantly changes as the task progresses. This study uses Tetris™ as a testbed for a tutor of a complex, dynamic task. Subjects were trained under a number of different feedback conditions using a computer model designed to approximate the behavior of human players. While each feedback system was hypothesized to affect human behavior in a particular way, no significant differences were observed in player behavior after training, but several interesting trends were suggested and continue to be explored.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectCognitive science
dc.titleImproving novice Tetris players with feedback from AI model based tutors
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid175983
dc.digitool.pid175984
dc.digitool.pid175985
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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