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    Improving novice Tetris players with feedback from AI model based tutors

    Author
    Sibert, Catherine
    View/Open
    175984_Sibert_rpi_0185N_10631.pdf (496.2Kb)
    Other Contributors
    Gray, Wayne D., 1950-; Schoelles, Michael J.; Reid, Larry D.;
    Date Issued
    2015-05
    Subject
    Cognitive science
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/1471
    Abstract
    Computer 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.;
    Description
    May 2015; School of Humanities, Arts, and Social Sciences
    Department
    Dept. of Cognitive Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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    • RPI Theses Online (Complete)

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