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dc.contributor.advisorRichard C. Larson.en_US
dc.contributor.authorTimmers, Kendell M. (Kendell MacQueen), 1978-en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2005-06-02T18:25:16Z
dc.date.available2005-06-02T18:25:16Z
dc.date.copyright2004en_US
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/17729
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.en_US
dc.descriptionIncludes bibliographical references (p. 105-110).en_US
dc.description.abstractThere is a great need among educators for a way to quickly assign teams in large or distance learning classrooms in a manner superior to random assignment or student self-selection. Forming teams based on knowledge of students' characteristics is too time-consuming for large classrooms, yet research has shown that the characteristics of individuals greatly affect the quality of the teamwork experience. This thesis provides an automated method to quickly assign students to teams based on individual characteristics. We begin with a thorough review of the literature on how individuals' characteristics affect team behavior, focusing on the level of diversity of four main classes of traits - knowledge/skills/abilities, demographics, personality, and motivation. By forming teams that have diversity on some of these traits and homogeneity on others, we will be able to improve performance over randomly assigned teams. We frame this problem from a group dynamics perspective, measuring the compatibility of every dyad of students within a team. We propose, for several group environments, which traits should be homogeneous and which heterogeneous, and how important each trait is, and use these values to create an equation for a student compatibility score, a number representing how well a pair of students will work together. We then simulate team assignment to determine which of several heuristics is most efficient. A combination of random generation and pairwise exchange is found to be the best, forming teams with average compatibilities 307% higher than the average randomly generated team. The code for this program is included in the appendices.en_US
dc.description.abstract(cont.) Additionally, we perform a classroom experiment in which sections of a class are divided into teams by three different methods - random assignment, intuition, and the method devised above. Although the experimental design was flawed, the results were encouraging, demonstrating that average student compatibility on a team was significantly positively associated with both the resulting team grade and the students' perception of how much they learned about teamwork. For a more detailed executive summary of this work, please see the Structure of the Thesis section on page 16.en_US
dc.description.statementofresponsibilityby Kendell M. Timmers.en_US
dc.format.extent146 p.en_US
dc.format.extent5480825 bytes
dc.format.extent5480631 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectOperations Research Center.en_US
dc.titleLearning together better : the structured design of learning teamsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc56473181en_US


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