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  • × author_ss:"Burnett, K."
  1. Miksa, S.D.; Burnett, K.; Bonnici, L.J.; Kim , J.: ¬The development of a facet analysis system to identify and measure the dimensions of interaction in online learning (2007) 0.02 
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    Abstract
    The development of a facet analysis system to code and analyze data in a mixed-method study is discussed. The research goal was to identify the dimensions of interaction that contribute to student satisfaction in online Web-supported courses. The study was conducted between 2000 and 2002 at the Florida State University School of Information Studies. The researchers developed a facet analysis system that meets S. R. Ranganathan's ([1967]) requirements for articulation on three planes (idea, verbal, and notational). This system includes a codebook (verbal), coding procedures, and formulae (notational) for quantitative analysis of logs of chat sessions and postings to discussion boards for eight master's level courses taught online during the fall 2000 semester. Focus group interviews were subsequently held with student participants to confirm that results of the facet analysis reflected their experiences with the courses. The system was developed through a process of emergent coding. The researchers have been unable to identify any prior use of facet analysis for the analysis of research data as in this study. Identifying the facet analysis system was a major breakthrough in the research process, which, in turn, provided the researchers with a lens through which to analyze and interpret the data. In addition, identification of the faceted nature of the system opens up new possibilities for automation of the coding process.
    Date
    2.11.2007 10:22:40
  2. Stvilia, B.; Hinnant, C.C.; Schindler, K.; Worrall, A.; Burnett, G.; Burnett, K.; Kazmer, M.M.; Marty, P.F.: Composition of scientific teams and publication productivity at a national science lab (2011) 0.02 
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    Abstract
    The production of scientific knowledge has evolved from a process of inquiry largely based on the activities of individual scientists to one grounded in the collaborative efforts of specialized research teams. This shift brings to light a new question: how the composition of scientific teams affects their production of knowledge. This study employs data from 1,415 experiments conducted at the National High Magnetic Field Laboratory (NHMFL) between 2005 and 2008 to identify and select a sample of 89 teams and examine whether team diversity and network characteristics affect productivity. The study examines how the diversity of science teams along several variables affects overall team productivity. Results indicate several diversity measures associated with network position and team productivity. Teams with mixed institutional associations were more central to the overall network compared with teams that primarily comprised NHMFL's own scientists. Team cohesion was positively related to productivity. The study indicates that high productivity in teams is associated with high disciplinary diversity and low seniority diversity of team membership. Finally, an increase in the share of senior members negatively affects productivity, and teams with members in central structural positions perform better than other teams.
    Date
    22. 1.2011 13:19:42

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