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    Mapping the structure of knowledge for teaching nominal categorical data analysis

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    http://hdl.handle.net/11603/133
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    • SU Department of Education Leadership and Graduate Studies
    • SU Faculty and Staff Collection
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    Author/Creator
    Groth, Randall E.
    Bergner, Jennifer A.
    Date
    2013
    Type of Work
    Text
    Citation of Original Publication
    Groth, R.E., & Bergner, J.A. (2013). Mapping the structure of knowledge for teaching nominal categorical data analysis. Educational Studies in Mathematics, 83, 247-265.
    Subjects
    Statistics
    Teacher education
    categorical data
    Statistical knowledge for teaching
    Partially correct constructs
    Abstract
    This report describes a model for mapping cognitive structures related to content knowledge for teaching. The model consists of knowledge elements pertinent to teaching a content domain, the nature of the connections among them, and a means for representing the elements and connections visually. The model is illustrated through empirical data generated as prospective teachers were in the process of developing knowledge for teaching nominal categorical data analysis. During a course focused on the development of statistical knowledge for teaching, the prospective teachers analyzed statistical problems, descriptions of children’s statistical thinking, and related classroom scenarios. Their analyses suggested various types of knowledge structures in development. In some cases, they constructed all knowledge elements targeted in the course. In many cases, however, their knowledge structures had missing, incompatible, and/or disconnected elements preventing them from carrying out recommendations for teaching elementary nominal categorical data analysis in an optimal manner. The report contributes to teacher education by drawing attention to prospective teachers’ learning needs, and it contributes to research on teachers’ cognition by providing a method for modeling their cognitive structures.


    Salisbury University
    Guerrieri Academic Commons
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    www.salisbury.edu

    Contact Information:
    Email: SOAR@salisbury.edu
    Phone: 410.543.6206


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    Salisbury University
    Guerrieri Academic Commons
    1101 Camden Ave.
    Salisbury, MD 21801

    www.salisbury.edu

    Contact Information:
    Email: SOAR@salisbury.edu
    Phone: 410.543.6206


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.