Multi-decision approaches for eliciting knowledge structure

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Scopus citations

Abstract

This chapter seeks to advance the systematic analysis of knowledge through two new multi-decision approaches, listwise and sorting, for eliciting knowledge structure. The chapter begins with an overview of assumptions about knowledge structure and its measurement. Next follows a discussion of relatedness raw data and its analysis and representation. Then the free recall and the pairwise approach for eliciting relatedness data are described including the likely influence of context and prompt on these measures of knowledge structure. Then the listwise and sorting multi-decision approaches are described, and two experimental investigations using these approaches are reviewed. Finally, the final section of this chapter argues that combining the two approaches can overcome some limitations in the individual approaches. Existing data is reanalyzed to examine the adequacy of a combined multi-decision approach relative to the traditional pairwise approach. Sufficient design details are provided so that others can replicate or extend similar multi-decision application software for eliciting participants' knowledge structure.

Original languageEnglish (US)
Title of host publicationComputer-Based Diagnostics and Systematic Analysis of Knowledge
PublisherSpringer US
Pages41-59
Number of pages19
ISBN (Print)9781441956613
DOIs
StatePublished - Dec 1 2010

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Arts and Humanities(all)

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    Clariana, R. (2010). Multi-decision approaches for eliciting knowledge structure. In Computer-Based Diagnostics and Systematic Analysis of Knowledge (pp. 41-59). Springer US. https://doi.org/10.1007/978-1-4419-5662-0_4