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Search-and-Retrieval Tools in E-learning Applications: An Empirical Comparison of Search Engines and Topic Maps in an Educational Context

Paper, was published by Steven Shaw, Vivek Venkatesh, Gretchen Lowerison, Dennis J. Dicks, and Dai Zhang at 2004-11-01

This paper reports on database-oriented search-and-retrieval mechanisms in e-learning applications through the use of Topic Maps.

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This paper reports on an investigation focusing on improving the reliability and efficiency in current database-oriented search-and-retrieval mechanisms in e-learning applications through the use of topic map technology (ISO/IEC 13250). The development of our topic maps is grounded in a theory of cognitive information-processing and information retrieval. We outline the development, implementation and evaluation of a topic map designed to help graduate students search for online resources in preparation for an essay assignment. Sources of data include (a) hard data drawn from observations, tracking of search-related steps, ; and (b) soft data concerning judgments of utility of information and users’ understandings of the search process. The results indicate that, in comparison to search engines, topic maps enable students to provide more precise answers and higher recall than the search engine employed in our study.

 

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Topic Maps is an excellent paradigm to support human thinking and to visualize networked information. As part of my PhD project, I therefore chose Topic Maps as the conceptual foundation for designing and implementing a software prototype for semantic knowledge retrieval.

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