Knowledge structures and the vocabulary of engineering novices. Presented at the Eighth International ISKO Conference, London, July 13-16, 2004.
AuthorColeman, Anita Sundaram
Local subject classificationcontrolled vocabularies
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CitationKnowledge structures and the vocabulary of engineering novices. Presented at the Eighth International ISKO Conference, London, July 13-16, 2004. 2004-07,
AbstractThis presentation is based on the refereed paper published in the ISKO 8 proceedings (see References for citation). It describes a study of the language used by undergraduate engineering students engaged in a civil engineering laboratory. Learnerâ s concepts and relationships in the area of soil consolidation were elicited in order to provide an understanding of the structural knowledge of novices and compare it with the knowledge structures of a human expert and a thesaurus tool. Concept maps and pathfinder networks were used to visualize and analyze the resultant knowledge structures of novice learners, expert, and tool. Results show that there is little similarity between the knowledge structures of the novice, the expert, and the tool. There is preliminary evidence that students with complex knowledge structures earn better grades thereby, encouraging collaborative research between instructional evaluation and knowledge organization in order to measure the educational impact of digital libraries (DL); for example, cause-effect relationships could be studied between the vocabularies used in browsing and other navigational systems in a DL and the educational outcomes achieved.
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Facilitating knowledge discovery by integrating bottom-up and top-down knowledge sources: A text mining approachLeroy, Gondy A. (The University of Arizona., 2003)This dissertation aims to discover synergistic combinations of top-down (ontologies), interactive (relevance feedback), and bottom-up (machine learning) knowledge encoding techniques for text mining. The strength of machine learning techniques lies in their coverage and efficiency because they can discover new knowledge without human intervention. The output, however, is often imprecise and irrelevant. Human knowledge, top-down or interactively encoded, may remedy this. The research question addressed is if knowledge discovery can become more precise and relevant with hybrid systems. Three different combinations are evaluated. The first study investigates an ontology, the Unified Medical Language System (UMLS), combined with an automatically created thesaurus to dynamically adjust the thesaurus' output. The augmented thesaurus was added to a medical, meta-search portal as a keyword suggester and compared with the unmodified thesaurus and UMLS. Users preferred the hybrid approach. Thus, the combination of the ontology with the thesaurus was better than the components separately. The second study investigates implicit relevance feedback combined with genetic algorithms designed to adjust user queries for online searching. These were compared with pure relevance feedback algorithms. Users were divided into groups based on their overall performance. The genetic algorithm significantly helped low achievers, but hindered high achievers. Thus, the interactively elicited knowledge from relevance feedback was judged insufficient to guide machine learning for all users. The final study investigates ontologies combined with two natural language processing techniques: a shallow parser and an automatically created thesaurus. Both capture relations between phrases in biomedical text. Qualified researchers found all terms to be precise; however, terms that belonged to ontologies were more relevant. Parser relations were all precise. Thesaurus relations were less precise, but precision improved for relations that had their terms represented in ontologies. Thus, this integration of ontologies with natural language processing provided good results. In general, it was concluded that top-down encoded knowledge could be effectively integrated with bottom-up encoded knowledge for knowledge discovery in text. This is particularly relevant to business fields, which are text and knowledge intensive. In the future, it will be worthwhile to extend the parser and also to test similar hybrid approaches for data mining.
The Knowledge of Drugs and How that Knowledge Improves after Current Drug Education Curriculum in an 8- to 11-year old PopulationApgar, David; Sexton, Lisa; College of Pharmacy, The University of Arizona (The University of Arizona., 2010)OBJECTIVES: To examine the effect of a current elementary school drug education program, Too Good for Drugs, on children’s views about drugs. METHODS: This was a descriptive, prospective study using pre- and post-test methodology. Participants were given a survey prior to the initiation of a drug education program, Too Good for Drugs. One month after the completion of the drug education program the same survey was given to the participants to see if their views and overall knowledge had changed. RESULTS: The overall knowledge of the groups increased from baseline (p=0.004). Participants in the fourth-grade group had a greater difference in drug knowledge from baseline than fifth-graders (p=0.008 vs. 0.01, respectively). The fourth-grade cohort had increased healthy attitudes about alcohol (p=0.007). Both groups had healthier views on marijuana (4th p=0.007 5th p=0.03) post- intervention. CONCLUSIONS: The Too Good for Drugs curriculum is effective at improving the views about drugs among the participants. Views on alcohol and marijuana improved. Participants in the program may be better served if there were an over-the-counter and prescription drug component.