A classification of mental models of undergraduates seeking information for a course essay in history and psychology: Preliminary investigations into aligning their mental models with online thesauri

Charles Cole, Yang Lin, John Leide, Andrew Large and Jamshid Beheshti

Journal of the American Society for Information Science and Technology, 2007, vol. 58, issue 13, 2092-2104

Abstract: The article reports a field study which examined the mental models of 80 undergraduates seeking information for either a history or psychology course essay when they were in an early, exploration stage of researching their essay. This group is presently at a disadvantage when using thesaurus‐type schemes in indexes and online search engines because there is a disconnect between how domain novice users of IR systems represent a topic space and how this space is represented in the standard IR system thesaurus. The study attempted to (a) ascertain the coding language used by the 80 undergraduates in the study to mentally represent their topic and then (b) align the mental models with the hierarchical structure found in many thesauri. The intervention focused the undergraduates' thinking about their topic from a topic statement to a thesis statement. The undergraduates were asked to produce three mental model diagrams for their real‐life course essay at the beginning, middle, and end of the interview, for a total of 240 mental model diagrams, from which we created a 12‐category mental model classification scheme. Findings indicate that at the end of the intervention, (a) the percentage of vertical mental models increased from 24 to 35% of all mental models; but that (b) 3rd‐year students had fewer vertical mental models than did 1st‐year undergraduates in the study, which is counterintuitive. The results indicate that there is justification for pursuing our research based on the hypothesis that rotating a domain novice's mental model into a vertical position would make it easier for him or her to cognitively connect with the thesaurus's hierarchical representation of the topic area.

Date: 2007
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