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Examining the classification and evolution of novice users’ mental models of an academic database in the search task completion process
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2020 (English)In: Journal of information science, ISSN 0165-5515, E-ISSN 1741-6485, Vol. 46, no 2, p. 205-225Article in journal (Refereed) Published
Abstract [en]

The main task of this article is to develop a classification system for novice users’ mental models of an academic database and to elaborate upon the evolution mechanisms of those mental models. In total, 83 undergraduate students, mainly sophomores, who were all novice users of the academic database China National Knowledge Infrastructure (CNKI), participated in the experimental study. Their mental models were measured from the diagrams or pictures and corresponding interpretations they produced to articulate their perceptions of CNKI at five time points. A bottom-up encoding approach and content analysis were used to analyse the research data. The results demonstrated that novice users’ mental models of the academic database can be classified as either system-oriented or user-oriented perspectives. Six categories were identified in the system-oriented perspective, and three were identified in the user-oriented perspective. It was also found that the evolution of users’ mental models can be facilitated by retrieval tasks, and it is noteworthy that task type can influence the evolution of users’ mental models. Furthermore, the evolution process of users’ mental models can be seen as learning behaviour, which includes a learning session and a forgetting session.

Place, publisher, year, edition, pages
2020. Vol. 46, no 2, p. 205-225
Keywords [en]
Academic database, evolution, learning behaviour, novice user, users’ mental model
National Category
Human Computer Interaction
Research subject
Man-Machine-Interaction (MMI)
Identifiers
URN: urn:nbn:se:su:diva-177724DOI: 10.1177/0165551519828621OAI: oai:DiVA.org:su-177724DiVA, id: diva2:1383322
Available from: 2020-01-07 Created: 2020-01-07 Last updated: 2020-02-19Bibliographically approved

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  • apa
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