Human-computer interaction: the impact of users' cognitive styles on query reformulation behaviour during web searching

  • Authors:

  • Khamsum Kinley;Dian Tjondronegoro;Helen Partridge;Sylvia Edwards

  • Affiliations:

  • Queensland University of Technology, Brisbane QLD, Australia;Queensland University of Technology, Brisbane QLD, Australia;Queensland University of Technology, Brisbane QLD, Australia;Queensland University of Technology, Brisbane QLD, Australia

  • Venue:
  • Proceedings of the 24th Australian Computer-Human Interaction Conference
  • Year:
  • 2012

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Abstract

This paper discusses users' query reformulation behaviour while searching information on the Web. Query reformulations have emerged as an important component of Web search behaviour and human-computer interaction (HCI) because a user's success of information retrieval (IR) depends on how he or she formulates queries. There are various factors, such as cognitive styles, that influence users' query reformulation behaviour. Understanding how users with different cognitive styles formulate their queries while performing Web searches can help HCI researchers and information systems (IS) developers to provide assistance to the users. This paper aims to examine the effects of users' cognitive styles on their query reformation behaviour. To achieve the goal of the study, a user study was conducted in which a total of 3613 search terms and 872 search queries were submitted by 50 users who engaged in 150 scenario-based search tasks. Riding's (1991) Cognitive Style Analysis (CSA) test was used to assess users' cognitive style as wholist or analytic, and verbaliser or imager. The study findings show that users' query reformulation behaviour is affected by their cognitive styles. The results reveal that analytic users tended to prefer Add queries while all other users preferred New queries. A significant difference was found among wholists and analytics in the manner they performed Remove query reformulations. Future HCI researchers and IS developers can utilize the study results to develop interactive and personalised search model, and to provide context-based query suggestions for users.