Types of Document Search Tasks and Users’ Cognitive Information Seeking Strategies

  • Hee-Eun Lee
  • Wan Chul Yoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)


For the researchers and learners, an unprecedented number of documents became available on the Internet and academic archives. Powerful search systems and sophisticated recommendation services are also available. Despite the IT assistance, finding the most useful information in daily knowledge works has become a cognitively demanding task more than ever due to the overwhelming number of documents. To improve the search systems with better human-computer cooperation, human information seeking strategies should be understood. This paper reports a study that identified the differences in the user search strategies with respect to two major search task types: open and purpose-driven exploring (OT) vs. closed and target-specified (CT) tasks. An observational experiment was conducted and the results were analyzed by mapping the user activities on a cognitive task-flow framework. The analysis comparing user activities in four search tasks revealed notable differences in their strategies to deal with the two task types. More frequent re-planning, especially goal reformulation, was observed for OT type tasks. The difference indicates that OT type tasks tended to trigger more knowledge-based behavior, while CT type tasks were performed relying more on rule-based behavior. These findings provide important insights for the design of search systems and user interfaces of knowledge-based systems.


Information Search Information Seeking Strategies Task Types Interaction Design Decision Behavior 


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  1. 1.
    Antoniou, G.: A semantic web primer. The MIT Press (2004)Google Scholar
  2. 2.
    Chin, J., Fu, W.T.: Interactive effects of age and interface differences on search strategies and performance. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 403–412. ACM (2010)Google Scholar
  3. 3.
    Hu, W.C., Chen, Y., Schmalz, M.S., Ritter, G.X.: An overview of world wide web search technologies. In: Proceedings of 5th World Multi Conference on Systems, Cybernetics, Informatics, SCI 2001, Orlando, Florida, pp. 22–25 (2001)Google Scholar
  4. 4.
    Ingwersen, P., Järvelin, K.: The turn: Integration of information seeking and retrieval in context, vol. 18. Springer (2005)Google Scholar
  5. 5.
    Järvelin, K., Ingwersen, P.: User-oriented and cognitive models of information retrieval. Understanding Information Retrieval Systems: Management, Types, and Standards 47 (2012)Google Scholar
  6. 6.
    Navarro-Prieto, R., Scaife, M., Rogers, Y.: Cognitive strategies in web searching. In: Proceedings of the 5th Conference on Human Factors & the Web, pp. 43–56 (1999)Google Scholar
  7. 7.
    Marchionini, G.: Information-seeking strategies of novices using a full-text electronic encyclopedia. Journal of the American Society for Information Science 40(1), 54–66 (1989)Google Scholar
  8. 8.
    Rasmussen, J.: Information Processing and Human-Machine Interaction. An Approach to Cognitive Engineering (1986)Google Scholar
  9. 9.
    Sánchez, D., Moreno, A.: Development of new techniques to improve web search. System 4(4), 2 (2005)Google Scholar
  10. 10.
    Stronge, A.J., Rogers, W.A., Fisk, A.D.: Web-based information search and retrieval: Effects of strategy use and age on search success. Human Factors: The Journal of the Human Factors and Ergonomics Society 48(3), 434–446 (2006)Google Scholar
  11. 11.
    Vicente, K.J.: Cognitive work analysis: Toward safe, productive, and healthy computer-based work. CRC Press (1999)Google Scholar
  12. 12.
    Wilson, M.L., Kules, B., Shneiderman, B.: From keyword search to exploration: Designing future search interfaces for the Web. Foundations and Trends in Web Science 2(1), 1–97 (2010)CrossRefzbMATHGoogle Scholar
  13. 13.
    Xie, I., Bates, M.: Information searching and search models. Understanding information retrieval systems, 31–46 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hee-Eun Lee
    • 1
  • Wan Chul Yoon
    • 2
  1. 1.Department of Industrial and System EngineeringKAISTDaejeonKorea
  2. 2.Department of Knowledge Service EngineeringKAISTDaejeonKorea

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