Summary
Search engines have entered popular culture. They touch people in diverse private and public settings and thus heighten the importance of such important social matters as information privacy and control, censorship, and equitable access. To fully benefit from search engines and to participate in debate about their merits, people necessarily appeal to their understandings for how they function. In this chapter we examine the conceptual understandings that people have of search engines by performing a content analysis on the sketches that 200 undergraduate and graduate students drew when asked to draw a sketch of how a search engine works. Analysis of the sketches reveals a diverse range of conceptual approaches, metaphors, representations, and misconceptions. On the whole, the conceptual models articulated by these students are simplistic. However, students with higher levels of academic achievement sketched more complete models. This research calls attention to the importance of improving students’ technical knowledge of how search engines work so they can be better equipped to develop and advocate policies for how search engines should be embedded in, and restricted from, various private and public information settings.
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References
Belew RK (2000) Finding out about: a cognitive perspective on search engine technology and the WWW. Cambridge University Press, Cambridge
Belkin NJ, Oddy RN, Brooks HM (1982) ASK for information-retrieval: 1. background and theory. Journal of Documentation 38: 61–71
Borgman CL (1985) The user’s mental model of an information retrieval system. Paper presented at the Proceedings of the 8th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp 268–273, June 5–7). Montreal, Quebec, Canada
Borgman CL (1986) The user’s mental model of an information retrieval system: an experiment on a prototype online catalog. International Journal of Man-Machine Studies 24: 47–64
Borgman CL (1996) Why are online catalogs hard to use? Lessons learned from information-retrieval studies. Journal of the American Society for Information Science 37: 387–400
Brin S, Page L (1998) The anatomy of a large-scale hypertextual search engine. Paper presented at the 7th International World Wide Web Conference. Retrieved July 1, 2004, from http://wwwdb.stanford.edu/.backrub/google.html
Brown, DR (2001) SuperModeler: hugh dubberly. Gain: AIGA Journal of Design for the Network Economy, 1: 1–8. Retrieved, June 1, 2004, from http://gain1.aiga.org/pdf/profile.pdf
Cohen R (2002, December 15) Is googling okay? New York Times Magazine, p 50
Edwards B (2004, April 13) Morning edition: search engine wars, Part II. (Radio Broadcast). Seattle, National Public Radio, KUOW
Efthimiadis EN (1990) Progress in documentation: online searching aids: a review of front-ends, gateways and other interfaces. Journal of Documentation 46: 218–262
Efthimiadis EN (2003–2007) The IR toolbox. Available at http://irtoolbox.ischool.washington.edu
Efthimiadis EN, Freier NG (2007) IR-Toolbox: an experiential learning tool for teaching IR. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Amsterdam, The Netherlands, July 23–27, 2007. SIGIR’07, ACM Press, New York
Efthimiadis EN, Hendry DG (2005) Search engines and how students think they work. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Salvador, Brazil, August 15–19, 2005). SIGIR ‘05. ACM Press, New York, NY, pp 595–596. http://doi.acm.org/10.1145/1076034.1076145
Fidel R, Davies RK, Douglas MH, Holder JK, Hopkins CJ, Kushner EJ, Miyagishima BK, Toney CD (1999) A visit to the information mall: web searching behavior of high school students. Journal of American Society for Information Science 50: 24–37
Fischer G, Giaccardi E, Ye Y, Sutcliffe AG, Mehandjiev N (2004) Meta-design: a manifesto for end-user development. Communications of the ACM 47: 33–37
Friedman B, Kahn PH Jr (2003) Human values, ethics, and design. In: Jacko JA and Sears A (eds), The human–computer interaction handbook (pp 1177–1201). Mahwah, NJ: Lawrence Erlbaum Associates
Friedman B, Howe D, and Felten E (2002) Informed consent in the Mozilla browser: implementing value sensitive design. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS ‘02) Vol 8 (January 7–10, 2002). Washington, DC: IEEE Computer Society. Retrieved July 15, 2005, from www.hicss.hawaii.edu/HICSS_35/HICSSpapers/PDFdocuments/OSPEI01.pdf
Gauch S, Smith J (1993) An expert system for automatic query reformulation. Journal of the American Society for Information Science 44: 124–136
Gentner D, Stevens AL (eds) (1983) Mental models. Hillsdale, NJ: Erlbaum
Gleick J (2004, March 21) Get out of my namespace. New York Times Magazine: 44–49
Greene SL, Devlin SJ, Cannata PE, Gomez LM (1990) No IFs, ANDs, ORs: a study of database querying. International Journal of Man–Machine Studies 32: 303–326
Halttunen K (2003) Students’ conceptions of information retrieval: implications for the design of learning environments. Library and Information Science Research 25: 307–332
Halttunen K, Jarvelin K (2005) Assessing learning outcomes in two information retrieval learning environments. Information Processing and Management 41: 949–972
Hansell S (2003, December 8) Foes of bush enlist google in group prank. New York Times, p C.8
Hendry DG (2006) Sketching with conceptual metaphors to explain computational processes. In: Proceedings of IEEE Symposium on Visual Languages/Human-Centric Computing 2006, September 4–7, 2006, Brighton, UK (pp 95–102). IEEE Computer Society Press
Hendry DG, Efthimiadis EN (2004) Students’ mental models of information retrieval systems. In: Proceedings of the American Society for Information Science and Technology, ASIST’04, Providence, Rhode Island, November 13–18, 2004. 41: 580–581. http://dx.doi.org/10.1002/meet.1450410186
Hendry DG, Harper DJ (1997) An informal information-seeking environment. Journal of American Society of Information Science (Special Issue on Human–Computer Interaction) 48: 1036–1048
Hochman D (2004, March 14) In searching we trust. New York Times, p 9.1
Holland D, Quinn N (eds) (1987) Cultural models in language and thought. Cambridge University Press, New York
Ingwersen P (1996) Cognitive perspectives of information retrieval interaction: elements of cognitive IR theory. Journal of Documentation 52: 3–50
Jansen BJ (2005) Seeking and implementing automated assistance during the search process. Information Processing and Management 41: 909–928
Johnson-Laird PN (1983) Mental models: towards a cognitive science of language, inference, and consciousness. Harvard University Press, Cambridge, MA
Kempton W (1987) Two theories of home heat control. In: D. Holland and N. Quinn (eds) Cultural models in language and thought (pp 223–242) Cambridge University Press, New York
Koenemann J, Belkin NJ (1996) A case for interaction: a study of interactive information retrieval behavior and effectiveness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Common Ground (pp 205–212). ACM Press
Lakoff G, Johnson M (1980) Metaphors we live by. The University of Chicago Press, Chicago
Landis J, Koch G (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159–174
Lenhart A, Horrigan J, Fallows D (2004) Content creation online: Pew Internet and American Life Project. Retrieved 1 June 2004 from http://www.pewInternet.org/pdfs/PIP_Content_Creation_Report.pdf
Liddy E (2001) How a search engine works. Searcher 9(5). (Also Retrieved 1 June 2004 from http://www.infotoday.com/searcher/may01/liddyhtm)
Lombard MJ, Snyder-Duch J, Bracken CC (2002). Content analysis in mass communication: assessment and reporting of intercoder reliability. Human Communication Research 28: 587–604
Luke (2004, 1 June) Lucene Index Toolbox. Retrieved 1 June 2004 from http://www.getoptorg/luke/
McNichol T (2004, January 22) Your message here. New York Times, p G.1
Media Metrix (2004) Press Release, RESTON, Va. March 19, 2004. Retrieved 1 June 2004 from http://www.comscore.com/press/release.asp?press=443
Miller JD (1998) The measurement of civic scientific literacy. Public Understanding of Science 7: 203–223
Muramatsu J, Pratt W (2001) Transparent queries: investigating users’ mental models of search engines. Paper presented at the Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, LO
Noguchi Y (2004, February 9) Online search engines help lift cover of privacy. Washington Post, p A01
Norman DA (1983) Some observations on mental models. In: Gentner D, Stevens A (eds) Mental models (pp 7–14). Hillsdale, NJ: Lawrence Erlbaum Associates
Oakes MP, Taylor MJ (1998) Automated assistance in the formulation of search statements for bibliographic databases. Information Processing and Management 34: 645–668
Payne S (1991) A descriptive study of mental models. Behaviour & Information Technology. 10: 3–21
Payne S (2003) Users’ mental models: the very ideas. In: Carroll JM (ed) HCI models, theories, and frameworks (pp. 135–156). Morgan Kaufmann, New York
Roth W-M, Lee S. (2002). Scientific literacy as collective praxis. Public Understanding of Science 11: 33–56
Schemo J (2004, January 28) Online auctions, misspelling in ads often spells cash. New York Times, p A1
n H (1996) The Sciences of the artificial (3rd ed). MIT, Cambridge, MA
Slone D (2002) The influence of mental models and goals on search patterns during web interaction. Journal of the American Society for Information Science and Technology 53(13):1152–1169
Spoerri A (1993) InfoCrystal: a visual tool for information retrieval & management. Paper presented at the Proceedings of the Second International Conference on Information and Knowledge Management (pp 11–20). New York: ACM Press
Topi H, Lucas W (2005) Mix and match: combining terms and operators for successful web searches. Information Processing and Management 41: 801–817
Totty M, Mangalindan M (2003, February 26) As google becomes web’s gatekeeper, sites fight to get in. Wall Street Journal, p A11.1
Wurman RS (2001) Information anxiety 2. Que, Indianapolis, IN
Young D, Shneiderman B (1993) A graphical filter/flow representation of boolean queries: a prototype implementation and evaluation. Journal of American Society for Information Science 44: 327–339
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Hendry, D.G., Efthimiadis, E.N. (2008). Conceptual Models for Search Engines. In: Spink, A., Zimmer, M. (eds) Web Search. Information Science and Knowledge Management, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75829-7_15
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DOI: https://doi.org/10.1007/978-3-540-75829-7_15
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