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Examining the classification and evolution of novice users’ mental models of an academic database in the search task completion process

Authors:

Zhengbiao Han, Preben Hansen, Haiyun Xu, Rui LuoAuthors Info & Claims

Journal of Information Science, Volume 46, Issue 2

Pages 205 - 225

https://doi.org/10.1177/0165551519828621

Published: 01 April 2020 Publication History

Abstract

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.

References

[1]

Nahl D. Social-biological information technology: an integrated conceptual framework. J Am Soc Inform Sci Technol 2007; 58(13): 2021–2046.

Digital Library

[2]

Kuhlthau CC. Inside the search process: information seeking from the user’s perspective. J Am Soc Inform Sci 1991; 42(5): 361–371.

[3]

Savolainen R. The interplay of affective and cognitive factors in information seeking and use: comparing Kuhlthau’s and Nahl’s models. J Document 2015; 71(1): 175–197.

[4]

Savolainen R. Approaching the affective barriers to information seeking: the viewpoint of appraisal theory. Inform Res 2016; 21(4): 1–20.

[5]

Nahl D and Tenopir C. Affective and cognitive searching behaviour of novice end-users of a full-text database. J Am Soc Inform Sci 1996; 47(4): 276–286.

Digital Library

[6]

Wilson TD. On user studies and information needs. J Document 1981; 37(1): 3–15.

[7]

Borgman CL. The user’s mental model of an information retrieval system: an experiment on a prototype online catalog. Int J Man Mach Stud 1986; 24(1): 47–64.

Digital Library

[8]

Zhang X. A study of the effects of user characteristics on mental models of information retrieval systems. PhD Thesis, University of Toronto, Toronto, ON, 1998.

[9]

Craik K. The nature of explanation. Cambridge: Cambridge University Press, 1943.

[10]

Rouse WB and Morris NM. On looking into the black box: prospects and limits in the search for mental models. Psychol Bull 1986; 100(3): 349–363.

[11]

Gan L, Bai C, and Zhu X. Analysis on information users’ mental model in the process of decision-making for choosing search strategies. J Chin Soc Sci Tech Inform 2010; 29(4): 641–651 (in Chinese).

[12]

Latta GF and Swigger K. Validation of the repertory grid for use in modelling knowledge. J Am Soc Inform Sci 1992; 43(2): 115–129.

[13]

Frohmann B. Cognitive paradigms and user needs. In: Proceedings of the 2nd international ISKO conference on cognitive paradigms in knowledge organization, Madras, India, 26–28 August 1992, pp. 35–50. Madras, India: University of Madras.

[14]

Han Z. Undergraduate students’ mental models of China’s comprehensive academic databases: an exploratory study. J Chin Soc Sci Tech Inform 2013; 33(4): 363–375 (in Chinese).

[15]

Nielsen J. A meta-model for interacting with computers. Interact Comput 1990; 2(2): 147–160.

Digital Library

[16]

Savage-Knepshield PA. Mental models: issues in construction, congruency and cognition. PhD Thesis, The State University of New Jersey, Camden, NJ, 2001.

[17]

Kanjug I and Chaijaroen S. The design of web-based learning environments enhancing mental model construction. Proced Soc Behav Sci 2012; 46: 3134–3140.

[18]

Hsu Y. The long-term effects of integral versus composite metaphors on experts’ and novices’ search behaviors. Interact Comput 2005; 17(4): 367–394.

Digital Library

[19]

Han Z and Xu H. An research in the driving factors for the evolution of users’ mental models of academic database. J Chin Soc Sci Tech Inform 2016; 35(7): 749–762 (in Chinese).

[20]

Byström K and Hansen P. Conceptual framework for task in information studies. J Am Soc Inform Sci Technol 2005; 56(10): 1050–1061.

Digital Library

[21]

Zhang Y. The development of users’ mental models of MedlinePlus in information searching. Libr Inform Sci Res 2013; 35(2): 159–170.

[22]

Glaser BG and Strauss AL. The discovery of ground theory: strategies for qualitative research. New York: Aldine de Gruyter, 1967.

[23]

Dimitroff A. Mental models and error behaviour in an interactive bibliographic retrieval system. PhD Thesis, University of Michigan, Ann Arbor, MI, 1990.

[24]

Solomon P. Children’s information retrieval behaviour: a case analysis of an OPAC. J Am Soc Inform Sci 1993; 44(5): 245–264.

[25]

Gray SH. Using protocol analyses and drawings to study mental model construction during hypertext navigation. Int J Hum Comput Interact 1990; 2(4): 359–378.

[26]

Makri S, Blandford A, and Gow J, et al. A library or just another information resource? A case study of users’ mental models of traditional and digital libraries. J Am Soc Inform Sci Technol 2007; 58(3): 433–445.

Digital Library

[27]

Kerr ST. Wayfinding in an electronic database: the relative importance of navigational cues vs. mental models. Inform Process Manage 1990; 26(4): 511–523.

Digital Library

[28]

Crudge SE and Johnson FC. Using the information seeker to elicit construct models for search engine evaluation. J Am Soc Inform Sci Technol 2004; 55(9): 794–806.

Digital Library

[29]

Crudge SE and Johnson FC. Using the repertory grid and laddering technique to determine the user’s evaluative model of search engines. J Document 2007; 63(2): 259–280.

[30]

Li P. Doctor students’ mental models of a web search engine: an exploratory study. PhD Thesis, McGill University, Montreal, QC, 2007.

[31]

Zhang Y. The impact of task complexity on people’s mental models of MedlinePlus. Inform Process Manage 2012; 48(1): 107–119.

Digital Library

[32]

Zhang Y. The influence of mental models on undergraduate students’ searching behaviour on the web. Inform Process Manage 2008; 44(3): 1330–1345.

Digital Library

[33]

Han Z. Research on influences factor and effect of undergraduate students’ mental models in the comprehensive academic database: taking CNKI as an example. Libr Inform Serv 2014; 58(21): 81–91 (in Chinese).

[34]

Dinet J and Kitajima M. ‘Draw me the web’: impact of mental model of the web on information search performance of young users. In: Proceedings of the 23rd French speaking conference on human–computer interaction, Sophia Antipolis, 24–27 October 2011, pp. 1–7. New York: Association for Computing Machinery.

[35]

Saxon S. Seventh grade students and electronic information retrieval systems: an exploratory study of mental model formation, completeness and change. PhD Thesis, University of North Carolina, Chapel Hill, NC, 1997.

[36]

Zhang Y. Dimensions and elements of people’s mental models of an information-rich web space. J Am Soc Inform Sci Technol 2010; 61(11): 2206–2218.

Digital Library

[37]

Han Z and Xu H. An empirical study on measurement for structure of mental models of undergraduate students using China’s comprehensive academic databases. J Chin Soc Sci Tech Inform 2014; 33(7): 740–751 (in Chinese).

[38]

Zhang X and Chignell M. Assessment of the effects of user characteristics on mental models of information retrieval systems. J Am Soc Inform Sci Technol 2001; 52(6): 445–459.

Digital Library

[39]

Lee J. The effects of visual metaphor and cognitive style for mental modeling in a hypermedia-based environment. Interact Comput 2007; 19(5–6): 614–629.

Digital Library

[40]

Darabi A, Hemphill J, and Nelson DW, et al. Mental model progression in learning the electron transport chain: effects of instructional strategies and cognitive flexibility. Adv Health Sci Educ 2010; 15(4): 479–489.

[41]

Darabi AA, Nelson DW, and Seel NM. Progression of mental models throughout the phases of a computer-based instructional simulation: supportive information, practice, and performance. Comput Hum Behav 2009; 25(3): 723–730.

Digital Library

[42]

Papastergiou M. Students’ mental models of the internet and their didactical exploitation in informatics education. Educ Inform Technol 2005; 10(4): 341–360.

Digital Library

[43]

Rowe AL and Cooke N. Measuring mental models: choosing the right tools for the job. Hum Resour Dev Quart 1995; 6(3): 243–255.

[44]

Pisanski J and Zumer M. Mental models of the bibliographic universe. Part 1: mental models of descriptions. J Document 2010; 66(5): 643–667.

[45]

Slone DJ. The influence of mental models and goals on search patterns during web interaction. J Am Soc Inform Sci Technol 2002; 53(13): 1152–1169.

Digital Library

[46]

Katzeff C. System demands on mental models for a fulltext database. Int J Man Mach Stud 1990; 32(5): 483–509.

Digital Library

[47]

Royer JM, Cisero CA, and Carlo MS. Techniques and procedures for assessing cognitive skills. Rev Educ Res 1993; 63(2): 201–243.

[48]

Thatcher A and Greyling M. Mental models of the internet. Int J Ind Ergon 1998; 22(4–5): 299–305.

[49]

Kodama C, St. Jean B, and Subramaniam M, et al. There’s a creepy guy on the other end at Google! Engaging middle school students in a drawing activity to elicit their mental models of Google. Inform Retr J 2017; 20(5): 403–432.

Digital Library

[50]

Westbrook L. Mental models: a theoretical overview and preliminary study. J Inform Sci 2006; 32(6): 563–579.

Digital Library

[51]

Holman L. Millennial students’ mental models of search: implications for academic librarians and database developers. J Acad Librariansh 2011; 37(1): 19–27.

[52]

Cole C, Lin Y, and Leide J, et al. 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. J Am Soc Inform Sci Technol 2007; 58(13): 2092–2104.

Digital Library

[53]

Mo Z and Ma F. Empirical study on database user’s satisfaction model of information resource content quality. J Libr Sci Chin 2013; 39(4): 85–97 (in Chinese).

[54]

Ding M and Hu Y. Comprehensive evaluation on knowledge discovery network platform of CNKI based on user experience. J Intell 2013; 32(10): 182–187 (in Chinese).

[55]

Li Y. Exploring the relationships between work task and search task in information search. J Am Soc Inform Sci Technol 2009; 60(2): 275–291.

Digital Library

[56]

Li Y and Belkin NJ. An exploration of the relationships between work task and interactive information search behavior. J Am Soc Inform Sci Technol 2010; 61(9): 1771–1789.

Digital Library

[57]

Marchionini G. Exploring search: from finding to understanding. Commun ACM 2006; 49(4): 41–46.

Digital Library

[58]

Kracker J and Wang P. Research anxiety and students’ perceptions of research: an experiment. Part II: content analysis of their writing on two experiments. J Am Soc Inform Sci Technol 2002; 54(3): 295–307.

Digital Library

[59]

Ellsworth PC and Scherer KP. Appraisal processes in emotion. In: Davidson RJ, Scherer KR, and Goldsmith H (eds) Handbook of affective science. Oxford: Oxford University Press, 2003.

[60]

Savolainen R. The role of emotions in online information seeking and sharing: a case study of consumer awareness. J Document 2015; 71(6): 1203–1227.

[61]

Nahl D. Affective load. In: Fisher KE, Erdelez S, and McKechnie L (eds) Theories of information behavior. Medford, NJ: Information Today, 2005, pp. 39–40.

[62]

Marchionini G. Making the transition from print to electronic encyclopedias: adaptation of mental models. Int J Man Mach Stud 1989; 30(6): 591–618.

Digital Library

[63]

Hsieh-Yee I. Research on web search behavior. Libr Inform Sci Res 2001; 23(2): 167–185.

[64]

Kim K-S and Allen B. Cognitive and task influences on web searching behaviour. J Am Soc Inform Sci Technol 2002; 53(2): 109–119.

Digital Library

[65]

Norman DA. Some observations on mental models. In: Gentner D and Stevens AL (eds) Mental models. Hillsdale, MI: Lawrence Erlbaum Associates, 1983, pp. 7–14.

[66]

Yan Z. Limited knowledge and limited resources: childrens’ and adolescents’ understanding of the internet. J Appl Dev Psychol 2009; 30(2): 103–115.

[67]

Lopatovska I and Arapakis I. Theories, methods and current research on emotions in library and information science, information retrieval and human–computer interaction. Inform Process Manage 2011; 47(4): 575–592.

Digital Library

[68]

Kim KS. Effects of emotion control and task on web searching behavior. Inform Process Manage 2008; 44(1): 373–385.

Digital Library

[69]

Savolainen R. Emotions as motivators for information seeking: a conceptual analysis. Libr Inform Sci Res 2014; 36(1): 59–65.

[70]

Kalbach J. ‘I’m feeling lucky’: the role of emotions in seeking information on the web. J Am Soc Inform Sci Technol 2006; 57(6): 813–818.

Digital Library

[71]

Tenopir C, Wang P, and Zhang Y, et al. Academic users’ interactions with ScienceDirect in search tasks: affective and cognitive behaviors. Inform Process Manage 2008; 44(1): 105–121.

Digital Library

[72]

Ford N, Miller D, and Moss N. The role of individual differences in internet searching: an empirical study. J Am Soc Inform Sci Technol 2001; 52(12): 1049–1066.

Digital Library

[73]

Papastergiou M and Solomonidou C. Gender issues in internet access and favourite internet activities among Greek high school pupils inside and outside school. Comput Educ 2005; 44(4): 377–393.

Digital Library

[74]

Kamala TN. Individual differences in the use of CD-ROM database. PhD Thesis, University of Hawaii, Honolulu, HI, 1991.

[75]

Kieras DE and Bovair S. The role of a mental model in learning to operate a device. Cogn Sci 1984; 8(3): 255–273.

[76]

Jansen BJ, Booth D, and Smith B. Using the taxonomy of cognitive learning to model online searching. Inform Process Manage 2009; 45(6): 643–663.

Digital Library

[77]

Hansen P and Rieh SY. Editorial: recent advances on searching as learning: an introduction to the special. J Inform Sci 2016; 42(1): 3–6.

Digital Library

[78]

Rieh SY, Collins-Thompson K, and Hansen P, et al. Towards searching as a learning process: a review of current perspectives and future directions. J Inform Sci 2016; 42(1): 19–34.

Digital Library

[79]

Gwizdka J and Chen X. Towards observable indicators of learning on search. In: Proceedings of the 2nd international workshop on search as learning, collocated with the 39th international ACM SIGIR conference, Pisa, 17–21 July 2016.

[80]

Freund L, Dodson S, and Kopak R. On measuring learning in search: a position paper. In: Proceedings of the 2nd international workshop on search as learning, collocated with the 39th international ACM SIGIR conference, Pisa, 17–21 July 2016.

[81]

Belkin NJ, Oddy RN, and Brooks HM. ASK for information retrieval: part 1: background and theory. J Document 1982; 38(2): 61–71.

[82]

Borlund P. A study of simulated work task situations in interactive information retrieval evaluations: a meta-evaluation. J Document 2016; 72(3): 394–413.

Cited By

Gao X(2024)Investigation into Usability Barriers Faced by Elderly Users and Aging-Appropriate Design of an Internet Hospital PlatformProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671236(468-475)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671236

Index Terms

Examining the classification and evolution of novice users’ mental models of an academic database in the search task completion process
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
2. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Journal of Information Science

Journal of Information Science Volume 46, Issue 2

Apr 2020

141 pages

ISSN:0165-5515

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© The Author(s) 2019.

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Sage Publications, Inc.

United States

Publication History

Published: 01 April 2020

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Cited By

Gao X(2024)Investigation into Usability Barriers Faced by Elderly Users and Aging-Appropriate Design of an Internet Hospital PlatformProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671236(468-475)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671236

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