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.
NahlD.Social-biological information technology: an integrated conceptual framework. J Am Soc Inform Sci Technol2007; 58(13): 2021–2046.
2.
KuhlthauCC.Inside the search process: information seeking from the user’s perspective. J Am Soc Inform Sci1991; 42(5): 361–371.
3.
SavolainenR.The interplay of affective and cognitive factors in information seeking and use: comparing Kuhlthau’s and Nahl’s models. J Document2015; 71(1): 175–197.
4.
SavolainenR.Approaching the affective barriers to information seeking: the viewpoint of appraisal theory. Inform Res2016; 21(4): 1–20.
5.
NahlDTenopirC.Affective and cognitive searching behaviour of novice end-users of a full-text database. J Am Soc Inform Sci1996; 47(4): 276–286.
6.
WilsonTD.On user studies and information needs. J Document1981; 37(1): 3–15.
7.
BorgmanCL.The user’s mental model of an information retrieval system: an experiment on a prototype online catalog. Int J Man Mach Stud1986; 24(1): 47–64.
8.
ZhangX.A study of the effects of user characteristics on mental models of information retrieval systems. PhD Thesis, University of Toronto, Toronto, ON, 1998.
9.
CraikK.The nature of explanation. Cambridge: Cambridge University Press, 1943.
10.
RouseWBMorrisNM.On looking into the black box: prospects and limits in the search for mental models. Psychol Bull1986; 100(3): 349–363.
11.
GanLBaiCZhuX.Analysis on information users’ mental model in the process of decision-making for choosing search strategies. J Chin Soc Sci Tech Inform2010; 29(4): 641–651 (in Chinese).
12.
LattaGFSwiggerK.Validation of the repertory grid for use in modelling knowledge. J Am Soc Inform Sci1992; 43(2): 115–129.
13.
FrohmannB.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.
HanZ.Undergraduate students’ mental models of China’s comprehensive academic databases: an exploratory study. J Chin Soc Sci Tech Inform2013; 33(4): 363–375 (in Chinese).
15.
NielsenJ.A meta-model for interacting with computers. Interact Comput1990; 2(2): 147–160.
16.
Savage-KnepshieldPA.Mental models: issues in construction, congruency and cognition. PhD Thesis, The State University of New Jersey, Camden, NJ, 2001.
17.
KanjugIChaijaroenS.The design of web-based learning environments enhancing mental model construction. Proced Soc Behav Sci2012; 46: 3134–3140.
18.
HsuY.The long-term effects of integral versus composite metaphors on experts’ and novices’ search behaviors. Interact Comput2005; 17(4): 367–394.
19.
HanZXuH.An research in the driving factors for the evolution of users’ mental models of academic database. J Chin Soc Sci Tech Inform2016; 35(7): 749–762 (in Chinese).
20.
ByströmKHansenP.Conceptual framework for task in information studies. J Am Soc Inform Sci Technol2005; 56(10): 1050–1061.
21.
ZhangY.The development of users’ mental models of MedlinePlus in information searching. Libr Inform Sci Res2013; 35(2): 159–170.
22.
GlaserBGStraussAL.The discovery of ground theory: strategies for qualitative research. New York: Aldine de Gruyter, 1967.
23.
DimitroffA.Mental models and error behaviour in an interactive bibliographic retrieval system. PhD Thesis, University of Michigan, Ann Arbor, MI, 1990.
24.
SolomonP.Children’s information retrieval behaviour: a case analysis of an OPAC. J Am Soc Inform Sci1993; 44(5): 245–264.
25.
GraySH.Using protocol analyses and drawings to study mental model construction during hypertext navigation. Int J Hum Comput Interact1990; 2(4): 359–378.
26.
MakriSBlandfordAGowJ, 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 Technol2007; 58(3): 433–445.
27.
KerrST. Wayfinding in an electronic database: the relative importance of navigational cues vs. mental models. Inform Process Manage1990; 26(4): 511–523.
28.
CrudgeSEJohnsonFC.Using the information seeker to elicit construct models for search engine evaluation. J Am Soc Inform Sci Technol2004; 55(9): 794–806.
29.
CrudgeSEJohnsonFC.Using the repertory grid and laddering technique to determine the user’s evaluative model of search engines. J Document2007; 63(2): 259–280.
30.
LiP.Doctor students’ mental models of a web search engine: an exploratory study. PhD Thesis, McGill University, Montreal, QC, 2007.
31.
ZhangY.The impact of task complexity on people’s mental models of MedlinePlus. Inform Process Manage2012; 48(1): 107–119.
32.
ZhangY.The influence of mental models on undergraduate students’ searching behaviour on the web. Inform Process Manage2008; 44(3): 1330–1345.
33.
HanZ.Research on influences factor and effect of undergraduate students’ mental models in the comprehensive academic database: taking CNKI as an example. Libr Inform Serv2014; 58(21): 81–91 (in Chinese).
34.
DinetJKitajimaM. ‘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.
SaxonS.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.
ZhangY.Dimensions and elements of people’s mental models of an information-rich web space. J Am Soc Inform Sci Technol2010; 61(11): 2206–2218.
37.
HanZXuH.An empirical study on measurement for structure of mental models of undergraduate students using China’s comprehensive academic databases. J Chin Soc Sci Tech Inform2014; 33(7): 740–751 (in Chinese).
38.
ZhangXChignellM.Assessment of the effects of user characteristics on mental models of information retrieval systems. J Am Soc Inform Sci Technol2001; 52(6): 445–459.
39.
LeeJ.The effects of visual metaphor and cognitive style for mental modeling in a hypermedia-based environment. Interact Comput2007; 19(5–6): 614–629.
40.
DarabiAHemphillJNelsonDW, et al. Mental model progression in learning the electron transport chain: effects of instructional strategies and cognitive flexibility. Adv Health Sci Educ2010; 15(4): 479–489.
41.
DarabiAANelsonDWSeelNM.Progression of mental models throughout the phases of a computer-based instructional simulation: supportive information, practice, and performance. Comput Hum Behav2009; 25(3): 723–730.
42.
PapastergiouM.Students’ mental models of the internet and their didactical exploitation in informatics education. Educ Inform Technol2005; 10(4): 341–360.
43.
RoweALCookeN.Measuring mental models: choosing the right tools for the job. Hum Resour Dev Quart1995; 6(3): 243–255.
44.
PisanskiJZumerM.Mental models of the bibliographic universe. Part 1: mental models of descriptions. J Document2010; 66(5): 643–667.
45.
SloneDJ.The influence of mental models and goals on search patterns during web interaction. J Am Soc Inform Sci Technol2002; 53(13): 1152–1169.
46.
KatzeffC.System demands on mental models for a fulltext database. Int J Man Mach Stud1990; 32(5): 483–509.
47.
RoyerJMCiseroCACarloMS.Techniques and procedures for assessing cognitive skills. Rev Educ Res1993; 63(2): 201–243.
48.
ThatcherAGreylingM.Mental models of the internet. Int J Ind Ergon1998; 22(4–5): 299–305.
49.
KodamaCSt. JeanBSubramaniamM, 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 J2017; 20(5): 403–432.
50.
WestbrookL.Mental models: a theoretical overview and preliminary study. J Inform Sci2006; 32(6): 563–579.
51.
HolmanL.Millennial students’ mental models of search: implications for academic librarians and database developers. J Acad Librariansh2011; 37(1): 19–27.
52.
ColeCLinYLeideJ, 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 Technol2007; 58(13): 2092–2104.
53.
MoZMaF.Empirical study on database user’s satisfaction model of information resource content quality. J Libr Sci Chin2013; 39(4): 85–97 (in Chinese).
54.
DingMHuY.Comprehensive evaluation on knowledge discovery network platform of CNKI based on user experience. J Intell2013; 32(10): 182–187 (in Chinese).
55.
LiY.Exploring the relationships between work task and search task in information search. J Am Soc Inform Sci Technol2009; 60(2): 275–291.
56.
LiYBelkinNJ.An exploration of the relationships between work task and interactive information search behavior. J Am Soc Inform Sci Technol2010; 61(9): 1771–1789.
57.
MarchioniniG.Exploring search: from finding to understanding. Commun ACM2006; 49(4): 41–46.
58.
KrackerJWangP.Research anxiety and students’ perceptions of research: an experiment. Part II: content analysis of their writing on two experiments. J Am Soc Inform Sci Technol2002; 54(3): 295–307.
59.
EllsworthPCSchererKP.Appraisal processes in emotion. In: DavidsonRJSchererKRGoldsmithH (eds) Handbook of affective science. Oxford: Oxford University Press, 2003.
60.
SavolainenR.The role of emotions in online information seeking and sharing: a case study of consumer awareness. J Document2015; 71(6): 1203–1227.
61.
NahlD.Affective load. In: FisherKEErdelezSMcKechnieL (eds) Theories of information behavior. Medford, NJ: Information Today, 2005, pp. 39–40.
62.
MarchioniniG.Making the transition from print to electronic encyclopedias: adaptation of mental models. Int J Man Mach Stud1989; 30(6): 591–618.
63.
Hsieh-YeeI.Research on web search behavior. Libr Inform Sci Res2001; 23(2): 167–185.
64.
KimK-SAllenB.Cognitive and task influences on web searching behaviour. J Am Soc Inform Sci Technol2002; 53(2): 109–119.
65.
NormanDA.Some observations on mental models. In: GentnerDStevensAL (eds) Mental models. Hillsdale, MI: Lawrence Erlbaum Associates, 1983, pp. 7–14.
66.
YanZ.Limited knowledge and limited resources: childrens’ and adolescents’ understanding of the internet. J Appl Dev Psychol2009; 30(2): 103–115.
67.
LopatovskaIArapakisI.Theories, methods and current research on emotions in library and information science, information retrieval and human–computer interaction. Inform Process Manage2011; 47(4): 575–592.
68.
KimKS.Effects of emotion control and task on web searching behavior. Inform Process Manage2008; 44(1): 373–385.
69.
SavolainenR.Emotions as motivators for information seeking: a conceptual analysis. Libr Inform Sci Res2014; 36(1): 59–65.
70.
KalbachJ.‘I’m feeling lucky’: the role of emotions in seeking information on the web. J Am Soc Inform Sci Technol2006; 57(6): 813–818.
71.
TenopirCWangPZhangY, et al. Academic users’ interactions with ScienceDirect in search tasks: affective and cognitive behaviors. Inform Process Manage2008; 44(1): 105–121.
72.
FordNMillerDMossN.The role of individual differences in internet searching: an empirical study. J Am Soc Inform Sci Technol2001; 52(12): 1049–1066.
73.
PapastergiouMSolomonidouC.Gender issues in internet access and favourite internet activities among Greek high school pupils inside and outside school. Comput Educ2005; 44(4): 377–393.
74.
KamalaTN.Individual differences in the use of CD-ROM database. PhD Thesis, University of Hawaii, Honolulu, HI, 1991.
75.
KierasDEBovairS.The role of a mental model in learning to operate a device. Cogn Sci1984; 8(3): 255–273.
76.
JansenBJBoothDSmithB.Using the taxonomy of cognitive learning to model online searching. Inform Process Manage2009; 45(6): 643–663.
77.
HansenPRiehSY.Editorial: recent advances on searching as learning: an introduction to the special. J Inform Sci2016; 42(1): 3–6.
78.
RiehSYCollins-ThompsonKHansenP, et al. Towards searching as a learning process: a review of current perspectives and future directions. J Inform Sci2016; 42(1): 19–34.
79.
GwizdkaJChenX. 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.
FreundLDodsonSKopakR. 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.
BelkinNJOddyRNBrooksHM.ASK for information retrieval: part 1: background and theory. J Document1982; 38(2): 61–71.
82.
BorlundP.A study of simulated work task situations in interactive information retrieval evaluations: a meta-evaluation. J Document2016; 72(3): 394–413.
