Investigation of Factors Affecting Student's Mental Model Completeness Level of Google Web Search Engine

Document Type : Research Article

Authors

• hassan behzadi ¹
• azam safari ²
• Iraj Radad ³

¹ Ferdowsi University of Mashhad

² ferdowsi university

³ Imam Reza International University

10.22067/riis.v7i2.58655

Abstract

INTRODUCTION: A user's mental model is his/her mental image of the system and how it operates. Studies have demonstrated that users' information seeking behavior is different and this difference is depended on the users' mental models of the system. Indeed, people’s thinking, behavior, and activities are guided by their mental models in the interactive contexts. Furthermore, there are different factors that influence mental model formation of the system. So, this study aims to investigate the factors affecting master students̓ mental model completeness level of Google web search engine.

METHODOLOGY: From the methodological perspective, this research is a practical one based on the survey method. The research sample consisted of 30 master students from Ferdowsi University of Mashhad majoring in engineering science and social/human sciences who were selected purposefully and participated voluntarily. The main tools for gathering data are semi-structured interviews (based on mental model completeness scale (Li, 2007)), group embedded figure test, Kolb learning style inventory and information literacy questionnaire. Therefore, students' mental models scores were measured by interviewing and after that the relationship between their scores and other research variables was investigated according to the following questions:
1. Is there a significant relationship between students̓ mental model completeness level of Google web search engine and their cognitive style?
2. Is there a significant difference between students̓ mental model completeness level of Google web search engine and their learning style?
3. Is there a significant relationship between students̓ mental model completeness level of Google web search engine and their information literacy?
4. Is there a significant difference between students̓ mental model completeness level of Google web search engine and their academic fields?
5. Is there a significant difference between students̓ mental model completeness level of Google web search engine and their gender?

FINDINGS: The results showed that there was a significant relationship between students' mental model completeness level and cognitive style. In other words, field independent students had more complete mental models than field dependent students. On the other hand, there was no significant difference between students' learning style and mental model completeness level. It was also observed no significant relationship between their mental model completeness level and information literacy, but there was a significantly, positive and moderate relationship between their mental model completeness level and information retrieval component. Also, there was a significant difference between students' academic field and mental model completeness level, so that the students majoring in engineering sciences had better mental models than students majoring in social/human sciences. On the other hand, there was no significant difference between students' gender and mental model completeness level.

CONCLUSIONS: According to the findings, it is important to consider factors affecting mental models in designing user-friendly interfaces, personalization of systems and user training. In such a way, our systems and user trainings become more effective. By designing systems based on the cognitive style, users can develop better mental models, have more effective search strategies and be more satisfied. Furthermore, investigating the relationship between learning style, information literacy, gender and mental model requires more researches with more participants.

Keywords

• Mental model
• Google search engine
• Li scale (2007)
• Cognitive style
• Learning Style
• Information literacy
• Completeness level
• Academic field
• gender

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References

امامی پور، سوزان؛ شمس اسفندآباد، حسن (1386). سبک‌های یادگیری و شناختی نظریه‌ها و آزمون‌ها. تهران: سازمان مطالعه و تدوین کتب علوم انسانی دانشگاه‌ها.

رجبعلی بگلو، رضا؛ فتاحی، رحمت‌الله؛ و پریرخ، مهری (1395). تأثیر نظام‌های اطلاعاتی بر شکل‌گیری مدل‌های ذهنی کاربران کتابخانه‌های دیجیتالی. مطالعات ملی کتابداری سازماندهی اطلاعات. 27 (2)، 21-39.

رهروانی، ساناز؛ میرزابیگی، مهدیه و عباس‌پور، جواد (زودآیند). مطالعه عوامل تأثیرگذار بر مدل ذهنی کاربران از آیکون‌های نرم‌افزارهای کتابخانه دیجیتال. پژوهشنامه پردازش و مدیریت اطلاعات.

زره‌ساز، محمد، (1394). واکاوی ارتباط میان قابلیت‌های روانشناختی و مهارتی کاربران با رفتار راهنمایابی آنها در کتابخانه دیجیتال بر پایه مدل تعدیل‌شده اطلاع‌یابی مارکیونینی. پایان‌نامه دکترای گروه کتابداری و اطلاع‌رسانی، دانشکده علوم تربیتی و روان‌شناسی دانشگاه فردوسی.

صفری، اعظم؛ بهزادی، حسن؛ رداد، ایرج (زودآیند). بررسی مدل‌های ذهنی دانشجویان از موتور کاوش گوگل. پژوهشنامه پردازش و مدیریت اطلاعات.

میرزابیگی، مهدیه، (1392). مدل ذهنی در پژوهش‌های رفتار اطلاع جویی پژوهشی در متون. تحقیقات کتابداری و اطلاع‌رسانی دانشگاهی، 47(3) ص 303-323

میرزابیگی، مهدیه، (1391). بررسی قضاوت ربط و معیارهای مورد استفاده دانشجویان در فرایند گزینش و ارزیابی منابع اطلاعاتی بر پایه نظریه سبک‌شناختی کل گرا- تحلیلی رایدینگ. پایان‌نامه دکترای گروه کتابداری و اطلاع‌رسانی، دانشکده علوم تربیتی و روان‌شناسی دانشگاه فردوسی.

Bawden D. Robinson L. (2011). Individual Differences in Information-Related Behaviour. In: A. Spink & J. Heinstrom (Eds.), New Directions in Information Behaviour. Library and Information Science, 1. (pp. 127-158). London, UK: Emerald.

Bruce, C. (2000). Information literacy research: dimensions of the emerging collective consciousness. Australian Academic & Research Libraries, 31(2), 91-109.

Dimitroff, Alexandra. (1990). Mental models and error behavior in an interactive bibliographic retrieval system. Doctoral dissertation, University of Michigan.

Fidel, R. (2012). Human Information Interaction: An Ecological Approach to Information Behavior. London: Massachusetts Institute of Technology.

Ford, N., Miller, D. & Moss N. 2005. Web search strategies and human individual differences: A combined analysis. Journal of the American Society for Information Science and Technology 56(7): 757 -764.

Ford, N., Miller, D., & Moss N. (2002). The role of individual differences in Internet searching: An empirical study. Journal of the American Society for Information Science and Technology, 52(12):1049-1066

Goodale, P., Clough, D., Fernando, S., Ford, N., & Stevenson, M. (2014). Cognitive styles within an exploratory search system for digital libraries. Journal of Documentation, 70(6), 970-996.

Huang, C., & Xie, I. (2011). Help feature interactions in digital libraries: Influence of learning styles. Proceedings of the American Society for Information Science and Technology, 48(1), 1-10.

Karuppan, C. N. (2011). Learning and Forgetting: Implications for Workforce Flexibility in AMT Environments. In.M. Y. Jaber (Ed.), Learning Curves: Theory, Models, and Applications (pp. 173-190), FL: Taylor & Francis Group

Li, P. (2007). Doctoral students’ mental models of a web search engine: An exploratory study. Doctoral dissertation, Mc Gill university.

Lorigo, L., Pan, B., Hembrooke, H., Joachims, T., Granka, L., & Gay, G. (2006). The influence of task and gender on search and evaluation behavior using Google. Information Processing & Management, 42(4), 1123-1131.

Marchionini, G. (1995). Information seeking in electronic environments. New York: Cambridge University Press

Norman, D. A. (1983). Some observations on mental models. In D. Gentner & A. L. Stevens (Eds.), Mental Models (pp. 7-14). Hillsdale, NJ: Erlbaum

Orooji, F, Taghiyareh, F and Rahimi, Z. (2011). "An Integrated Environment for Providing Learning Style Information in a Unified Manner." 10th European Conf.on E-learning (ECEL2011), Brighton, November 10-11.

Palmquist, Ruth A. and Kim, K.S. (2000). Cognitive style and on-line database search experience as predictors of Web search performance. Journal of the American Society for Information Science 51(6): 558-566.

Pitts, J. M., McGregor, J. H., & Stripling, B. K. (1995). Mental models of information: The 1993-94 AASL/Highsmith research award study. School Library Media Quarterly, 23, 177-177.

Saxon, S. (1997). Seventh Grade Students and Electronic Information Retrieval Systems: An Exploratory Study of Mental Model Formation,Completeness and Change. (Doctoral dissertation, University of North Carolina).Spink, A., & Zimmer, M. (Eds.). (2008). Web search: Multidisciplinary perspectives. Berlin: Springer.

Spink, A., & Zimmer, M. (Eds.). (2008). Web search: Multidisciplinary perspectives. Berlin: Springer.

Westbrook, L. (2006). Mental models: A theoretical overview and preliminary study. Journal of Information Science, 32 (6), 563-579.

Worth, J. & Fidler, C. (1997). Exploring the effects of learning style on the use of an electronic library system. Proceedings of the 4th Electronic Library and Visual Information Research Conference, ELVIRA4, De Montfort University, London, Aslib, 1997: 83-92.

Yuan, X., Zhang, X., Chen, c., & Avery. J. (2011). Seeking information with an information visualization system: a study of cognitive styles. Information Research, 16(4), 5-5.

Zhang, X. (1998). A study of the effects of user characteristics on mental models of information retrieval systems. Doctoral dissertation, University of Toronto.

Zhang, X., & Chignell, M. (2001). Assessment of the effects of user characteristics on mental models of information retrieval systems. Journal of the American society for information science and technology, 52(6), 445-459.

Zhang, Y. (2008). Undergraduate students' mental models of the Web as an information retrieval system. Journal of the American Society for Information Science and Technology, 59(13), 2087–2098.

Zhang, Y. (2012). The impact of task complexity on people’s mental models of MedlinePlus. Information Processing & Management, 48(1), 107-119.

Zhang,Y. (2009). The Construction of Mental Models of Information-rich Web Spaces: The Development Process and the Impact of Task Complexity. (Doctoral dissertation, University of North Carolina).