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Journal of Computing in Higher Education

, Volume 27, Issue 2, pp 134–157 | Cite as

Students’ evaluation strategies in a Web research task: Are they sensitive to relevance and reliability?

  • Héctor García Rodicio
Article
First Online:

Abstract

When searching and using resources on the Web, students have to evaluate Web pages in terms of relevance and reliability. This evaluation can be done in a more or less systematic way, by either considering deep or superficial cues of relevance and reliability. The goal of this study was to examine how systematic students are when evaluating Web pages. Forty-two undergraduate students performed a Web research task. They were provided with a search-engine results page including 12 Web pages, which were relevant/irrelevant and reliable/unreliable. The students had to navigate the pages for 30 min to prepare for a subsequent test on a target topic. Their navigation behaviors were video-recorded and analyzed. The findings revealed that the students were not systematic when accessing the pages: They accessed more unreliable pages than reliable pages. The pattern was different when considering study time: The students allocated more time to relevant and reliable pages. Finally, allocating additional time to relevant Web pages was associated with better learning from the materials. Overall, the results suggest that undergraduate students are able to deploy systematic evaluations of Web resources and this is crucial for achieving learning.

Keywords

World Wide Web Relevance Trustworthiness Evaluation strategies Web literacy Information problem solving 
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References

  1. Anmarkrud, Ø., McCrudden, M. T., Braten, I., & Strømsø, H. (2013). Task-oriented reading of multiple documents: Online comprehension processes and offline products. Instructional Science, 41, 873–894.CrossRefGoogle Scholar
  2. Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39, 775–786.CrossRefGoogle Scholar
  3. Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using internet. Computers & Education, 53, 1207–1217.CrossRefGoogle Scholar
  4. Bråten, I., Strømsø, H., & Salmerón, L. (2011). Trust and mistrust when students read multiple information sources about climate change. Learning and Instruction, 21, 180–192.CrossRefGoogle Scholar
  5. Duggan, G. B., & Payne, S. J. (2009). Text skimming: The process and effectiveness of foraging through text under time pressure. Journal of Experimental Psychology: Applied, 15, 228–242.Google Scholar
  6. European-Union. (2012). Internet use in households and by individuals in 2012. Eurostat: Statistics in focus, 50.Google Scholar
  7. Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning-by-teaching and teaching-expectancy. Contemporary Educational Psychology, 38, 281–288.CrossRefGoogle Scholar
  8. Gerjets, P., Kammerer, Y., & Werner, B. (2011). Measuring spontaneous and instructed evaluation processes during Web search: Integrating concurrent thinking-aloud protocols and eye-tracking data. Learning and Instruction, 21, 220–231.CrossRefGoogle Scholar
  9. Graesser, A. C., & Lehman, B. A. (2011). Questions drive comprehension of text and multimedia. In M. T. McCrudden, J. P. Magliano, & G. Schraw (Eds.), Text relevance and learning from text (pp. 53–74). Greenwich: Information Age Publishing.Google Scholar
  10. Head, A. J., & Eisenberg, M. B. (2010). How today’s college students use Wikipedia for course-related research. First Monday, 15.Google Scholar
  11. Kahneman, D., & Frederick, S. (2005). A model of heuristic judgment. In K. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 267–294). Cambridge: Cambridge University Press.Google Scholar
  12. Kammerer, Y., Bråten, I., Gerjets, P., & Strømsø, H. (2013). The role of Internet-specific epistemic beliefs in laypersons’ source evaluations and decisions during Web search on a medical issue. Computers in Human Behavior, 29, 1193–1203.CrossRefGoogle Scholar
  13. Lewandowski, D. (2012). Credibility in Web search engines. In S. Apostel & M. Folk (Eds.), Online credibility and digital ethos: Evaluating computer-mediated communication. Hershey, PA: IGI Global.Google Scholar
  14. Lucassen, T., & Schraagen, J. M. (2013). The influence of source cues and topic familiarity on credibility evaluation. Computers in Human Behavior, 29, 1387–1392.CrossRefGoogle Scholar
  15. McCrudden, M. T., Magliano, J. P., & Schraw, G. (2010). Exploring how relevance instructions affect personal reading intentions, reading goals and text processing: A mixed methods study. Contemporary Educational Psychology, 35, 229–241.CrossRefGoogle Scholar
  16. McCrudden, M. T., & Schraw, G. (2007). Relevance and goal-focusing in text processing. Educational Psychology Review, 19, 113–139.CrossRefGoogle Scholar
  17. OECD. (2011). PISA 2009 results: Students on line: Digital technologies and performance (Vol. VI). Paris: OECD.CrossRefGoogle Scholar
  18. Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In Google we trust: Users’ decisions on rank, position, and relevance. Journal of Computer-Mediated Communication, 12, 801–823.CrossRefGoogle Scholar
  19. Pingdom. (2013, January). Internet 2012 in numbers. Royal Pingdom, Retrieved June 17, 2013 from http://royal.pingdom.com/2013/2001/2016/internet-2012-in-numbers
  20. Rieh, S. Y., Kim, Y. M., & Markey, K. (2012). Amount of invested mental effort (AIME) in online searching. Information Processing and Management, 48, 1136–1150.CrossRefGoogle Scholar
  21. Rouet, J.-F., Ros, C., Goumi, A., Macedo-Rouet, M., & Dinet, J. (2011). The influence of surface and deep cues on primary and secondary school students’ assessment of relevance in Web menus. Learning and Instruction, 21, 205–219.CrossRefGoogle Scholar
  22. Salmerón, L., Kammerer, Y., & García-Carrión, P. (2013). Searching the Web for conflicting topics: Page and user factors. Computers in Human Behavior, 29, 2161–2171.CrossRefGoogle Scholar
  23. van den Broek, P., Lorch, R. F, Jr, Linderholm, T., & Gustafson, M. (2001). The effects of readers’ goals on inference generation and memory for texts. Memory and Cognition, 29, 1081–1087.CrossRefGoogle Scholar
  24. Walraven, A., Brand-Gruwel, S., & Boshuizen, H. P. A. (2008). Information- problem solving: A review of problems students encounter and instructional solutions. Computers in Human Behavior, 24, 623–648.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of EducationUniversity of CantabriaSantanderSpain

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