Employing Eye Tracking in Quantifying and Qualifying Visual Attention of Web Site Viewers (Physical Education Faculties)

Document Type : Original research papers


1 Assistant Professor, Department of physical education and sport sciences, University of Ferdosi,Mashhad, Iran

2 Assistant Professor, Department of physical education and sport sciences, Faculty of education sciences and psychology, University of Mohaghegh Ardabili, Ardabil, Iran

3 Sport Management, Faculty of Sport Science, Ferdowsi University, Mashhad, Iran

4 Department of Sport Management, Faculty of Sport Science, Ferdowsi University of Mashhad, Mashhad, Iran.


In recent years, the web has emerged as an ideal media, that knowledge and information through which are effectively disseminated around the world. The purpose of this research is to study the websites of physical education and sports sciences with neuro marketing approach. The research method of this study was semi-empirical. The statistical population consisted of Ferdowsi University of Mashhad (FUM) students and other universities and educational institutions active in Mashhad that signed the consent form to participate in the study. Among selected individuals, 28 people were as research samples. In this research, Be Gaze software has been used to convert eye tracker data into quantitative data and to test the hypotheses of research we used repeated measure analysis and SPSS 21. The analysis showed that there was a significant difference between all areas in time of fixation at them; it also had significant differences between all areas in number of fixation, except the right menu and the header menu. FUM student did not affect the number and duration fixation of people at the affected areas, and FUM students were not biased toward their college. In relation to gender, the data showed that gender did not have a significant difference in the number of fixation, but gender was significantly different on the duration of fixation at all areas. In general, based on the areas of interest (AOI), FUM reach ranked among the other universities in a variety of factor and, in general, was better and more fully evaluated.


Website, Top Universities, Faculty of physical education, Eye tracker, visual attention, Iran

Main Subjects

1.         Erfan Manesh MA, Didgah F. Apparent estimation, impact factor and rate of visits to websites of Iranian medical sciences universities. Message of library journal, 2008;15(3):169-94.
2.         Wang Q, Yang S, Liu M, Cao Z, Ma Q. An eye-tracking study of website complexity from cognitive load perspective. Decision support systems, 2014;62:1-10.
3.         Amiri MR, Karami S, Farhadi A, Rezayi N, Zareeyan S. Evaluation of hospitals’ website of Hamedan university of medical sciences based on webometrics  criteria in 2014. Pajouhan Scientific Journal, 2016;14(2):53-61.
4.         Lu K, Joo S, Wolfram D. An investigation of web resource distribution in the field of information science. Cybermetrics: International Journal of Scientometrics, Informetrics and Bibliometrics, 2011; (15):1-6.
5.         Şengel E, Öncü S. Conducting preliminary steps to usability testing: investigating the website of Uludağ University. Procedia-Social and Behavioral Sciences, 2010;2(2):890-4.
6.         Pareek S, Gupta DK. Academic Library Websites in Rajasthan: an analysis of Content. Library Philosophy & Practice, 2013.
7.         Danesh F, Soheil F, Karami NA, Zareyi A. Core Web Sites of Universities of Islamic world Countries' Capitals, Information processing & Management. 2012;27(3):759-76.
8.         Sheibani M, Eskrootchi R, Hajizeinolabedini M, Hosseini A. Ranking of Iranian Pharmacy School Websites Based on Web Impact Factor. Health Management, 2012;15(50):41-51.
9.         Khlaisang J. Based guidelines for evaluating educational service website: case study of Thailand cyber university project. Procedia-Social and Behavioral Sciences, 2015;174:751-8.
10.       Mentes SA, Turan AH. Assessing the usability of university websites: An empirical study on Namik Kemal University. Turkish Online Journal of Educational Technology-TOJET, 2012;11(3):61-9.
11.       Zahedi S. Research on websites of five major Iranian universities and presenting a suitable model. Information Technology Management, 2010;3(6):21-44.
12.       Hasan L, Morris A, Probets S. A comparison of usability evaluation methods for evaluating e-commerce websites. Behaviour & Information Technology, 2012;31(7):707-37.
13.       Mustafa SH, Al-Zoua’bi LF. Usability of the academic websites of Jordan's universities an evaluation study. In 9th International Arab Conference for Information Technology; 2008. Tunisia, 1-8.
14.       Alahmadi T, Drew S. An evaluation of the accessibility of top-ranking university websites: Accessibility rates from 2005 to 2015. Journal of Open, Flexible, and Distance Learning, 2017;21(1):7-24.
15.       Ismail A, Kuppusamy K. Accessibility of Indian universities’ homepages: An exploratory study. Journal of King Saud University-Computer and Information Sciences, 2018;30(2):268-78.
16.       Katiliute E, Daunoriene A. Dissemination of Sustainable Development on Universities Websites ‘. Procedia-Social and Behavioral Sciences, 2015;191:865-71.
17.       Ahmi A, Mohamad R, editors. Web accessibility of the malaysian public university websites. Proceedings of International Conference on E-Commerce; 2015.
18.       Khodadadi MR, Sarlab R, Bejani A. Webometrics of Physical Education Faculties of Iranian State Universities by TOPSIS and VIKOR Techniques. Research on Educational Sport, 2016;4(10):57-80.
19.       Zahed A, Ghazavi R, Otraj Z, Taheri B, Soleymanzadeh Najafi N, Mazaheri E. The Webometric Status of Isfahan University of Medical Sciences. Journal of Isfahan Medical School, 2013;31(254):1548-59.
20.       Seif F, Oskuyi Zadeh R. The effect of menu position on the visual attention of website users. Quarterly Journal of Scientific – Research of Iran Information and Communication Technology, 2014;18:43-56.
21.       Rashid S, Soo ST, Sivaji A, Naeni HS, Bahri S. Preliminary usability testing with eye tracking and FCAT analysis on occupational safety and health websites. Procedia-Social and Behavioral Sciences, 2013;97:737-44.
22.       Nooghabi MZ, Fattahi R, Salehi Fadardi J, Nowkarizi M. Eye Tracking Method in Human-Computer Interaction: Assessing the Interaction based on the Eye Movement Data. Iranian Journal of information processing and management, 2017;34(1):349-74.
23.       Weichbroth P, Redlarski K, Garnik I. Eye-tracking web usability research. Proceeding Federated Conference on Computer Science and Information Systems (FedCSIS); 2016: 1681-1684.
24.       Velásquez JD. Combining eye-tracking technologies with web usage mining for identifying Website Keyobjects. Engineering Applications of Artificial Intelligence, 2013;26(5-6):1469-78.
25.       Law R. Evaluation of hotel websites: Progress and future developments. International Journal of Hospitality Management, 2019;76:2-9.
26.       Al Maqbali H. Using eye tracking for evaluation of information visualisation in web search interfaces. In school of Computer Science and Information Technology, College of science, Engineering and Health. 2013, RMIT University, Melborne, Australia.
27.       Ortega JL, Aguillo IF. Mapping world-class universities on the web. Information Processing & Management, 2009;45(2):272-9.
28.       Grier R, Kortum P, Miller J, How users view web pages: An exploration of cognitive and perceptual mechanisms.  Human computer interaction research in Web design and evaluation: IGI Global; 2007. p. 22-41.
29.       Djamasbi S, Siegel M, Tullis T, Generation Y. Web design, and eye tracking. International journal of human-computer studies, 2010;68(5):307-23.
30.       Faraday P. Visually critiquing web pages. In: Correia N., Chambel, T., Davenport, G. (eds), Multimedia’99; 2000: 155-166.
31.       Grier RA. Visual attention and web design. University of Cincinnati; 2004.
32.       Roth SP, Tuch AN, Meklerm ED, Bargas-Avilam JA, Opwis K. Location matters, especially for non-salient features–An eye-tracking study on the effects of web object placement on different types of websites. International journal of human-computer studies, 2013;71(3):228-35.
33.       Still JD. Web page visual hierarchy: Examining Faraday's guidelines for entry points. Computers in Human Behavior, 2018;84:352-9.
34.       Kalbach J, Bosenick T. Web page layout: A comparison between left-and right-justified site navigation menus. Journal of Digital Information, 2003;4(1).
35.       Bojko A. Eye tracking the user experience: A practical guide to research. Booklin, NY: Rosenfeld Media; 2013.
36.       Tzafilkou K, Protogeros N. Diagnosing user perception and acceptance using eye tracking in web-based end-user development. Computers in Human Behavior, 2017;72:23-37.
37.       Roth SP, Schmutz P, Pauwels SL, Bargas-Avila JA, Opwis K. Mental models for web objects: Where do users expect to find the most frequent objects in online shops, news portals, and company web pages? Interacting with computers, 2009;22(2):140-52.
38.       Hartzel K. How self-efficacy and gender issues affect software adoption and use. Communications of the ACM, 2003;46(9):167-71.
39.       Teo T, Fan X, Du J. Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology, 2015;31(3).
40.       Terzis V, Economides AA. Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 2011;27(6):2108-22.