Identifying Patterns in Eyetracking Scanpaths in Terms of Visual Elements of Web Pages

Eraslan, Şükrü; Yeşi̇lada, Yeli̇z; Harper, Simon

doi:10.1007/978-3-319-08245-5_10

Cited by 22 publications

(36 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similar to the most of other existing scanpath analysis algorithms (such as West et al [2006], Goldberg and Helfman [2010] and Eraslan et al [2014]), our algorithm conducts a scanpath analysis based on the areas of visual stimuli that we call them visual elements of web pages.…”

Section: Related Workmentioning

confidence: 99%

Eye tracking scanpath analysis on web pages

Eraslan

Yeşi̇lada

Harper

2016

Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research &Amp; Applications

Self Cite

View full text Add to dashboard Cite

The number of users required for usability studies has been a controversial issue over 30 years. Some researchers suggest a certain number of users to be included in these studies. However, they do not focus on eye tracking studies for analysing eye movement sequences of users (i.e., scanpaths) on web pages. We investigate the effects of the number of users on scanpath analysis with our algorithm that was designed for identifying the most commonly followed path by multiple users. Our experimental results suggest that it is possible to approximate the same results with a smaller number of users. The results also suggest that more users are required when they serendipitously browse on web pages in comparison with when they search for specific information or items. We observed that we could achieve 75% similarity to the results of 65 users with 27 users for searching tasks and 34 users for browsing tasks. This study guides researchers to determine the ideal number of users for analysing scanpaths on web pages based on their budget and time.

show abstract

Section: Related Workmentioning

confidence: 99%

Eye tracking scanpath analysis on web pages

Eraslan

Yeşi̇lada

Harper

2016

Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research &Amp; Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…The visualisation techniques are typically designed for analysing scanpaths in an exploratory and qualitative way [Räihä et al 2005;Blascheck et al 2014] whereas the algorithms are mainly designed for comparing a pair of scanpaths quantitatively, computing transition probabilities between web page elements, detecting patterns within scanpaths and identifying a general scanpath for multiple scanpaths [Eraslan et al 2016a;Holmqvist et al 2011]. Although some of these algorithms analyse these scanpaths as geometric figures [Jarodzka et al 2010;Mathôt et al 2012;Le Meur and Baccino 2013], most of them analyse scanpaths which are represented as a sequence of Areas of Interest (AoIs) [Sutcliffe and Namoun 2012;Le Meur and Baccino 2013;Eraslan et al 2014]. For example, if a user visits the Apple web page, shown in Figure 1, and fixates the AoI F, then the AoI C, then the AoI E and then the AoI B, the user's scanpath is represented as FCEB.…”

Section: Related Workmentioning

confidence: 99%

Scanpath Trend Analysis on Web Pages

2016

Self Cite

View full text Add to dashboard Cite

Eye tracking studies have widely been used in improving the design and usability of web pages, and in the research of understanding how users navigate them. However, there is limited research in clustering users' eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist which means the resulting path is so short that is not useful. Moreover, there is little work on correlating users' scanpaths with visual elements of web pages and the underlying source code which means the result cannot be used for further processing. In order to address these limitations, we introduce a new concept in clustering scanpaths called Scanpath Trend Analysis (STA) which does not only consider the visual elements visited by all users, but also considers the visual elements visited by the majority in any order. We present an algorithm which automatically does this trend analysis to identify a trending scanpath of multiple web users in terms of visual elements of a web page. In contrast to existing research, the STA algorithm first analyses mostly visited visual elements in given scanpaths, then clusters the scanpaths by using these visual elements based on their overall positions in the individual scanpaths, and then constructs a trending scanpath in terms of these visual elements. This algorithm was experimentally evaluated by an eye tracking study on six web pages for two different kinds of tasks, and therefore 12 cases in total. Our experimental results show that the STA algorithm generates a trending scanpath that addresses the reductionist problem of existing work by preventing the loss of commonly visited visual elements for all the cases. Based on the statistical tests, the STA algorithm also generates a trending scanpath which is significantly more similar to the inputted scanpaths compared to other existing work in 10 out of 12 cases. In the rest of the cases, the STA algorithm still performs significantly better than some of other existing work. This algorithm contributes to behaviour analysis research on the web which can be used for different purposes, for example re-engineering web pages guided by the trending scanpath to improve users' experience or guiding designers to improve their design.

show abstract

“…We then applied the STA and other existing algorithms (the eyePatterns Discover Patterns algorithm [25], the Dotplots based algorithm [13], the SPAM algorithm [14,12], and eMine algorithm [8,27]) to all of the individual scanpaths on the six web pages for the browsing and searching tasks. With the SPAM algorithm, multiple scanpaths were detected for some of the pages.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the Dotplots based hierarchical clustering, eMINE scanpath algorithm 4 was proposed which also uses a hierarchical clustering but with the String-edit and the Longest Common Subsequence (LCS) algorithms [8,7]. In this clustering, the String-edit algorithm is used to find the two most similar scanpaths from the list and then the LCS algorithm is used to find the common scanpath of the two similar scanpaths.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Trends in Eye Tracking Scanpaths

Eraslan

Yeşi̇lada

Harper

2016

Proceedings of the 27th ACM Conference on Hypertext and Social Media

Self Cite

View full text Add to dashboard Cite

Eye tracking has been widely used to investigate user interactions with the Web to improve user experience. In our previous work, we developed an algorithm called Scanpath Trend Analysis (STA) that analyses eye movement sequences (i.e., scanpaths) of multiple users on a web page and identifies their most commonly followed path in terms of the visual elements of the page. These visual elements are mainly the segments of a page generated by automated segmentation approaches. In our previous work, we also showed that the STA algorithm performs better than other existing algorithms in terms of providing the most representative scanpath of users. However, we did not know whether the validity of the algorithm is limited to a particular segmentation approach. In this paper, we investigate the effect of two different segmentation approaches on the STA algorithm. The results suggest that the validity of the algorithm is not affected by the segmentation approach used. Specifically, the resulting scanpath of the STA algorithm is the most representative scanpath of users in comparison with the resulting scanpaths of other existing algorithms regardless of the segmentation approach used.

show abstract

Identifying Patterns in Eyetracking Scanpaths in Terms of Visual Elements of Web Pages

Cited by 22 publications

References 18 publications

Eye tracking scanpath analysis on web pages

Eye tracking scanpath analysis on web pages

Scanpath Trend Analysis on Web Pages

Trends in Eye Tracking Scanpaths

Contact Info

Product

Resources

About