Web Browsing Behavior Analysis and Interactive Hypervideo

© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in, ACM Transactions on the Web, Vol. 7, No. 4, Article 20, Publication date: October 2013.http://doi.acm.org/ 10.1145/2529995.2529996[EN] Processing data on any sort of user interaction is well known to be cumbersome and mostly time consuming. In order to assist researchers in easily inspecting fine-grained browsing data, current tools usually display user interactions as mouse cursor tracks, a video-like visualization scheme. However, to date, traditional online video inspection has not explored the full capabilities of hypermedia and interactive techniques. In response to this need, we have developed SMT 2ǫ, a Web-based tracking system for analyzing browsing behavior using feature-rich hypervideo visualizations. We compare our system to related work in academia and the industry, showing that ours features unprecedented visualization capabilities. We also show that SMT 2ǫ efficiently captures browsing data and is perceived by users to be both helpful and usable. A series of prediction experiments illustrate that raw cursor data are accessible and can be easily handled, providing evidence that the data can be used to construct and verify research hypotheses. Considering its limitations, it is our hope that SMT 2ǫ will assist researchers, usability practitioners, and other professionals interested in understanding how users browse the Web.This work was partially supported by the MIPRCV Consolider Ingenio 2010 program (CSD2007-00018) and the TIN2009-14103-C03-03 project. It is also supported by the 7th Framework Program of the European Commision (FP7/2007-13) under grant agreement No. 287576 (CasMaCat).Leiva Torres, LA.; Vivó Hernando, RA. (2013). Web Browsing Behavior Analysis and Interactive Hypervideo. ACM Transactions on the Web. 7(4):20:1-20:28. https://doi.org/10.1145/2529995.2529996S20:120:287

Visualization of eye gaze data using heat maps

Usability testing is widely used today to determine, among other things, the quality of web site designs. To help the researchers, a number of techniques have been suggested for visualizing the eye tracker’s data. Using one of the most popular techniques, gaze fixations are plotted in 2-D against the stimulus image in the background. However, there is an alternative visualization technique, based on the heat map paradigm, which offers additional benefits by better separating the different levels of observation intensity. We present a modified version of this technique to facilitate visualizations by allowing the transparency of the heat map to depend on the gaze data itself. In our version, transparency is presented in either the gray scale, or employing some color scheme. The intensity is proportional to the duration of the observation. Thus, longer fixations add more transparency than shorter ones. Conversely, the least observed areas are hidden by a shadow or fog. We also propose three alternative forms for the function of the transparency distribution. One of these is a simple linear relationship, whereas the other two are nonlinear (a sum of linear and sine wave, and a Gaussian)

Highly flexible software for gaze path analysis and visualization.

The growing interest in using eye-movements for human-computer interaction has also increased the need for tools to investigate and analyze the behavior of human eyes. Nowadays, there are many commercial and academic products available for researchers in this field and the quality and accuracy of eye-tracking devices are constantly increasing. Despite the numerous methods developed for analyzing and visualizing gaze paths, however, no universal tools are available yet to accomplish this. To fill in this gap, we developed iComponent – a software product with a highly flexible architecture for easy development of interchangeable plug-in modules to support various eye-tracking devices and experimental software. iComponent has four types of 2D visualizations of gaze data and one type of 1D visualization. The 2D visualizations are superimposed on the stimulus seen by the user during a session. Three of the visualizations are capable of showing several subjects’ gaze paths simultaneously