Eye Tracking in Web Usability Studies

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Table of Contents

Introduction
Eye Tracking Measures
Measurements of Attraction
Reference List

Introduction

Web usability studies aim to represent how individuals can use websites and determine their effectiveness. In this case, eye tracking is often used as an appropriate research tool to support the usability testing in studies (Lew 2009). The focus on eye tracking is significant to understand the nature of users’ reactions in such forms and dimensions as behaviour, attitude, and attention, as it is noted by Lew (2009):

Behaviour. The researcher’s emphasis is on users’ eye movements and actions.
Attitude. The investigator’s emphasis is on users’ words or opinions regarding the website.
Attention. The researcher’s emphasis is on actual aspects of the site that are interesting for a user because he or she focuses on them.

According to Lew (2009), eye tracking is the most effective tool to study all three mentioned aspects, with the particular focus upon the user’s attention important for investigating by researchers. However, Nielsen and Pernice (2009) state that eye tracking for determining and measuring usability of websites is often an expensive procedure, and researchers try to conduct studies using cheaper means.

Nevertheless, only eye tracking can be most appropriate to analyse all usability problems and errors identified during tests because of focusing on users’ actual attention (Lew 2009).

Eye Tracking Measures

Researchers concentrate on two main types of eye tracking measures such as (1) measurements of attraction, and (2) measurements of performance. For the purpose of this research, measurements of attraction need to be discussed in detail.

Measurements of Attraction

The data received from using eye tracking is in a form of a heat map that is necessary to understand aspects of users’ fixations on particular features of the web page. Still, to provide effective study results, this heat map should be analysed with the focus on concrete quantitative measures to allow researchers to use study findings actively (Bojko 2012; Bojko 2013; Duchowski 2007).

As a result, Bojko (2012) proposes to refer to such measurements of attraction as Noticeability and Interest and use associated metrics to receive the clear picture.

Noticeability Area Measures

Noticeability means that particular elements of the website can be quickly noticed by the user because of their size, design, and placement (Nielsen & Pernice 2009; Turigas 2012). The elements should have a persuading effect on a customer, and noticeability is measured according to specific metrics:

The number of users fixated on an area of interest (AOI). A researcher determines fixation criteria and monitors fixations of the sample participants on the AOI.
The number of users’ fixations before the fixation on an area of interest (AOI). A researcher calculates the number of users’ fixations on the website areas before fixating on the AOI (Bojko 2012). In this case, the number of users making fixations should also be mentioned along with the number of actual fixations.
The time before the first fixation on an area of interest (AOI). A researcher determines how quickly a user can fix on the AOI in connection to the number of prior fixations.

Interest Area Measures

Having noticed the target information, a user is expected to become interested in it because of its relation to customers’ needs. Thus, noticeability of the information should be supported with the customers’ interest and attention. The user’s interest is measured with references to following metrics:

The number of fixations on an area of interest (AOI). A researcher calculates the number of fixations on an area of interest to understand the level of the customers’ interest.
Total dwell time fixed on an area of interest (AOI). It is important to calculate the total time spent on the website since entering it to exiting with the focus on the number of fixations and their duration. The comparison of data can also be performed based on average fixation durations (Bojko 2012). Calculating the total dwell time related to an AOI, it is also necessary to exclude the time spent for blinks and saccades.
Percentage of the time spent on an area of interest (AOI). In most cases, a researcher needs to study the actual time spent on an area of interest. As a result, the data related to the different time spent by participants on the website can be discussed as inaccurate. In this case, a researcher should calculate the percentage of time spent on the AOI as an effective measure (Bojko 2013; Duchowski 2007). This percentage depends on the difference between the total dwell time spent on the AOI and the total dwell time spent on observing the website.

In order to receive the most accurate results, a researcher should determine clearly what measures of interest are meaningful to be calculated and how to measure the results typical for website users who focus on the AOI and for those who do not focus on the AOI. From this point, it is significant to find the average numbers and to refer to the percentage of users who pay attention to the AOI as to the measures indicating the level of the customers’ interest.

Reference List

Bojko, A 2012, 100 Eye tracking measures and counting, UX Research, Barcelona.

Bojko, A 2013, Eye tracking the user experience: a practical guide to research, Rosenfeld Media, New York.

Duchowski, AT 2007, Eye tracking methodology: theory and practice, Springer, New York.

Lew, G 2009, The role of eye tracking in user experience research, HFES Webinar Series Publishing, San Francisco.

Nielsen, J & Pernice, K 2009, Eyetracking web usability, New Riders Press, San Francisco.

Turigas, RT 2012, Evaluation of advertisement effectiveness with user interaction, UX Research, Barcelona.

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