Using Eye Tracking to Assess User Behavior in Virtual Training

2020 ◽  
pp. 341-347
Author(s):  
Mina Fahimipirehgalin ◽  
Frieder Loch ◽  
Birgit Vogel-Heuser
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Virtual Training

Impact of bookshelf locations using eye-tracking methodology

2020 ◽  
Vol 41 (8/9) ◽  
pp. 617-629
Author(s):  
Sho Sato ◽  
Yukari Eto ◽  
Kotomi Iwaki ◽  
Tadashi Oyanagi ◽  
Yu Yasuma
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Public Library ◽  
Gaze Behavior ◽  
Content Type ◽  
User Behaviors ◽  
Significant Relationships ◽  
Vertical Location ◽  
The Right ◽  
The Impact

PurposeThis study aimed to understand better the user gaze behavior on bookshelves using eye-tracking technology.Design/methodology/approachAn eye-tracking experiment in a public library with 11 participants was performed. The impact of vertical shelf location of books on the number of times the books are looked at, the impact of horizontal location and the relationship between user behavior and location impact were examined by the findings.FindingsThe results showed that the vertical location of books has a significant impact on the number of times the books are looked at. More than 80% of the time spent looking at bookshelves was spent on books on the top to fourth rows. It was also revealed that the horizontal location of books has a little impact. Books located on the left side of shelves will be looked at significantly more often than those on the right side. No significant relationships between type of user behaviors and location impact were observed.Originality/valueThe study explored the impact of the vertical location of books on time spent looking at bookshelves using eye-tracking methodology. Few published studies do such experiments to address user gaze behavior on bookshelves. The study explored that the vertical location of books has a great impact, and horizontal location has a little impact on user gaze behavior.

scholarly journals Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1381 ◽  
Author(s):  
Piotr Sulikowski ◽  
Tomasz Zdziebko ◽  
Kristof Coussement ◽  
Krzysztof Dyczkowski ◽  
Krzysztof Kluza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Eye Tracking ◽  
User Behavior ◽  
Implicit Feedback ◽  
Physical Parameters ◽  
Object Model ◽  
Machine Learning Methods ◽  
Event Tracking ◽  
Item Presentation

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.

Eye-tracking analysis of user behavior and performance in web search on large and small screens

2014 ◽  
Vol 66 (3) ◽  
pp. 526-544 ◽  
Author(s):  
Jaewon Kim ◽  
Paul Thomas ◽  
Ramesh Sankaranarayana ◽  
Tom Gedeon ◽  
Hwan-Jin Yoon
Keyword(s):  
Eye Tracking ◽  
Web Search ◽  
User Behavior ◽  
And Performance

User Behavior Analytics in Virtual Training Environments for Sensory Substitution Devices

2018 ◽  
Author(s):  
Daniel Flamaropol ◽  
Alin Moldoveanu ◽  
Florica Moldoveanu ◽  
Maria-Iuliana Dascalu ◽  
Iulia Stanica ◽  
...  
Keyword(s):  
User Behavior ◽  
Sensory Substitution ◽  
Virtual Training

scholarly journals Using eye-tracking to evaluate the viewing behavior on tourist landscapes

2021 ◽  
pp. 141-146
Author(s):  
Gianpaolo Zammarchi ◽  
Giulia Contu ◽  
Luca Frigau
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Visual Stimuli ◽  
Decision Makers ◽  
Eye Position ◽  
Tracking Data ◽  
Natural Landscapes ◽  
Different Types ◽  
Tourism Websites ◽  
Tracking Technology

Every tourist website employs images to attract potential tourists. In particular, destination tourism websites use environmental images, such as landscapes, to attract the attention of tourists and to address their purchase choice. Nowadays the effectiveness of these tools has been enhanced by the use of eye-tracking technology. That allows measuring the exact eye position during the visualization of images, texts, or other visual stimuli. Consequently, eye-tracking data can be processed to obtain quantitative measures of viewing behavior that can be analyzed for several purposes in many fields such as to cluster consumers, to improve the effectiveness of a website and for neuroscience studies. This work is aimed to use eye-tracking technology to investigate user behavior according to different types of images (e.g. natural landscapes, city landscapes). Specifically, we compare different statistical descriptive tools with supervised and unsupervised models. Furthermore, we discuss the effectiveness of their results and their capacity to provide satisfactory and interpretable solutions that can be used by decision-makers.

Eye Tracking Analysis of User Behavior in Online Social Networks

2013 ◽  
pp. 113-119 ◽  
Author(s):  
Wan Adilah Wan Adnan ◽  
Wan Nur Hafizhoh Hassan ◽  
Natrah Abdullah ◽  
Jamaliah Taslim
Keyword(s):  
Social Networks ◽  
Eye Tracking ◽  
Online Social Networks ◽  
User Behavior
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