Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Discovering Visual Elements of Web Pages and Their Roles: Users' Perception
Date
2017-11-01
Author
Akpinar, M. Elgin
Yesilada, Yeliz
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
226
views
0
downloads
Cite This
Web pages typically include many visual elements such as header and footer to support interaction with the user. However, if web pages do not comply with web accessibility guidelines, and these visual elements are not explicitly encoded in the underlying source code, they become inaccessible in alternative presentations, such as audio. This article presents an automatic role detection approach to identify visual elements in web pages and their roles. The system architecture has three major components: automatic identification of visual elements in web pages; automatic generation of heuristic rules from the knowledge base; and application of these rules for automatic annotation of visual elements with their roles. This article first explains the system architecture in detail and then presents both technical and user evaluations of the proposed approach. Our user evaluation shows that the automatic role detection approach has around 70% receptive accuracy, but the proposed knowledge base could be further improved for better results. Our technical evaluation shows that the complexity is an important performance factor in role detection - required resources and execution time increases when the web page has more complex structure.
Subject Keywords
User studies
,
Web-based interaction paradigm
,
Empirical studies in HCI
,
User interface design
,
Accessibility
,
Web interfaces
URI
https://hdl.handle.net/11511/65973
Journal
INTERACTING WITH COMPUTERS
DOI
https://doi.org/10.1093/iwc/iwx015
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Automatic identification and role detection of visual elements in web pages
Akpınar, Mehmet Elgin; Karagöz, Pınar; Yeşilada, Yeliz; Department of Computer Engineering (2014)
Web pages typically include many visual elements to support interaction with the user. However, if visual elements do not comply with web accessibility guidelines, they become inaccessible in alternative presentations, such as audio. This study presents an automatic role detection approach to identify visual elements in web pages and their roles. The system architecture has three major components: automatic identification of visual elements in web pages; automatic generation of heuristic rules from the know...
Improving the prediction of page access by using semantically enhanced clustering
Şen, Erman; Toroslu, İsmail Hakkı; Karagöz, Pınar; Department of Computer Engineering (2014)
There are many parameters that may affect the navigation behaviour of web users. Prediction of the potential next page that may be visited by the web user is important, since this information can be used for prefetching or personalization of the page for that user. One of the successful methods for the determination of the next web page is to construct behaviour models of the users by clustering. The success of clustering is highly correlated with similarity measure that is used for calculating the similari...
Predicting Trending Elements on Web Pages Using Eye-Tracking Data
Shekh Khalil , Naziha; Yeşilada, Yeliz; Eraslan, Şükrü; Computer Engineering (2022-8-31)
Eye-tracking data can be used to understand how users interact with web pages and such understanding can be facilitated for different purposes, for example, it can be used to support better accessibility for disabled users or better usability for all users. However, understanding the sequential behavior of a group of users is challenging from eye-tracking data because individual eye movement sequences, i.e. scanpaths, tend to be complicated and different from each other. Previous work proposes an algorithm ...
Improving the prediction of page access by using semantically enhanced clustering
Sen, Erman; Toroslu, İsmail Hakkı; Karagöz, Pınar (2016-08-01)
There are many parameters that may affect the navigation behaviour of web users. Prediction of the potential next page that may be visited by the web user is important, since this information can be used for prefetching or personalization of the page for that user. One of the successful methods for the determination of the next web page is to construct behaviour models of the users by clustering. The success of clustering is highly correlated with the similarity measure that is used for calculating the simi...
Identifying Patterns in Eyetracking Scanpaths in Terms of Visual Elements of Web Pages
Eraslan, Sukru; Yesilada, Yeliz; Harper, Simon (2014-07-04)
Web pages are typically decorated with different kinds of visual elements that help sighted people complete their tasks. Unfortunately, this is not the case for people accessing web pages in constraint environments such as visually disabled or small screen device users. In our previous work, we show that tracking the eye movements of sighted users provide good understanding of how people use these visual elements. We also show that people's experience in constraint environments can be improved by reengineer...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. E. Akpinar and Y. Yesilada, “Discovering Visual Elements of Web Pages and Their Roles: Users’ Perception,”
INTERACTING WITH COMPUTERS
, pp. 845–867, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65973.