Abstract
Web accessibility evaluation checks the accessibility of the website to help improve the user experiences for disabled people. Due to the massive number of web pages in a website, manually reviewing all the pages becomes totally impractical. But the complexities of evaluating some checkpoints require certain human involvements. To address this issue, we develop the semi-supervised group sparse regression algorithm which takes advantages of the high precision of a small amount of manual evaluation results along with the global distribution of all the web pages and efficiently gives out the overall evaluation result of the website. Moreover, the proposed method can tell the importance of each feature in evaluating each checkpoint. The experiments on various websites demonstrate the superiority of our proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nietzio, A., Eibegger, M., Goodwin, M., Snaprud, M.: Following the WCAG 2.0 techniques: experiences from designing a WCAG 2.0 checking tool. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds.) ICCHP 2012. LNCS, vol. 7382, pp. 417–424. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31522-0_63
Klein, E., Bolfing, A., Riesch, M.: Checking web accessibility with the content accessibility checker (CAC). In: Miesenberger, K., Fels, D., Archambault, D., Peňáz, P., Zagler, W. (eds.) ICCHP 2014. LNCS, vol. 8547, pp. 109–112. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08596-8_16
Lazar, J., Beere, P., Greenidge, K.D., Nagappa, Y.: Web accessibility in the Mid-Atlantic United States: a study of 50 homepages. Univers. Access Inf. Soc. 2(4), 331–341 (2003)
Lazar, J., Greenidge, K.D.: One year older, but not necessarily wiser: an evaluation of homepage accessibility problems over time. Univers. Access Inf. Soc. 4(4), 285–291 (2006)
Hackett, S., Parmanto, B.: Homepage not enough when evaluating web site accessibility. Internet Res. 19(1), 78–87 (2009)
Harper, S., Moon, A.A., Vigo, M., Brajnik, G., Yesilada, Y.: DOM block clustering for enhanced sampling and evaluation. In: Proceedings of the 12th Web for All Conference, p. 15. ACM (2015)
Mucha, J., Snaprud, M., Nietzio, A.: Web page clustering for more efficient website accessibility evaluations. In: Miesenberger, K., Bühler, C., Penaz, P. (eds.) ICCHP 2016. LNCS, vol. 9758, pp. 259–266. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41264-1_35
Zhang, M., Wang, C., Bu, J., Yu, Z., Zhou, Y., Chen, C.: A sampling method based on URL clustering for fast web accessibility evaluation. Front. Inf. Technol. Electron. Eng. 16(6), 449–456 (2015)
Zhang, M., Wang, C., Yu, Z., Shen, C., Bu, J.: Active learning for web accessibility evaluation. In: Proceedings of the 14th Web for All Conference on the Future of Accessible Work, p. 16. ACM (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Wu, Y. et al. (2018). Using Semi-supervised Group Sparse Regression to Improve Web Accessibility Evaluation. In: Miesenberger, K., Kouroupetroglou, G. (eds) Computers Helping People with Special Needs. ICCHP 2018. Lecture Notes in Computer Science(), vol 10896. Springer, Cham. https://doi.org/10.1007/978-3-319-94277-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-94277-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94276-6
Online ISBN: 978-3-319-94277-3
eBook Packages: Computer ScienceComputer Science (R0)