Web Mining for navigation problem detection and diagnosis in Discapnet: a website aimed at disabled people

The dramatic increase in the amount of information stored on the web makes it more important to familiarize people with disabilities and elderly people with digital devices and applications and to adapt websites to enable their use by these users. Discapnet is a website mainly aimed at visually disa...

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Bibliographic Details
Authors: Arbelaiz Gallego, Olatz, Lojo Novo, Aizea;, Muguerza Rivero, Javier Francisco, Perona Balda, Iñigo
Format: article
Publication Date:2015
Country:España
Institution:Universidad del País Vasco
Repository:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/75020
Online Access:http://hdl.handle.net/10810/75020
Access Level:Open access
Keyword:Web accessibility
Web usability
Disabled users
Navigation problems
User experience
Web interaction
User modeling
Server logs
Web-usage mining
Clustering
Discapnet
Description
Summary:The dramatic increase in the amount of information stored on the web makes it more important to familiarize people with disabilities and elderly people with digital devices and applications and to adapt websites to enable their use by these users. Discapnet is a website mainly aimed at visually disabled people, and navigation is a challenging task for its users. In this context, system evaluation and problem detection become crucial aspects for enhancing user experience and may contribute greatly to diminishing the existing technological gap. This study proposes a system based on web-mining techniques that collects in-use information while the user is accessing the web (thus, being a noninvasive system). The proposed system models users in the wild and discovers navigation problems appearing in Discapnet and can also be used for problem detection when new users are navigating the site. The system was tested and its efficiency demonstrated in an experiment involving navigation under supervision, in which 82.6% of a set of disabled people were automatically labeled as having problems with the website.