RUM++: A Log Mining Approach to Classify Users Based on Data Profile

Today the Web is pervasive in everyday life. Daily activities such as shopping and banking are now available from almost everywhere, which makes modern life more convenient. However, not everyone may benefit from this convenience. Low web literacy still prevents many users to take full advantage from online services. A group that usually presents issues related to web access is the elderly. As people grow older, motor control, visual acuity and cognition decreases, which makes aging users struggle to perform tasks in web applications. Therefore, it is important to detect struggling web users in order to support them, for instance, by providing friendly user interfaces. In order to tackle this problem, we propose an approach that is able to identify usage patterns commonly found among the elderly. Our approach allows the identification of struggling users while they browse web applications. Thus, by using our approach, developers may code adaptations to support these users. An experiment performed with real data from an educational web site shows that our approach is effective to identify struggling users in web applications.

[1]  Andrew Sears,et al.  Introduction ASSETS’10 Special Issue , 2011, TACC.

[2]  Grace Ebong Mbipom,et al.  The interplay between web aesthetics and accessibility , 2011, ASSETS.

[3]  Barbara S. Chaparro,et al.  A Comparison of Website Usage between Young Adults and the Elderly , 2000 .

[4]  Vicki L. Hanson,et al.  Age, technology usage, and cognitive characteristics in relation to perceived disorientation and reported website ease of use , 2014, ASSETS.

[5]  Jon Froehlich,et al.  Age-related differences in performance with touchscreens compared to traditional mouse input , 2013, CHI.

[6]  Leandro Guarino de Vasconcelos,et al.  Towards an automatic evaluation of web applications , 2012, SAC '12.

[7]  Faustina Hwang,et al.  Effects of Target Expansion on Selection Performance in Older Computer Users , 2013, ACM Trans. Access. Comput..

[8]  Neil Charness,et al.  TRAINING OLDER AND YOUNGER ADULTS TO USE SOFTWARE , 1989 .

[9]  Peter Graf,et al.  Multi-Layered Interfaces to Improve Older Adults’ Initial Learnability of Mobile Applications , 2010, TACC.

[10]  Peter G. Fairweather.  How older and younger adults differ in their approach to problem solving on a complex website , 2008, Assets '08.

[11]  Renata Pontin de Mattos Fortes,et al.  A study on the acceptance of website interaction aids by older adults , 2015, Universal Access in the Information Society.

[12]  Sara J. Czaja,et al.  Age related differences in learning to use a text-editing system , 1989 .

[13]  Ian H. Witten,et al.  Chapter 15 – Embedded Machine Learning , 2011 .

[14]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[15]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[16]  Sang D. Choi,et al.  The impact of age on computer input device use:: Psychophysical and physiological measures , 1999 .

[17]  José Antonio Pow-Sang,et al.  Evaluation of Usability Heuristics for Transactional Web Sites: A Comparative Study , 2016 .

[18]  Luís Magalhães,et al.  Different interaction paradigms for different user groups: an evaluation regarding content selection , 2014, Interacción '14.

[19]  Eurico Carrapatoso,et al.  Age group differences in performance using diverse input modalities: insertion task evaluation , 2016, Interacción.

[20]  Arthur D. Fisk,et al.  Understanding age and technology experience differences in use of prior knowledge for everyday technology interactions , 2012, TACC.

[21]  Lee Priest,et al.  Website task performance by older adults , 2007, Behav. Inf. Technol..