Abstract
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.
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de Sousa, H.F., de Vasconcelos, L.G., Baldochi, L.A. (2019). RUM++: A Log Mining Approach to Classify Users Based on Data Profile. In: Latifi, S. (eds) 16th International Conference on Information Technology-New Generations (ITNG 2019). Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-14070-0_34
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