Usability is generally considered as a metric to judge the efficacy of any interface. This is also true for the web pages of a website. There are different factors - efficiency, memorability, learnability, errors, and aesthetics play significant roles in order to determine usability. In this work, we proposed a computational model to predict the efficiency with which users can do a particular task on a website. We considered seventeen features of web pages that may affect the efficiency of a task. The statistical significance of these features was tested based on the empirical data collected using twenty websites. For each website, a representative task was identified. Twenty participants completed these tasks using a controlled environment within a group. Task completion times were recorded for feature identification. The one Dimensional ANOVA study reveals sixteen out of the seventeen are statistically significant for efficiency measurement. Using these features, a computational model was developed based on the Support Vector Regression. Experimental results show that our model can predict the efficiency of web pages' tasks with an accuracy of 90.64%.
Keywords: Analysis of Variance (ANOVA); Computational model; Efficiency modeling; Empirical study; Machine learning; Support Vector Machine (SVM); Support vector regression; Usability evaluation.
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