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Clustering Analysis of Usability in Web Sites of Higher Technological Institutes of Ecuador

Abstract:

Techniques for evaluating usability continue to be innovated. This document shares the application of a heuristic-based framework for measuring web usability - SIRIUS, complemented by two machine learning techniques for clustering: a) Hierarchical, with the Ward.2 method and Euclidean; and b) K-means clustering. For data processing, CRISP-DM has been proposed as a general method. Since our objective is to evaluate the usability characteristics of the websites of the Technical and Technological Institutes of Ecuador, data has been obtained from the web portals of 83 Institutes (34 public and 49 private). As a result, three clusters have been obtained, which encompass the 10 aspects of the framework, and which allow us to identify the levels of usability of technological institutes. As a result, 18 institutes have been categorized into the group of websites with above-average usability (cluster1), 32 institutes with below-average usability (cluster2), and the remainder with an acceptable degree of usability. The method used and proposed has made it possible to have a general usability map of the web portals of the technical and technological institutes of a country, as input for decision-making.