Statistical Grouping Methods for Identifying User Profiles

Statistical Grouping Methods for Identifying User Profiles

Francisco Kelsen de Oliveira, Max Brandão de Oliveira, Alex Sandro Gomes, Leandro Marques Queiros
Copyright: © 2019 |Pages: 12
DOI: 10.4018/IJTHI.2019040104
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Abstract

This article contains data from a group of users, divided into subgroups according to their levels of knowledge about technology. Statistical hierarchical and non-hierarchical clustering methods were studied, compared and used in the creations of the subgroups from the similarities of the skill levels with these users' technology. The research sample consists of teachers who answered online questionnaires about their skills in the use of software and hardware with an educational bias. The statistical methods of the grouping were performed and showed the possibilities of groupings of the users. The analysis of these groups allowed the identification of the common characteristics among the individuals of each subgroup. Therefore, it was possible to define two subgroups of users, one with skills in technology and another without skills in technology. The partial results of the research showed two main algorithms for grouping with 92% similarity from groups of users with skills in technology and the other with little skill, confirming the accuracy of the techniques discriminating against individuals.
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The Users Profile

An important step in the development of any product is to meet the wishes and the needs of the users. This also occurs with the information and communication technology (ICT), so the area of human-computer interaction (HCI) is dedicated to the study and development of more efficient methods and techniques on capturing such data of these users.

The user profile is the individualized description of the characteristics of users (Barbosa & Silva, 2010). (Baxter, Courage & Caine, 2015) in line with the concept presented and still ensures that the purpose of raising the profile of the user represents really know better for anyone who is developing the product and who will choose to research, validation, satisfaction and other.

(Rogers, Sharp & Preece, 2013) report that the characteristics of the users should cover the main attributes of the intended user group, highlight the relevant skills and abilities of the user, and even cites some attributes to be considered in the survey of profile: nationality, education, preferences, personal circumstances, physical or mental handicaps and other.

The researches of (Courage & Baxter, 2005), Hackos et al. (1998) and Peffer et al. (2015) describe some types of data for better clarification of the user profile and to be collected for better definition of the domain of the product and the user interface with the technology: demographics, experience in the position he holds, company information, degree of education, experience with computers, experience with specific product or similar tools, available technology, training, attitudes and values, domain knowledge, goals, tasks, severity of errors, motivation to work, languages and jargon.

Highlights the importance of identifying the level of user experience on that if you want to investigate (beginner, expert, casual user or frequent user) as it affects especially the forms of interactions to be designed. This shows the importance of the definition of subgroups of the sample from statistical analysis of the data provided by users in order to truly implement products and their validation mechanisms in accordance with the intended target audience (Rogers et al., 2013).

The researches Courage et al. (2005) and Hackos et al. (1998) confirm such a point of view by saying that the user profile helps meet to whom the product is being built, as well as collaborate in choosing participants for future activities of analysis and product evaluation.

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