Evaluating IBMEC-RJ’s Intranet Usability Using Fuzzy Logic

Evaluating IBMEC-RJ’s Intranet Usability Using Fuzzy Logic

Ana Beatriz Cavaleiro dos Reis Velloso (IBMEC Business School, Brazil), Walter Gassenferth (IBMEC Business School, Brazil) and Maria Augusta Soares Machado (IBMEC Business School, Brazil)
DOI: 10.4018/978-1-61350-168-9.ch010

Abstract

System usability is a concept that goes beyond the ease of use, and includes several criteria for measurement. This study aims to evaluate the usability and thus the quality of IBMEC-RJ’s Intranet in Rio de Janeiro, Brazil, by the fact that it is of great assistance to teachers and students. The method is applied research through questionnaires. The universe of users was limited by a convenience sample of IBMEC. The methodology that had used Microsoft Excel and Matlab from Mathworks is innovative. Fuzzy logic is a fundamental tool for consolidating and analyzing data.
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The Fuzzy Logic

The first notions of the Fuzzy Logic were developed by Jan Lukasiewicz (1878 – 1956) in 1920. Instead of using rigid rules and a line of logic thinking based upon premises and conclusions, Lukasiewicz attributes levels of pertinence {0, ½, 1} to classify vague and inaccurate concepts. A short time later, he expanded that set to all values contained in the interval [0,1]. Yet, the first publication on Fuzzy Logic dates back to 1965 by Lotfi Asker Zadeh, a professor at the University of Berkeley, California (Cezar, Machado and Oliveira Jr., 2006).

The power of the Fuzzy Logic stems from its ability to infer conclusions and generate replies based on vague, ambiguous and qualitatively incomplete and inaccurate information. With this regard, the fuzzy systems have the ability to ‘think’ in a very similar way to humans (OLIVEIRA JR., 1999).

The Fuzzy Sets and the Fuzzy Logic provide the basis to generate powerful problem-solving techniques with a wide applicability especially in the fields of decision-making and decision-control. The power of the Fuzzy Logic stems from its ability to infer conclusions and provide replies based on vague, ambiguous and qualitatively incomplete and inaccurate information. Regarding this matter, the fuzzy-based systems have the ability to think similarly to humans. Their behavior is represented simply and naturally, thus leading to building comprehensible and easy-to-maintain systems.

The fuzzy logic is based upon the theory of the Fuzzy Sets. This is a generalization of the Traditional Sets theory to solve the paradoxes generated from the “true or false” classification of the Classical Logic. Traditionally, a logical proportion has two extremes, namely: either “completely true” or “completely false”. Nevertheless, in the Fuzzy Logic, a premise ranges in the ‘true’ level from 0 to 1, causing it to be partially true or partially false. Upon the implementation of the “true level”, the Fuzzy sets theory expands the Traditional Sets theory. The groups are labeled qualitatively (by using such linguistic terms as: high, warm, active, small, near etc.) and the elements of these sets are characterized by varying the level of pertinence (a value that indicates the level at which an element belongs in a set). For example, temperatures between 30° (thirty degrees) and 40° (forty degrees) belong to the “high temperatures” set, although the 40° temperature has a higher level of pertinence in this set (Oliveira Jr. et al, 2007).

In a way that is not well understood, humans have the capability to associate a level of pertinence to a certain object without understanding consciously hot to reach it. For example, it would not be difficult for a student to assign a level to the teacher in the “good teachers” set. Such level is achieved immediately with no conscious analysis on the factors that influence such decision (Cezar, Machado and Oliveira Jr., 2006).

Key Terms in this Chapter

Effectiveness: Power to be effective; the quality of being able to bring about an effect.

Attractiveness: The ability of the software to attract the user, to be pleasant.

Operability: The ability of the software to enable the user to operate and control it.

Apprehensibility: The ability of the software to enable the user to learn how to handle it.

Usability: Means making products and systems easier to use, and matching them more closely to user needs and requirements.

Defuzzification: Is the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and corresponding membership degrees.

Intelligibility: The ability of the software to enable the user to identify whether the software is suitable for performing a given task.

Usability-Related Conformity: The ability of the software to be in accordance with usability-related standards and conventions.

Confidence Degree: Help to clarify how confident we are in a particular future and how based in reality a data is; has an inverse relation with failure rate.

Fuzzy Logic: Fuzzy logic includes 0 and 1 as extreme cases of truth (or “the state of matters” or “fact”) but also includes the various states of truth in between so that, for example, the result of a comparison between two things could be not “tall” or “short” but 0.38 of tallness.

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