A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities

A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities

Raghda Hraiz (PSUT, Amman, Jordan), Mariam Khader (SUT, Amman, Jordan) and Adnan Shaout (The University of Michigan, Dearborn, USA)
Copyright: © 2019 |Pages: 33
DOI: 10.4018/IJIIT.2019010103

Abstract

Assessing applicants for faculty positions in universities involves many issues. Each issue may involve a judgment based on uncertain or imprecise data. The uncertainty in data may exist in the interpretation made by the evaluator. This issue might lead to improper decision making. Modeling such a system using fuzzy logic will provide a more efficient model for handling imprecision. This article presents a fuzzy system for modeling the assessment of applicants for employment at academic universities. This system will utilize a multi-stage fuzzy model for measuring and evaluating the applicants. Utilizing fuzzy logic for applicants' evaluation will help administrators in choosing the best candidates for faculty positions. The fuzzy system was developed using jFuzzyLogic Java library. The reliability of the proposed system was proved by evaluating real-world case studies to prove its effectiveness to mimic human judgment. Moreover, the developed system has been evaluated by comparing it with a traditional mathematical method to prove the credibility and fairness of the proposed fuzzy system.
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1. Introduction

Improving performance evaluation is a critical issue for academic institutions. The goal is to achieve a fair evaluation in selecting a candidate for an academic position that will ensure having a qualified faculty member (Patel et al., 2014) (Guruprasad et al., 2015). Administrators need to implement effective assessment process with high reliable criteria to evaluate candidates for academic positions at universities (Sheykhi et al., 2012). Selecting the proper instructor has a great role in building a better educational system (Guruprasad et al., 2015) (Jyothi et al., 2014) (Trstenjak & Donko, 2013), because instructors have the best impact on student performance (Khan et al., 2011). However, common instructor measuring methods use conventional calculations and statistical techniques based on crisp values that might have judgment based on imprecise data (Trstenjak & Donko, 2013) (Ramli & Arbaiy, 2006). Implementing conventional methods may not guarantee the best way to evaluate human skills and performance (Ramli & Arbaiy, 2006), since it incorporates linguistic terms and imprecise data that may result in uncertain and vague results (Guruprasad et al., 2015).

Fuzzy logic has been selected to model many complex and vague systems it mimic the human way of thinking (Patel et al., 2014) since. Moreover, fuzzy systems have proven to provide reliable and consistent results (Ramli & Arbaiy, 2006). Fuzzy logic proved its efficiency in many applications, in (Sasirekha & Swamynathan, 2017) fuzzy logic was adopted to improve the accuracy of environment monitoring systems that are used in weather controlled laboratories. The fuzzy rough set was utilized in (Anitha & Acharjya, 2016) to make a system for customer's choice prediction.

Fuzzy logic allows for flexibility in building system components. It is easy to add or remove an input to a system without affecting other system components. A new output can be generated easily too. Various membership functions with different ranges could be applied and rules can easily be generated for an application (Ramli & Arbaiy, 2006). In the academic field, some applications have used fuzzy logic to measure students’ performance and evaluating faculty performance for promotion, assessment, etc. (Guruprasad et al., 2015) (Khan et al., 2011) (Shaout & Al-Shammari, 1998). Fuzzy logic has helped administrators in universities in the decision process of selecting instructors. It was used to measure their ability, skills and adequacy, which are uncertain and imprecision information that can be better captures by fuzzy modeling (Jyothi et al., 2014). However, fuzzy logic was not used in the decision of selecting new faculty positions in universities.

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