Evaluation of Students Performance using Fuzzy Set Theory in Online Learning of Islamic Finance Course

<p class="0abstract"><strong>Abstract—</strong>The objective of this study is to evaluate student performance using fuzzy set theory in Islamic Finance online course. This study focuses on selecting best individual among 30 students that registered for Islamic Bank Management course. The variables that involved in this study are online quiz marks, online assignment marks and online self-learning time. The outcome of the fuzzy set analysis was compared with final examination data. The methodology of this study involving converting real data to fuzzy set, intersection calculation, decision analysis using maximizing approach. Result of fuzzy set shows the best individual score is 0.9. This student selected as best candidate for student performance in online learning with considering three variables namely online quizzes, online assignment and online self-learning hour. The comparison with final examination marks shows a good agreement with fuzzy set theory that concluded best individual from fuzzy set theory exhibits highest performance during final examination. The main finding of this study can help educators to predict the best performer in online learning class. In the same time, finding of this study can act as guideline to advise students in achieving their desired grade for online learning course.</p>

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A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488507004650 ◽

2007 ◽

Vol 15 (supp02) ◽

pp. 117-138 ◽

Cited By ~ 11

Author(s):

JIAN ZHOU ◽

CHIH-CHENG HUNG

Keyword(s):

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Clustering Algorithms ◽

Infinite Family ◽

Real Data ◽

Point Of View ◽

New Family ◽

Possibilistic Clustering

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.

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Fuzzy set theory-based model for identifying the potential of improving process KPIs in production logistics area

New Trends and Issues Proceedings on Humanities and Social Sciences ◽

10.18844/prosoc.v4i10.3098 ◽

2018 ◽

Vol 4 (10) ◽

pp. 327-336

Author(s):

Denisa Hrusecka

Keyword(s):

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Performance Indicators ◽

Business Processes ◽

Fuzzy Model ◽

Real Data ◽

Key Performance Indicators ◽

Production Logistics ◽

Low Performance

The high complexity of today’s manufacturing environment brings many problems with planning and managing, especially production, logistic and other key business processes. In many cases, it is quite complicating to identify the real causes of problems that enterprises face or to decide which one of them should be solved first. Especially, in the case of large enterprises, it is complicating to access expertise among all departments and employed professionals in order to solve the problems most efficiently. Our fuzzy model provides a simple tool for easy identification of the most significant problems of observed processes that cause their low performance according to the measured values of their key performance indicators. The model is based on data gained through interviews with production managers, industry experts and other professionals, and verified by real data from a model company. The results are presented in the form of case studies in this contribution. Keywords: Production logistics, key performance indicators, KPI, productivity, problem identification, fuzzy set theory, process.

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Fuzzy set theory–based model for identifying the potential for improving process KPI in production logistics area

New Trends and Issues Proceedings on Humanities and Social Sciences ◽

10.18844/prosoc.v4i10.3086 ◽

2018 ◽

Vol 4 (10) ◽

pp. 154-163

Author(s):

Denisa Hrusecka

Keyword(s):

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Performance Indicators ◽

Business Processes ◽

Fuzzy Model ◽

Real Data ◽

Key Performance Indicators ◽

Production Logistics ◽

Low Performance

The high complexity of today´s manufacturing environment brings many problems with planning and managing, especially production, logistic and other key business processes. In many cases, it is quite complicated to identify the real causes of problems that enterprise faces or to decide which one of them should be solved as a first. Especially, in the case of large enterprises, it is quite complicated to access expertise among all departments and employed professionals in order to solve the problems in the most efficient way. The purpose of our fuzzy model is to provide a simple tool for easy identification of the most significant problems of observed processes that causes their low performance according to the measured values of their key performance indicators (KPIs). The model is based on data gained through the interviews with production managers, industry experts and other professionals, and it was verified by real data from one model company. The results are presented in the form of case study in this contribution. Keywords: Production logistics, key performance indicators (KPI), productivity, problem identification, fuzzy set theory, process.

Download Full-text