Evaluation of student performance in laboratory applications using fuzzy logic

Gökmen, Gökhan; Akıncı, Tahir Çetin; Tektaş, Mehmet; Onat, Nevzat; Koçyiğit, Gökhan; Tektaş, Necla

doi:10.1016/j.sbspro.2010.03.124

Cited by 78 publications

(72 citation statements)

References 2 publications

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“…et al, 2002). This difficult process is basically involved with incomplete and imprecise information subjectivity, and linguistics factors, which tend to be present to some degree (Gokmen et al, 2010). As knowledge involved in employee appraisal evaluation is approximate, and fuzzy logic theory is successfully used for approximate reasoning in such circumstances, its application becomes significant to manage the uncertainty in the evaluation system.…”

Section: Methodsmentioning

confidence: 99%

Applying fuzzy logic theory to performance management

Ardil¹

2017

Pressacademia

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Organizations usually apply classical methods of employee performance evaluation. In this classical system, employee performance depends on work results, and it is evaluated only as success or failure in job behaviors. The non-classical performance evaluation methods such as fuzzy logic may mainly be used to many forms of decision-making including artificial intelligence systems. This research proposes a new employee performance evaluation method based on fuzzy logic systems. The process of performance measurement for evaluating the effectiveness, efficiency, and productivity of employees encompasses data collection, data design, and data analysis stages and it involves a level of uncertainty associated with performance measures. In evaluating employee performance, it usually involves granting numerical values or linguistic labels to employee performance in the organization. The scores accorded by the appraisers are only approximations, which are then, used to represent each employee's achievement by reasoning incorporated in the computational methods. In this paper, the fuzzy logic theory approach is used to represent the uncertainty caused by performance measures during its design, use and analysis stages. This research seeks to describe and execute the fuzzy logic theory approach for decision making in the employee performance appraisal process. Finally, reasoning based on fuzzy logic theory provides an alternative way in dealing with imprecise data, which is often reflected in the way humans think and make judgments in real life.

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Section: Methodsmentioning

confidence: 99%

Applying fuzzy logic theory to performance management

Ardil¹

2017

Pressacademia

View full text Add to dashboard Cite

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“…According to Gökmen et al (2010), Hoban (2002), and John and Gravani (2005), this increases the need for the development of frameworks for looking at the PL, which links different learning perspectives.…”

Section: Fuzzy Logic Methodsmentioning

confidence: 99%

Evaluating the attitudes of prospective elementary school mathematics teachers towards postgraduate education in turkey and other components with fuzzy logic

Bahadir

2017

HIG

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Postgraduate education, which is regarded as necessary in the field of education in order to improve the qualifications of teachers seen as culture workers, has become more important than it used to be. In this context, the attitudes of prospective teachers toward pursuing a postgraduate education are crucial. In this study, all of the components, including grades, scores and the attitudes of prospective teachers were interpreted by a fuzzy logic-based evaluation for admission to postgraduate education. The attitudes of the underpostgraduate students towards postgraduate education were determined according to 'the Postgraduate Attitude Scale' . The students' four year college grade point averages and 'academic personnel and postgraduate education entrance exam' scores were also taken into account, and their eligibility for postgraduate education was assessed by using fuzzy logic rules. The study was conducted with 155 prospective teachers who were underpostgraduate students in the program of mathematics teaching for elementary school. During the study, after the test operation was done with one-third of the data, an 80.77% result of the general prediction rate was obtained. These results were further analyzed in detail, and 76 out of 96 students who were eligible for postgraduate education and 48 out of the 59 remaining students were predicted correctly. The data obtained from the study was assessed using fuzzy logic methods, and this approach has proven to reach more effective and accurate results. Keywords: Fuzzy logic, Postgraduate education, Attitude ÖzBir kültür çalışanı olarak değerlendirilen öğretmenlerin niteliklerini arttırmak için günümüzde diğer alanlarda olduğu gibi eğitim alanında da gereklilik gibi görülen lisansüstü eğitim, geçmiş dönemlere nazaran daha önemli hale gelmiştir. Öğretmen adayların lisansüstü eğitime dair tutumları bu bağlamda oldukça önemlidir. Lisansüstü eğitime girişte lisans mezuniyet ortalamaları ve 'akademik personel ve lisansüstü eğitimi giriş sınavı' puanları ağırlıklı olarak dikkate alınmaktadır. Araştırmamızda bu verilerin yanında öğretmen adaylarının tutumlarını da dikkate alarak, lisansüstü eğitime girişte bütün bu bileşenlerin bulanık mantık tabanlı bir değerlendirme ile yorumlanmasına yer verilmektedir. Bu çalışmada, öğrencilerin lisansüstü eğitime yönelik tutumlarının belirlenmesinde 'Lisansüstü Tutum Ölçeği' ile elde edilmiş sonuçlar yanı sıra öğretmen adaylarının lisans mezuniyet ortalamaları ve ' Akademik Personel ve Lisansüstü Eğitimi Giriş Sınavı' puanları da dikkate alınarak lisansüstü eğitime uygun öğrencilerin değerlendirmesi, bulanık mantık kurallarıyla gerçekleştirilmiştir. Çalışma 155 ilköğretim matematik öğretmenliği öğrencisi ile yürütülmüştür. %80.77'lik genel öngörü oranı ile sınıflama yapılmıştır. Öğretmen adaylarının tutum puanları ve eldeki diğer verilerin değerlendirmesi bulanık mantık yöntemi ile yapılmış ve bu yaklaşımla daha etkin ve doğru sonuçlara ulaşılabileceği gösterilmiştir.

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“…The extended version of Biswas evaluation method is carried out by Chen and his colleagues [9]. Gokmen et al reported the evaluation of student performance in laboratory applications using fuzzy logic [10].…”

Section: Introductionmentioning

confidence: 99%