EVALUATION OF PROSPECTIVE MATH TEACHERS’ ABILITY TO ENTER GRADUATE EDUCATION WITH FUZZY LOGIC ALONG WITH VARIOUS COMPONENTS

It seems that getting graduate education has become more important compared to the past. This is the case for teachers and prospective teachers. In order to be admitted for graduate education in Turkey, one must have ALES (Academic personnel and graduate education entrance exam), College GPA (graduation grade point average) and a foreign language score. The road to success is a difficult process for many students to complete when represented by classification of traditional graduation grade point average. Development approaches of student achievement need to have a framework consisting of more in number and a complex success criteria in order to be more effective. Apart from the aforementioned grade data that was mainly determined in the classification that classify whether prospective teachers were suitable for graduate education or not, some other components such as; their emotional data, their level of knowledge on graduate education and how much priority they give to teaching department while doing their university preferences have also become important. The study was shaped in this context and by assessing various components related to the students with fuzzy logic, a more effective prediction and classification was tried to be presented. In the study, considering attitudes of prospective teachers towards graduate education, their genders, their levels of knowledge on graduate education, their university entrance scores, their order of preference, and their levels in undergraduate education, their suitabilities of admission to graduate education was aimed to be determined by fuzzy logic. In our study in which relationships of all above mentioned components with each other were analyzed, survey (scanning) method, of quantitative research methods, was used and the relational scanning model was preferred. In the study, the information of 390 prospective teachers who were studying at the department of primary school mathematics teaching in three different state universities and attending at formation programs but graduated from faculty of arts and sciences mathematics teaching department was used. MATLAB software was used for fuzzy logic analysis. In the research, a fuzzy logic rule base was created and 98 (25.1%) of the analyzed data were decided to be suitable for graduate education program. 29 (7.4%) of these prospective teachers were from the first year, 48 (12.3%) of them were from the fourth year, and 21 (5.3%) of them were from the formation group. The group with the highest percentage of prospective teachers considered to be suitable for graduate education is fourth year undergraduate students with 12.3%. The group with the lowest percentage is formation students with 5.3%. As a result of the analyses conducted by fuzzy logic providing a valid prediction and classification, the reason of fourth year prospective teachers have the highest percentage in the research can be explained as their having higher attitude scores and being more knowledge about graduate education and having higher scores on the university entrance exams than the other participants. In order to ensure prospective teachers to have a higher attitude towards the graduate education, their gaining awareness of research and being informed about graduate education from the first years of college can provide significant benefits. Prospective teachers in different departments may be included in the study. Considering different components related to the prospective teachers and conducting researches using other methods of artificial intelligence such as fuzzy logic, students and educators can be provided an effective prediction and classification opportunities.  Article visualizations

A computational intelligence optimization algorithm based on the behavior of the social-spider

Classical optimization methods often face great difficulties while dealing with several engineering applications. Under such conditions, the use of computational intelligence approaches has been recently extended to address challenging real-world optimization problems. On the other hand, the interesting and exotic collective behavior of social insects have fascinated and attracted researchers for many years. The collaborative swarming behavior observed in these groups provides survival advantages, where insect aggregations of relatively simple and “unintelligent” individuals can accomplish very complex tasks using only limited local information and simple rules of behavior. Swarm intelligence, as a computational intelligence paradigm, models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this chapter, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions. © 2015 Springer International Publishing Switzerlan

A computational intelligence optimization algorithm based on the behavior of the social-spider

We develop a comprehensive theory of phase for finite-dimensional quantum systems. The only physical requirement we impose is that phase is complementary to amplitude. To implement this complementarity we use the notion of mutually unbiased bases, which exist for dimensions that are powers of a prime. For a d-dimensional system (qudit) we explicitly construct d+1 classes of maximally commuting operators, each one consisting of d-1 operators. One of these classes consists of diagonal operators that represent amplitudes (or inversions). By finite Fourier transformation, it is mapped onto ladder operators that can be appropriately interpreted as phase variables. We discuss examples of qubits and qutrits, and show how these results generalize previous approaches. " 2005 IOP Publishing Ltd.",,,,,,"10.1088/1464-4266/7/9/008",,,"http://hdl.handle.net/20.500.12104/38948","http://www.scopus.com/inward/record.url?eid=2-s2.0-25144524029&partnerID=40&md5=bde88e1cc9332ada4d1ca976b4193451",,,,,,"9",,"Journal of Optics B: Quantum and Semiclassical Optics",,"28