The impact of learning environment based on multiple intelligence designated by fuzzy logic to students' achievement

The general purpose of this research is to determine the academic success of students which are presented depending on the intelligence type determined via fuzzy logic. Differences in the types of intelligence that shows the effect on students in their learning environment. When training programs are preparing, they are trying to take into account these differences. To determine these differences, literature studies were conducted. There is no study which detemines intelligence type by fuzzy logic. That is why, this research is needed. Thus, thanks to one of the artificial intelligence technology which is called fuzzy logic, individual intelligence type is aimed to determine by membership degrees. The population for the research consists of students in the Computer Education and Instructional Technology department of Çukurova University. The sample for the research consists of the 2. And 3. students who took Programming Languages I lesson. To control group, traditional method was used. On the other hand, to experimental group fuzzy logic has been applied according to the intelligence type. Cluster analysis was conducted to ensure neutrality . The theoretical dimension was formed by evaluating data obtained by literature scanning and expert opinions. The data were collected through Multiple Intelligences scale and achievement test. Multiple Intelligence Scale was given to students before application .Achievement test were given to students before and after the application . After the control and experimental groups formed, the conventional method was implemented on the control group, and prepared programmes by using Flash Professional CS6 were administered to the experimental groups. In the reliability and validity analysis for measuring Instruments, Babacan and Dilci (2012) was made the reliability and validity of the Multiple Intelligence Scale. Because same questions were used, reliability and validity tests was not made again. Babacan and Dilci (2012 ) 's Cronbach internal consistency coefficient results is listed. .85 for physical intelligence, .85 for existential intelligence, .78 for Interpersonal intelligence, .84 for internal intelligence, .75 for the logical intelligence, .74 for Musical Intelligence, .73 for naturalists intelligence , .84 for verbal intelligence and .86 for visual intelligence was found. Programming languages test scale was evaluated over 100 points. Scale was created by Tukiainen and Mönkkönen (2002) and Yılmaz and Çakır (2013) was finalized. Exam questions are divided into five sections. Each section of student achievement scores are calculated over 20 points . In the stage of determining the questions, Yılmaz and Çakır (2013) was used table of specifications based on experts' opinions. The table of specifications was prepared according to the scale presented to the three experts for validity. Experts consist of faculty members. Achievement test rearranged according to the feedback received from the experts. For the reliability of the test, Pearson Correlation was made. Positive and significant relationship was observed between first and second expert who read pre-test paper results. [r (=0.867; p<0.01] . Frequency, percentage and arithmetical average were applied for analysis related to the participants. In addition, in the analysis of the obtained data, a dependent and independent groups' test were used in comparison of pre-test and post-test points. Computer-aided teaching materials was developed by Adobe Flash Professional CS6 and the type of intelligence was developed by using fuzzy logic system. An experimental and control groups was created by the cluster analysis. The research determined the impact of teaching according to fuzzy logic, which is an independent variable, and the conventional education programme on student achievement, which is a dependent variable. A mamdani type-fuzzy logic algorithm mechanism which has nine entries and one exit was used with the aim of determining intelligence type of students. Membership degrees of the points students took in the entry membership functions were calculated as well. Input values are the students receive their scores from multiple intelligences scale. The scores of the students was calculated according to membership degrees based on input membership functions. These values were passed through a fuzzy logic mechanism which was developed using Matlab software. Data obtained through implementation of this research were passed through statistical data analysis methods, producing the findings. Then these findings were interpreted. According to results of the analysis, which was made by taking the two groups' points into consideration; a significant difference is not an evident among the averages of the two groups. This difference was observed between the control group's and the experimental group's post-test. One of the main reasons is that Programming Languages I course that can not be acquired by the students basic computer skills are thought to be include skills. As programming languages was an area requiring expertise, almost all of the students showed that they were ignorant beginning of the course. Implementation of the lesson even with a conventional method or a contempotary method leads to general improvement among students. So, even though the students are in the control group or experimental group, an increase in the students' post-test results was observed .