Murat Koklu

An Investigation of Students, Parents and Teachers’ Satisfaction in Science High Schools in Konya Province in Terms of Some Variabilities

Education is the most decisive factor in the success of people in life and work. Today expectations in education have changed. Increases in education levels and facility of ways to access information have differentiated our level of social consciousness. Educational expectations of parents and teachers have also changed. There is now a mass who feign reluctance more and have a higher expectation from the school. Teachers’ expectations of students are also increasing. Researches examining the effects of school variables on student achievement have increased in recent years. Many studies indicate that schools with desirable characteristics have positive effects on student achievement. There are a large number of components that make the school environment come to life. Classroom sizes at schools, school culture, teaching methods used by teachers and professional qualifications are some of them. These components affect the satisfaction levels of teachers and parents working in schools. The aim of this research is to examine the satisfaction of teachers, students and students’ parents in Meram Science High School, Selcuklu Science High School and Karatay Science High School in terms of some variables in Konya province. Satisfaction scale was developed by researchers. Conducting a literature survey,the researchers have found that there were 46 questions and 3 open ended questions for the Student Satisfaction Scale, 45 questions in 3 sections including 5 demographic questions, 37 questionnaires and 3 open ended questions for the Parent Satisfaction Scale; For the Teacher Satisfaction Scale, 3 demographic questions, 43 scale questionnaires and 5 open-ended questions,the scales were finalized with 51 questions in 3 sections. When the answers given by the students are examined, the school satisfaction ratings were determined as undecided. When the answers given by the parents were examined, the school satisfaction level was partially determined as agreeing. When the answers given by the teachers were examined, the school satisfaction level was determined as strongly agreeing.

Diagnosis of urinary tract infection based on artificial intelligence methods

BACKGROUND AND OBJECTIVE Urinary tract infection (UTI) is a common disease affecting the vast majority of people. UTI involves a simple infection caused by urinary tract inflammation as well as a complicated infection that may be caused by an inflammation of other urinary tract organs. Since all of these infections have similar symptoms, it is difficult to identify the cause of primary infection. Therefore, it is not easy to diagnose a UTI with routine examination procedures. Invasive methods that require surgery could be necessary. This study aims to develop an artificial intelligence model to support the diagnosis of UTI with complex symptoms. METHODS Firstly, routine examination data and definitive diagnosis results for 59 UTI patients gathered and composed as a UTI dataset. Three classification models namely; decision tree (DT), support vector machine (SVM), random forest (RF) and artificial neural network (ANN), which are widely used in medical diagnosis systems, were created to model the definitive diagnosis results using the composed UTI dataset. Accuracy, specificity and sensitivity statistical measurements were used to determine the performance of created models. RESULTS DT, SVM, RF and ANN models have 93.22%, 96.61%, 96.61%, 98.30% accuracy, 95.55%, 97.77%, 95.55%, 97.77% sensitivity and 85.71%, 92.85%, 100%, 100% specificy results, respectively. CONCLUSIONS ANN has the highest accuracy result of 98.3% for UTI diagnosis within the proposed models. Although several symptoms, laboratory findings, and ultrasound results are needed for clinical UTI diagnosis, this ANN model only needs pollacuria, suprapubic pain symptoms and erythrocyte to get the same diagnosis with such accuracy. This proposed model is a successful medical decision support system for UTI with complex symptoms. Usage of this artificial intelligence method has its advantages of lower diagnosis cost, lower diagnosis time and there is no need for invasive methods.