Modeling Effective Teaching of Faculty Members Based on Artificial Neural Network Analysis

The aim of this study was to Modeling Effective Teaching of Faculty Members Based on Artificial Neural Network Analysis. So this study has practical purpose and in terms of research methods was combined and Hybrid-type exploration projects. In this study, all students of Islamic Azad university of Urmia were considered as the population. on qualitative section; we interviewed 24 students from Islamic Azad University of Urmia. Participants were selected using typical sampling until we research theoretical saturation while conducting deep and semi-structured interview. Using a random stratified sample size formula Cochran,361 students were selected. In this study, to collect data and information semi-structured interview and questionnaire have been used. Formal and content validity of the questionnaire was approved. Coronbach's alpha coefficient was 0/81. To analyze the data, descriptive and inferential statistical techniques were used. Based on the findings of the interview, it was determined that effective factors on effective teaching in 5 categories of personality and personal traits, scientific characteristics and literacy, ability in human relationships and communication, teaching and classroom management skills and evaluation skills. The quantitative findings also showed that from the perspective of students, the best structure for effective teaching, had an input layer with 5 input variables and a hidden layer with 3 neurons and an output layer with an output variable. Studying the importance of independent variables in predicting effective teaching showed that from the viewpoint of students, the most important is the personality and personality traits, and the least important is the academic characteristics and literacy of the teacher.