رزیابی شایستگی دانشجومعلمان آموزش زیستشناسی دانشگاه فرهنگیان با بهکارگیری شبکه عصبی پیشخور هوش مصنوعی.

Background and Objectives: Education is one of the main parts of knowledge and science production in which teachers contribute to the scientific and cultural progress of any country. Inefficiency in education and training can lead to challenges and widespread problems of social, cultural, scientific, political, religious dimensions in society. Therefore, it seems necessary to assess the competence of student teachers in terms of knowledge, skills, and attitudes especially before entering the professional field of teaching. In this research, the competency of student teachers before entering education and training was evaluated using artificial intelligence neural network as a precise computing tool. Methods: In the present study, the research method is applied and quantitative. The statistical population consisted of students of Farhangian University, and the sample included 91 teacher students who were selected via cluster sampling in 2015-2016 in the field of Biology Education of Shahid Beheshti Higher Education Center of Farhangian University. A corpus of 500 data (80% training and 20% test) was formed based on the scores of general, educational, specialized educational, theoretical, research, internship, and total average courses. The findings were assessed using the feedforward neural network method. According to the investigations carried out on the layers and the number of neurons on the data of the algorithm, a three-layer neural network was designed with two hidden layers with a number of 300 neurons and an output layer with a number of 1 neuron. Findings: The results of the examination of professional competence are based on the scores of student teachers and the extraction of new data of the seven dimensions of theoreticspecialization, education- specialization, education- practice, general, internship, research, and total average, which show the highest average with a score of 19.8 in the educational courses and the lowest average with a score of 16.67 was in theory-specialized courses. The analysis of the findings according to the educational data of the graduates and the labeling of experts showed the level of competence as 17.77. Conclusion: These results indicate that curriculum planners should pay more attention to specialized-theory courses because teachers must have the appropriate and sufficient knowledge and scientific level to present scientific materials to students in addition to learning educational lessons. Besides, it is suggested to evaluate the competence coefficient in other studies by using special questionnaires based on students' attitudes toward the teaching profession. On the other hand, the student's handwriting can be examined and studied as another indicator. Also, the neural network model of artificial intelligence should be used to determine the competence of student teachers in other fields of basic sciences and humanities. [ABSTRACT FROM AUTHOR]