درخت حوزه‌های تخصصی

Introduction: This study aimed to provide a model in order to educate professional citizens through knowledge-oriented era of Iran's education system. Methodology: The statistical population of the research consisted of all female and male principals and deputies of governmental elementary schools within cities of Tehran province (N = 11382). The sample size estimated to be 362 people based on the Cochran formula. The data collection tool was a researcher-made questionnaire based on a 5-point scale. Findings: Data analysis was performed by Cronbach's alpha test, Average Extracted Variance (AVE), Square root of AVE, Kolmogorov-Smirnov, Exploratory Factor Analysis, Confirmatory Factor Analysis and Friedman ranking test. Reviewing the research literature for understanding the complexity and extent of theoretical and practical domain of the research in training professional citizenship, and also reviewing the models, theories, and the study components, a global model of educating professional citizens was developed including the components of philosophy, goals, Perceptual framework, procedure of implementation, and system of evaluation and engineering. A survey was conducted through 30 experts of this field in order to determine the degree of quality of the proposed model for presenting the final model through an evaluation questionnaire including the philosophy and objectives of the model, the theoretical foundations, dimensions, components, indices, and mechanisms for educating a professional citizen in the knowledge-oriented era of Iran’s education system. In addition, this questionnaire was confirmed with a scale of 4.64 out of 5. Conclusion: Using this model, the country's education and training system can provide knowledgeable, committed, skilled, and professional citizens for society, knowledge economy and knowledge management in response to the third millennium generation.

Handwriting Analysis has been used for a very long time to analyze an individual’s suitability for a job, and is in recent times, gaining popularity as a valid means of a person’s evaluation. Extensive Research has been done in the field of determining the Personality Traits of a person through handwriting. We intend to analyze an individual’s personality by breaking it down into the Big Five Personality Traits using their handwriting samples. We present a dataset that links personality traits to the handwriting features. We then propose our algorithm - consisting of one ANN based model and PersonaNet, a CNN based model. The paper evaluates our algorithm’s performance with baseline machine learning models on our dataset. Testing our novel architecture on this dataset, we compare our algorithm based on various metrics, and show that our novel algorithm performs better than the baseline Machine Learning models.

Nowadays, English has become an international language and is used for communication and interaction among different countries and cultures across the world. So, the ability to communicate in a comprehensive way is one of the major goals of any second language educational setting. The aims of the present research were to investigate the relationship between Iranian EFL learners’ social intelligence (SI) and their willingness to communicate (WTC) across gender, as well as to find out any possible difference between male and female learners in SI and their WTC relationship. To this end, 240 male and female EFL learners from different institutions were selected as the participants of the study with different age ranges based on convenient sampling. Then two standardized questionnaires, i.e., SI and WTC, were administered for all participants to answer. After collecting the required data and its analysis, the findings revealed a positive and significant correlation between male and female SI and their WTC. In addition, further statistical analysis using formula showed no difference between learners’ SI and WTC relationship across gender. The findings of the present research could be applicable for EFL learners, teachers, and material developers. Keywords: