درخت حوزه‌های تخصصی

Purpose: The present study was conducted to identify the components of the learning organization in primary schools in Tehran. Methodology: The research method was exploratory in terms of practical and qualitative purpose. The statistical population of the study included academic experts and education experts. Samples in this section were selected by targeted sampling and theoretical saturation. The research tools were semi-constructed interviews. In order to ensure the validity of the research tools and to ensure the accuracy of the findings from the researcher's point of view, the valuable views of professor’s familiar with the field and academic experts who were experts in this field were used. To calculate the reliability of the interview, the method of agreement within the subject matter of the two coders was used. The data analysis method in this study was theoretical coding derived from the fundamental data processing theory method. Findings: The results of the study showed that the components of the learning organization, including the components of individual mastery, mental patterns, systemic thinking, common aspiration, team learning and information technology. Also, the components influencing the learning organization in elementary school included the components of organizational culture, leadership, and human resources. Conclusion: Having individual mastery among teachers and having positive mindset patterns is very important for learning that results in the use of modern learning methods, such as team learning.

Purpose: The present study was conducted with the aim of presenting a professional development model for high school teachers. Methodology: The research is applied in terms of purpose and exploratory in terms of mixed method. In the qualitative section, the statistical population included all experts in education and training sciences in Tehran that a sample of 15 people was selected by purposive sampling using the principle of theoretical saturation from the above community. In the quantitative part, the statistical population included all high school teachers in Tehran that 382 people were selected by stepwise cluster sampling. In order to collect data in the qualitative part, an in-depth semi-structured interview with 5 open-ended questions was used, and in the quantitative part, a researcher-made questionnaire was used to design a career development model. Qualitative data analysis was performed using thematic analysis method and partial least squares method was used to analyze quantitative data. Findings showed that the components of professional development according to experts included three dimensions of professional knowledge, professional commitment and professional ability. Findings: The results of factor analysis on 25 items of professional development model showed that the factor loads of the items of each factor and the relevant t-index had a factor load of more than 0.4. Also, all variables had the mean and extracted variance above 0.5. Also, in order to determine the reliability of data collection tools, Cronbach's alpha method was used. The values ​​of these coefficients for all variables were above 0.7, which indicated the appropriate reliability of the questionnaire. There is a strong link between the role of human capital and the development of countries. Conclusion: Human capital growth is a set of knowledge, skills and characteristics that lead a person to individual, organizational and social success.

Purpose: Considering the quantitative growth of university growth and technology centers in the country and the need for solutions to facilitate the success of these companies in global competition, this study identifies the indicators of innovation ecosystem of university growth and technology centers with the knowledge-based economy approach with data foundation approach. Methodology: This research was based on empirical philosophy and has been done with a deductive-inductive approach. From the perspective of the goal, it was a fundamental research that has been done with a mixed research method. The study population of this study includes entrepreneurship professors and managers who have work experience in knowledge-based companies. Sampling was done by non-probabilistic and purposeful sampling method. Data collection tools are interviews and questionnaires. In the analysis of the interviews, the data theory of the foundation (selective, open and pivotal) was used. Also, the identified indicators have been validated by fuzzy Delphi method. Finally, the final model of the research is presented. Research data analysis was performed in the qualitative phase with MAXQDA software and in the quantitative phase with Matlab software. Findings: To achieve the objectives of the research, by analyzing interviews with experts, a set of practical indicators of the innovation ecosystem were identified. In the open coding stage, 517 codes were identified, which with the observed observations, 9 main categories and 41 sub-categories were obtained. Conclusion: Based on the achievements of this study, issues such as organizational management, growth center rules and regulations, innovation ecosystem infrastructure, knowledge ecosystem, innovation ecosystem, ecosystem capital, economic ecosystem, innovation ecosystem challenges and key players are key elements in the success of these universities in knowledge-based economy was considered. These cases also interact with each other in 41 minor areas.

Purpose: The aim of this study was to investigate the effect of students' attitudes toward entrepreneurship education and entrepreneurial environment on entrepreneurial intention with respect to the moderating role of gender and family background. Methodology: The present research was applied in terms of purpose and quantitative research in terms of data collection. The statistical population included all business management students of Islamic Azad University, Tehran Branch of Science and Research, who have taken entrepreneurship courses as part of their curriculum. To determine the sample size, stratified random sampling was used and 297 people were selected using Morgan table. The data collection tool was the Jena Standard Questionnaire (2020) based on a 5-item scale. The validity of the questionnaire was confirmed in terms of form and content by several experts, the validity was confirmed by calculating the trough and the validity of the questionnaire was confirmed by calculating the square root of the AVE. The reliability of the questionnaire was obtained through Cronbachs alpha for the whole questionnaire of 0.968.for data analysis, Cronbach's alpha tests, orthodontics (AVE), AVE root matrix, Kolmogrofs-Smirnov, confirmatory factor analysis and t-test bootstrap approach were used. Findings: The results showed that each of the components of attitude (emotional, behavioral and cognitive) and entrepreneurial environment has a significant effect on entrepreneurial intention. Discussion: This study also confirmed the effect of control groups (gender and family history of the entrepreneur) on the relationship between entrepreneurial attitude and intention.

Purpose: This study was conducted with the aim of determine the effect of wisdom and socio-economic status of the family through the mediation of academic self-concept on academic vitality. Methodology: The purpose of the research was applied and the research method was descriptive correlation type. The statistical population of research included all female high school students in district 2 of Tehran in 2018 with a total of 1200 people, 300 people According to Kerjeci- Morgan table were selected by multistage random sampling. The study tools were included Wisdom Measurement by Schmitt et al., (2012); Socio-Economic Status Inventory by Ghodratnama et al., (2013); School Self-Concept Inventory by Chen & Thompson (2003) and Educational Vitality Questionnaire by Dehghani Zadeh et al., (2013). The data were analyzed using structural equation modeling and Amos software. Findings: The results of the present study showed that wisdom has a positive and significant effect on self-concept (P=0.001). Wisdom has a positive and significant effect on academic vitality (P=0.001). Socio-economic status has no direct effect on self-concept (P=0.208). Socio-economic status has no direct effect on academic vitality (P=0.358). Self-concept has a positive and significant effect on academic vitality (P=0.001). Wisdom has an indirect and significant effect on academic vitality through the mediation of academic self-concept (P=0.001). Socio-economic status has no indirect effect on academic vitality in a positive and significant way with the mediation of academic self-concept (P=0.208) Conclusion: Wisdom in turn directly and indirectly through the mediation of self-concept affects the students' academic vitality.