درخت حوزه‌های تخصصی

Purpose: This study aimed to plan and validate the product creativity evaluation model in architectural education. According to three product creativity evaluation models: CPAM, CSDS and PCMI. Methodology: The research was applied in terms of purpose and in terms of data collection among descriptive studies was correlational and in terms of method was mixed exploratory research. The statistical population in the qualitative section included all experts in education and creativity in Mashhad. A sample of 10 people was selected by purposive sampling using the principles of theoretical saturation from the mentioned community. In the quantitative part, the statistical population included all university professors in the field of architecture in Mashhad, and 150 people were selected by random sampling. The required data was collected in the summer of 2020 using semi-structured interviews and upstream documents in the qualitative phase. The measurement tool in the quantitative section was a researcher-made questionnaire whose construct validity was calculated using factor analysis, and the reliability value through Cronbach's alpha coefficient was 0.82. The researcher carried out qualitative data analysis using the thematic analysis coding approach developed by Braun and Clarke and quantitative data analysis using the partial least squares method. Findings: The results revealed that five components with 12 indicators make the status of novelty with two markers of originality and surprise. Relevance to the problem includes two indicators of regulatory compliance and practicality. In addition, the effectiveness of solutions has three indicators, namely meeting the needs of contacts, being environmentally friendly, and having durability. Design elements include two indicators, details (well-defined component) and elegance, and the collection of design elements contains the three indicators of harmonization, completeness, and well-formation (well-crafted) at the 95% confidence interval (p = .001). In addition, confirmed element analysis of the obtained components revealed that all factor loads were significant, and there is an acceptable agreement between the creativity model of architectural design products and the data. The heavy element load related to novelty (0.91) and the light one are related to the collection of design elements. Conclusion: Based on the findings, it can be concluded that according to the experts, the factor of novelty, such as originality and surprise, along with functional factors such as relevance, effectiveness, and artistic factors consisting of the design of elements, and the collection of design elements affects the designing creativity evaluation model of problem-solving results.

Purpose: this study was conducted aimed to provide a knowledge management model for the branches of the Islamic Azad University of West Azerbaijan Province. Methodology: The present study was applied in terms of aim and quantitatively correlated in terms of implementation method. The study population was 654 faculty members of the Islamic Azad University of West Azerbaijan Province during the academic years 2020-21. The sample size was estimated n = 243 based on Cochran's formula who were selected by cluster sampling method with respect to gender ratio and scientific rank. The data were collected by a researcher-made questionnaire (74 items), content validity of which was confirmed by experts and its reliability was calculated by Cronbach's alpha of 0.90. Exploratory factor analysis and structural equation modeling by SPSS-23 and LISREL-8.8 software were used for data analysis. Findings: The results of factor analysis showed that knowledge management with eight factors of infrastructure and information technology, knowledge management processes, organizational structure and setting, intellectual and innovative capital, human resources and training, knowledge leadership, and organizational management and culture explained 74% of the total variance of knowledge management. The results of structural equation modeling showed that the knowledge management model had a good fit and the eight factors had a direct and significant effect on knowledge management (P <0.05). Conclusion: According to the study results, planning to improve knowledge management through factors o infrastructure and information technology factors, knowledge management processes, organizational structure and setting, intellectual and innovative capital, human resources and training, knowledge leadership, and organizational management and culture is essential. For this purpose, the use of training workshops can be effective.

Purpose: The aim of the present study was to identify the causes of social isolation and design a relevant model for high school students. Methodology: This was an applied study in terms of its aim and mixed-method study in terms of its implementation method (qualitative and quantitative). The study population in the qualitative section included documents and resources and educational management experts and, in the quantitative section, 180 female high-school first-year students in Gorgan during the academic year 2019-2020. According to the data saturation principle, the research population in the qualitative part included documents and resources related to social isolation and 10 educational management experts who were selected by purposive sampling. According to Cochran's formula, the study population in the quantitative section included 180 female high-school first-year students who were selected by cluster sampling method. Data were collected by note taking, interviews and a researcher-made questionnaire (39 items), the construct and convergent validity of which were confirmed and its reliability was obtained α=0.96 by Cronbach's alpha method. Data were analyzed by open and axial coding methods, factor analysis and structural equation modeling in MAXQDA software. Findings: The findings of the qualitative section showed that social isolation consists of six components and 40 indicators, including socio-economic anomalies (7 indicators), individual variables (9 indicators), parental role (5 indicators), strengthening social relations (6 indicators), psychological interactions (10 indicators), and family level (3 indicators). The findings of the quantitative section showed that the social isolation model had a good fit and all six components, i.e. socio-economic anomalies, individual variables, parental role, strengthening social relations, psychological interactions and family level had a significant effect on social isolation (P<0.05). Conclusion: According to the results, sociologists can use the social isolation model designed in the present study to reduce social isolation; therefore, it is necessary to pay attention to the role of effective components.