Designing and validating the competency model of smart school principals

The present study aimed to design and validate the competency model of smart school principals. The statistical population of the present study in the qualitative section included all university professors related to smart schools (members of the Smart Schools Council), technologists and specialists, education officials and smart school principals. 22 smart school principals and educational technology experts from 6 provinces of Kermanshah, Ilam, Qazvin, Bandar Abbas, Hamedan and Fars were randomly selected. In a small part of the cluster random sampling method, based on Cochran's formula, 386 questionnaires were used. In the qualitative part of the research, structured interviews were used and data were analyzed using Maxa Kyoda software. Then, in a small part, using SMART.PLS software, the relationships between variables are examined. Lincoln and Guba (1985) approach was used to ensure validity and reliability. The results showed that the results of open coding of qualitative data collected using the interview tool, it was observed that 62 open codes out of 370 concepts were identified. In the axial coding section, 62 primary codes and in the selective coding section, 15 categories including values and attitudes of the principal and staff, evaluation, supervision and control, principal knowledge competence, general competencies of smart school principals, professional competencies of principals, school infrastructure conditions, Social and cultural factors, economic factors, religious factors, resource and educational management, teacher skills and competencies, principal skills and competencies, cooperation and collaboration with staff and organization, school intelligence, personality traits identification Were. Model validation also showed that all known relationships in the paradigm model were validated.The present study aimed to design and validate the competency model of smart school principals. The statistical population of the present study in the qualitative section included all university professors related to smart schools (members of the Smart Schools Council), technologists and specialists, education officials and smart school principals. 22 smart school principals and educational technology experts from 6 provinces of Kermanshah, Ilam, Qazvin, Bandar Abbas, Hamedan and Fars were randomly selected. In a small part of the cluster random sampling method, based on Cochran's formula, 386 questionnaires were used. In the qualitative part of the research, structured interviews were used and data were analyzed using Maxa Kyoda software. Then, in a small part, using SMART.PLS software, the relationships between variables are examined. Lincoln and Guba (1985) approach was used to ensure validity and reliability. The results showed that the results of open coding of qualitative data collected using the interview tool, it was observed that 62 open codes out of 370 concepts were identified. In the axial coding section, 62 primary codes and in the selective coding section, 15 categories including values and attitudes of the principal and staff, evaluation, supervision and control, principal knowledge competence, general competencies of smart school principals, professional competencies of principals, school infrastructure conditions, Social and cultural factors, economic factors, religious factors, resource and educational management, teacher skills and competencies, principal skills and competencies, cooperation and collaboration with staff and organization, school intelligence, personality traits identification Were. Model validation also showed that all known relationships in the paradigm model were validated.The present study aimed to design and validate the competency model of smart school principals. The statistical population of the present study in the qualitative section included all university professors related to smart schools (members of the Smart Schools Council), technologists and specialists, education officials and smart school principals. 22 smart school principals and educational technology experts from 6 provinces of Kermanshah, Ilam, Qazvin, Bandar Abbas, Hamedan and Fars were randomly selected. In a small part of the cluster random sampling method, based on Cochran's formula, 386 questionnaires were used. In the qualitative part of the research, structured interviews were used and data were analyzed using Maxa Kyoda software. Then, in a small part, using SMART.PLS software, the relationships between variables are examined. Lincoln and Guba (1985) approach was used to ensure validity and reliability. The results showed that the results of open coding of qualitative data collected using the interview tool, it was observed that 62 open codes out of 370 concepts were identified. In the axial coding section, 62 primary codes and in the selective coding section, 15 categories including values and attitudes of the principal and staff, evaluation, supervision and control, principal knowledge competence, general competencies of smart school principals, professional competencies of principals, school infrastructure conditions, Social and cultural factors, economic factors, religious factors, resource and educational management, teacher skills and competencies, principal skills and competencies, cooperation and collaboration with staff and organization, school intelligence, personality traits identification Were. Model validation also showed that all known relationships in the paradigm model were validated.The present study aimed to design and validate the competency model of smart school principals. The statistical population of the present study in the qualitative section included all university professors related to smart schools (members of the Smart Schools Council), technologists and specialists, education officials and smart school principals. 22 smart school principals and educational technology experts from 6 provinces of Kermanshah, Ilam, Qazvin, Bandar Abbas, Hamedan and Fars were randomly selected. In a small part of the cluster random sampling method, based on Cochran's formula, 386 questionnaires were used. In the qualitative part of the research, structured interviews were used and data were analyzed using Maxa Kyoda software. Then, in a small part, using SMART.PLS software, the relationships between variables are examined.