Document Type : Research Paper
Authors
1 PhD student in educational management, Sari Branch, Islamic Azad University, Sari, Iran .
2 Professor of the Department of Educational Management, Sari Branch, Islamic Azad University, Sari, Iran.
3 Professor, Assessment and measurement department, Tabataba’i University, Tehran,
4 Assistant Professor of Educational Management Department, Islamic Azad University, Sari, Iran
Abstract
The aim of this study is to analyze the competencies that affect the acceptance and use of educational technologies with a cognitive and motivational approach among teachers and trainers of technical and vocational centers across the country. The data collection process included administering a questionnaire to a sample of 190 teachers and trainers of technical and vocational centers across the country. In addition, semi-structured interviews were conducted with 18 experts, specialists, and knowledgeable individuals to gather deeper insights. Using the content analysis method, 87 indicators (items), 2 main dimensions, and 12 components can be identified. The face and content validity of the questionnaire has been confirmed by experts. Also, convergent and divergent validity were used to examine the construct validity, and Cronbach's alpha and composite reliability were used to measure reliability. Descriptive and inferential statistical methods and structural equation analysis using Smart PLS software were used to analyze the data. The results of the current situation in 12 components of integrated cognitive and motivational competencies of educators and lecturers are significant. The ranking results indicate that the professional skills of educators, the development of creativity and the desire to innovate using technology and the motivation for change and the spirit of curiosity are the main competencies. The results of the structural equation model show that all the components identified for integrated cognitive and motivational competencies are significantly related to these structures. In other words, these components have been able to explain these structures well. Positive values of the redundancy validity index for all components indicate good model fit and appropriate predictive power. The coefficient of determination of the average shows that the value of the standardized root mean square residual index and the normal fit index obtained indicate a good fit of the model.
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