Volume 4, Issue 3 (September 2025)
IJER 2025, 4(3): 1-23 |
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Dortaj Y, Mashayekh P, Ghasemi A. (2025). Designing and Validation of In-Service Teacher Training Courses Based on Cloud Computing. IJER. 4(3), 1-23.
URL: http://ijer.hormozgan.ac.ir/article-1-142-en.html
URL: http://ijer.hormozgan.ac.ir/article-1-142-en.html
1- Ph.D. Student of Educational Administration, Kazrun Branch, Islamic Azad University, Kazrun, Iran
2- Assistant Professor, Department of Educational Administration, Kazrun Branch, Islamic Azad University, Kazrun, Iran ,pary.mashayekh@iau.ac.ir
3- Assistant Professor, Department of Educational Sciences, Farhangian University, Tehran, Iran
2- Assistant Professor, Department of Educational Administration, Kazrun Branch, Islamic Azad University, Kazrun, Iran ,
3- Assistant Professor, Department of Educational Sciences, Farhangian University, Tehran, Iran
Abstract: (1251 Views)
Objective: In recent years, cloud computing has led to new cloud-based learning tools for collaborating and sharing content with large numbers of students. This study aimed to propose a model for the use of cloud computing in in-service teacher training courses and to validate this model.
Methods: This study was applied in terms of purpose and adopted a mixed method with an exploratory approach. The statistical population of the qualitative study consisted of experts in the field of computer technology and curriculum planning who were selected purposefully, and finally, due to the saturation method, 14 experts were selected as a sample. The statistical population in the quantitative phase included 250 participants selected by stratified random sampling. The research data collection tool in the qualitative phase was a semi-structured interview whereas, in the quantitative section, a researcher-made questionnaire was extracted from interviews.
Results: Data in the qualitative phase were analyzed using the thematic analysis method, and in the quantitative phase, confirmatory factor analysis, divergent and convergent validity were used. The face, content, and construct validity of the instrument were confirmed. Their composite reliability and Cronbach's alpha were calculated above 0.70, which was approved. Finally, the model of cloud computing in in-service teacher training courses was presented with five factors including managerial, cultural, human resources, financial and physical and technology. The results of the confirmatory factor analysis showed that the correlations between the data are suitable for factor analysis and have the necessary and sufficient coherence.
Conclusion: The results indicate that the Ministry of Education is not optimally positioned in terms of institutional, managerial, support and technological aspects for the implementation of in-service teacher training programs on the cloud computing platform. The findings of this study can benefit teacher educators, policymakers, and in-service education and training (INSET) programmers. Training skillful and knowledgeable teachers can reduce the time, energy, and expense spent on teaching.
Methods: This study was applied in terms of purpose and adopted a mixed method with an exploratory approach. The statistical population of the qualitative study consisted of experts in the field of computer technology and curriculum planning who were selected purposefully, and finally, due to the saturation method, 14 experts were selected as a sample. The statistical population in the quantitative phase included 250 participants selected by stratified random sampling. The research data collection tool in the qualitative phase was a semi-structured interview whereas, in the quantitative section, a researcher-made questionnaire was extracted from interviews.
Results: Data in the qualitative phase were analyzed using the thematic analysis method, and in the quantitative phase, confirmatory factor analysis, divergent and convergent validity were used. The face, content, and construct validity of the instrument were confirmed. Their composite reliability and Cronbach's alpha were calculated above 0.70, which was approved. Finally, the model of cloud computing in in-service teacher training courses was presented with five factors including managerial, cultural, human resources, financial and physical and technology. The results of the confirmatory factor analysis showed that the correlations between the data are suitable for factor analysis and have the necessary and sufficient coherence.
Conclusion: The results indicate that the Ministry of Education is not optimally positioned in terms of institutional, managerial, support and technological aspects for the implementation of in-service teacher training programs on the cloud computing platform. The findings of this study can benefit teacher educators, policymakers, and in-service education and training (INSET) programmers. Training skillful and knowledgeable teachers can reduce the time, energy, and expense spent on teaching.
Type of Study: Original |
Subject:
Educational Studies
Received: 2024/02/15 | Accepted: 2024/12/16 | Published: 2025/09/1
Received: 2024/02/15 | Accepted: 2024/12/16 | Published: 2025/09/1
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