military university

Teachers' beliefs and their alignment with teaching methods significantly impact students' growth and academic progress. Given the importance of the English language in defense and military contexts at military and Defence universities,, this study aims to explore the beliefs of English language professors and how these beliefs influence teaching methods and associated challenges. This research utilizes a mixed exploratory data collection method. Initially, participants completed a structured questionnaire with closed questions to assess their beliefs in teaching grammar. Subsequently, 15 participants engaged in open and semi-structured interviews. The statistical population comprises 102 English language professors from various universities of the Islamic Republic of Iran Army, selected using a non-random method (availability). The findings of this research examine teachers' beliefs regarding learning grammar in seven categories, including the significance of grammar in education, inductive approach, analogical approach, focus on form approach, meaning-based approach, value of practice, feedback, and error correction in grammar. Quantitative data was analyzed using SPSS software, while qualitative data underwent content analysis. The results suggest the importance of teaching grammar in language instruction and the necessity for teachers to align their beliefs to address educational challenges effectively.

This research aims to conduct a comprehensive review and meta-analysis of previous studies on the use of machine learning to improve educational quality. The study seeks to identify gaps in existing research and provide new insights for optimizing educational methods through the application of machine learning. The research method involves a meta-analysis of prior studies in this field, revealing that most research has focused on the benefits of machine learning, but its challenges and limitations have not been thoroughly examined. The findings of this research indicate that machine learning can significantly contribute to enhancing educational quality. However, challenges such as data privacy issues, the need for advanced technological infrastructure, and potential resistance to adopting new technologies have not been fully explored. Additionally, the cultural and social impacts of using machine learning in educational settings, particularly in military academies, have received less attention. This lack of focus could have significant implications for the success or failure of implementing these technologies. The conclusion of this research focuses on providing practical recommendations and strategies for optimizing the use of machine learning in the education system. These recommendations can assist decision-makers and policymakers in developing and implementing more effective programs and strategies to enhance educational quality. By identifying and addressing existing gaps, this study paves the way for a more effective utilization of machine learning in improving educational quality.

Improving educational quality requires attention to factors such as educational content, teaching methods, the quality of instructors, and the educational environment. Previous research has shown that interactive teaching methods, up-to-date and relevant content, and a suitable educational environment can have a significant impact on the quality of education. Machine learning can analyze educational data to identify student learning patterns and provide personalized recommendations that improve their performance and motivation. Numerous studies in this field have examined the role of machine learning in enhancing educational quality, but there has not been a comprehensive review of the literature in this area. This study aims to review previous research in the field under review and demonstrates that most of these studies focus on the benefits of machine learning without thoroughly examining its challenges and limitations. Among the challenges in this area, particularly with regard to improving educational quality in military academies, are issues related to data privacy, the need for advanced technological infrastructure, and the potential resistance of instructors and students to adopting new technologies. Additionally, the cultural and social impacts of using machine learning in military academies have received less attention. The main objective of this research is to identify and fill these research gaps and provide solutions for the optimal use of machine learning in improving educational quality. By offering practical recommendations and solutions, this research can assist decision-makers and policymakers in developing more effective programs and strategies to enhance educational quality.

This research is intended to investigate factors affecting self-control culture through an Islamic approach in a military university. The identified indicators will be classified under more general topics. They will be prioritized according to their effectiveness. The research population contains all of the 80 members of the faculty of humanities in a military university whose members are more familiar with culture and self-control. They filled in the questionnaires to offer their ideas. Explorative factor analysis, coefficient of variance parameter, and SPSS were employed for data analysis. Considering the research findings, indicators were categorized intofour groups (factors). It was finally found out that motivating the subjectsby combining individual and organizational goals gained the first priority. The results indicated thatthis organization gained a high spiritual maturity, because the spiritual needs and self-actualization were considered highly in the organization. High priority of managerial factors indicate that mangers must pay attention to these factors more than before.