درخت حوزه‌های تخصصی

Purpose: The current research was conducted with the aim of validating the structural model of open data governance in line with the health of the administrative system in e-government in government organizations. Method: This research, with a practical purpose, was based on a descriptive survey implementation method. The statistical population of the research included the managers of the country's tax affairs organization and several university professors, all of whom (18 person) were selected as a statistical sample. The data collection tool in this research was a researcher-made questionnaire, and confirmatory factor analysis, structural equation modeling, and Smart PLS software were used for data analysis. Findings: The results of the research show that the structural model of open data governance in the direction of administrative health in e-government at governmental organizations includes six components: platforms (with the dimensions of intra-organizational platforms and external platforms), strategies (with the dimensions of common strategies—internal and external—, intra-organizational strategies, and extra-organizational strategies), intervening conditions (with common dimensions—internal and external), causal category (with dimensions of internal-external organizational factors, individual and social factors), central category, and consequences (with common dimensions—internal and external). Conclusion: Intra-organizational and extra-organizational platforms have a positive and significant effect on common strategies. The strategies of this research have a positive and significant effect on the results. The impact of intervening conditions on the central category is positive and significant. The effect of the causal category of this research, including individual factors and social factors, on the central category, is negative and significant because the regression coefficient has become negative. The impact of the central category on strategies has become negative, but this impact is not significant.

Purpose: The primary objective of this research was to integrate ChatGPT, an advanced artificial intelligence language model, into distance science education. By addressing the specific challenges inherent in online learning environments, this study aims to investigate how technological advancements, such as ChatGPT, can effectively enhance the management and overall quality of distance education experiences. Method: This qualitative, exploratory study aimed to provide a comprehensive understanding of the potential applications of ChatGPT in distance science education. This study identifies the various interactions that can occur in distance education and describes and evaluates the input and output instructions associated with each interaction. The chat bot used is https://chatbot.theb.ai and A biology tutoring platform uses ChatGPT to provide personalized support to students working on biology issues or concepts. Findings: Based on the results, the artificial intelligence can play an important role in improving distance science education. Furthermore, this study highlights the importance of developing metacognitive skills among learners and encourages educators to seek professional development opportunities to enhance the quality and engagement of distance science education. This study emphasizes the need for careful planning, research, design, evaluation, management, and responsible application of AI technology in distance education. Conclusion: This study was concluded that ChatGPT could be very useful for both learners and educators in distance education. This research showcases the innovative results facilitated by ChatGPT and provides valuable insights and practical guidance for educators, researchers, and policy makers on the effective use of AI technologies in distance science education.

Purpose: The purpose of the current research is to extract the components and dimensions of digital transformation and determine the levels and sequence of dimensions in Iran's education and upbringing, for the planning and implementation roadmap.Method: This research has a mixed approach (qualitative-quantitative). The qualitative section was able to extract the components of digital transformation by searching for authentic Persian and English articles published in the last ten years. With the help of seven experts, the digital transformation components were classified into eight dimensions in the form of 109 identified sub-components using the ISM method. Dimension levels were determined by SSIM matrix calculations in the quantitative part. MICMAC software version 5.3.0 was used to calculate the impact of relationships.Findings: Based on the opinion of experts and the results of ISM calculations, the identified dimensions including: institutional, education, technology, infrastructural, social, cultural and economic were placed in four levels.Conclusion: According to experts, the institutional dimension, being placed at the fourth level, has the greatest impact on digital transformation in Iran's education. Therefore, it is one of the key dimensions to achieve digital educational transformation that is placed at the first level of the model. According to the results of ISM, the economic and cultural dimensions were placed at the third level. Also, in analyzing the impact of relationships between dimensions using MICMAC software, the results indicate that the institutional dimension has a significant impact on infrastructure development.

Students’ engagement in classroom activities has been at the center of attention these days that affect learners’ success, mental health and alternatively, their disengagement impacts their achievement and may also affect their future professional well-being. Motivation is another important contributor to success in education, which is also in line with engagement. Likewise, demotivation has been a concentration point of instructional research; yet, it has not obtained much concentration in the realm of language acquisition. Indeed, English as Foreign Language (EFL) learners’ demotivation has an essential role in hindering their learning in the English language learning classroom. The level of these two concepts, namely disengagement and demotivation is influenced by different factors in students and as far as EFL teaching is concerned, one of these factors is school climate which is deemed as a predictor of academic success. Indeed, the learning climate has been attracting the attention of researchers for decades given that this construct improves or prevents student achievement and poor mental health. Nevertheless, its contribution to mitigating learners’ demotivation and disengagement has not been scrutinized yet. Accordingly, this review presents the power of the learning climate where EFL students are learning a language and its role in students’ demotivation and disengagement. Subsequently, this is followed by giving some implications for EFL teachers, school authorities, and researchers.