عوامل مؤثر بر تحقق شهر یادگیرنده در کلان شهر اهواز (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
پژوهش حاضر باهدف تحلیل ساختاری – تفسیری (ISM) عوامل مؤثر بر تحقق شهر یادگیرنده در کلان شهر اهواز تدوین شده است. در ابتدا با استفاده از روش اسنادی، مفاهیم و متغیرهای بررسی شده در ادبیات موضوع مشخص گردید. در ادامه بر اساس یافته های ادبیات و مصاحبه عمیق نیمه ساختاریافته با 15 نفر از خبرگان علمی و اجرایی، عوامل مؤثر بر تحقق شهر یادگیرنده در قالب 19 شاخص استخراج گردید، سپس به جهت بررسی توافق بین خبرگان و تعیین توافق و اولویت بندی شاخص های این عوامل با استفاده از روش دلفی و در طی سه دور با استفاده از ابزار پرسشنامه و در طیف لیکرت، شاخص های مرتبط موردتوافق جمعی قرار گرفت و بر اساس میانگین و انحراف معیار، شاخص های غیر اولویت دار حذف گردید و درنهایت 13 شاخص موردتوافق قرار گرفت. در انتها با استفاده از روش تحلیل ساختاری- تفسیری، سطح بندی و ایجاد مدل و تعیین نوع ارتباط عوامل و تجزیه وتحلیل انجام شد. یافته های پژوهش نشان داد که معیارهای فراهم کردن اینترنت پرسرعت و دسترسی آسان به فناوری های آموزشی (C1)، ایجاد مراکز آموزشی نوآورانه و مناسب برای انواع دانش آموزان و دانشجویان (C2)، تدوین سیاست های حمایتی از آموزش و توسعه آموزش عمومی (C8) و حمایت از صنایع و استارتاپ های مرتبط با آموزش و فناوری (C11) از نوع متغیرهای مستقل هستند. این متغیرها دارای وابستگی کم و هدایت بالا می باشند؛ به عبارتی دیگر تأثیرگذاری بالا و تأثیرپذیری کم از ویژگی های این متغیرها است. معیارهای استفاده از تکنولوژی های داده کاوی برای تحلیل نقاط قوت و ضعف سیستم آموزشی شهر (C3)، ترویج فرهنگ یادگیری در جامعه (C4)، ایجاد فرصت های مشارکت در تصمیم سازی مرتبط با آموزش شهری (C7) و توسعه سیستم های حمل ونقل عمومی و ایجاد محیط های دسترسی آسان به مراکز آموزشی (C9) از نوع وابسته هستند؛ این متغیرها دارای وابستگی قوی و هدایت ضعیف هستندFactors affecting the realization of the learning city in Ahvaz metropolis
The present study aimed to analyze the interpretive structural (ISM) factors that affect the realization of the learning city in the Ahvaz metropolis; first, the concepts and variables examined in the subject literature were determined using the documentation method. Based on the literature findings and in-depth semi-structured interviews with 15 scientific and executive experts, the factors affecting the realization of the learning city were extracted in the form of 19 indicators to check the agreement between the experts, determine the agreement, and prioritize the indicators. These factors were collectively agreed upon using the Delphi method. During three rounds, the questionnaire tool was used, and on the Likert spectrum, based on the mean and standard deviation, the non-priority indicators were removed. Finally, 13 indicators were agreed upon. In the end, using the method of structural-interpretive analysis, leveling and creating a model, and determining the relationship between the factors and analysis were done. The research findings showed that the criteria of providing high-speed internet and easy access to educational technologies (C1), creating innovative and suitable educational centers for all types of students and students (C2), formulating policies to support education and the development of public education (C8), and supporting industries and startups related to education and technology (C11) are independent variables. These variables have low dependence and high driving power. In other words, high influence and low dependence are the characteristics of these variables. The criteria for using data mining technologies to analyze the strengths and weaknesses of the city’s educational system (C3), promote the culture of learning in society (C4), create opportunities for participation in decision-making related to urban education (C7), and develop public transportation systems and the creation of accessible access environments to educational centers (C9) are dependent variables. These variables have strong dependence and weak driving power.
Extended Abstract
Introduction
With the development of learning at the beginning of the 21st century, the world is in the middle of significant changes. In the meantime, the cities of the world are at the center of attention for these changes in the learning process. The structural and functional changes in social and political systems have created different bases in the administration of city affairs. These developments have caused concerning events such as environmental development, concepts such as green city or clean city; regarding the development and growth of technology, concepts such as electronic city, virtual city, digital city, information city, and concerning human factors, concepts such as knowledge city, learning city, creative city, should be brought up and brought to the attention of researchers, experts, and policymakers.
Learning is an indispensable part of life. Learning is a relatively permanent change in a person’s feelings, thinking, and behavior based on experience. Today, cities are places where people live and work and places of leisure, culture, business, and education. According to this, managers and urban policymakers have noticed concepts such as the learning city in recent years.
Methodology
This applied study employed a descriptive-analytical research method and survey research design. At first, related sources were extracted using a documentary method, and according to the communication structure of the sources and the process of the studies, the concepts and variables investigated in the subject literature were determined. Based on the literature and semi-structured, in-depth interviews with 15 scientific and executive urban experts, the factors that affect the realization of the city of learning were extracted in the form of 19 indicators. Then, to check the agreement between the experts’ opinions and determine the agreement, prioritize the indicators of these factors using the Delphi method. A questionnaire and the Likert scale were employed during three rounds, and the relevant indicators were collectively agreed upon. Based on the mean and standard deviation, non-priority indicators were removed, and 13 indicators were agreed upon. The sample size for the Delphi method was ten experts with knowledge and expertise related to the field under study, and it was done using non-probability sampling. In the end, using ISM, leveling and creating a model, and determining the relationship between the factors and analysis were done.
Results and discussion
The current research has been written in the direction of ISM of factors affecting the realization of the learning city in Ahvaz metropolis. For this purpose, in this research, after reviewing the basics and theoretical concepts about learning cities and their components and indicators, the criteria and measurement indicators were developed from the perspective of the learning city approach. Then, based on the measurement framework of the research, ISM was used. This research investigates 13 factors affecting the realization of the learning city in Ahvaz city. In this method, six steps were examined. An interactive matrix using experts’ opinions was formed in the first step. Then, in the second step, the primary achievement matrix was formed by converting the structural self-interaction matrix to zero and one.
In the third step, its internal consistency should be established after the initial acquisition matrix is obtained. For example, if variable 1 leads to variable 2 and variable 2 leads to variable 3, variable 1 should also lead to variable 3, and if this state is not established in the access matrix, the matrix should be modified, and such relationships should be modified and created. Each criterion’s input (prerequisite) and output (achievement) criteria were calculated in the fourth step. Then, the common factors were specified. In this step, the highest level criterion is that the output set (achievement) equals the common set. In the fifth step, the network of ISM interactions is drawn using the levels obtained from the criteria. If there is a relationship between two variables, i and j, we show it with a directed arrow. The research model can also be shown in terms of influence and dependence in the sixth step of the MICMAC analysis model.
Conclusion
The criteria for providing high-speed internet and easy access to educational technologies (C1), creating innovative and suitable educational centers for all types of students and students (C2), developing policies to support education and the development of public education (C8), and supporting related industries and startups with education and technology (C11) are independent variables. These variables have low dependence and high driving power. In other words, high influence and low dependence are the characteristics of these variables. The criteria for using data mining technologies to analyze the strengths and weaknesses of the city’s education system (C3), promote the culture of learning in society (C4), create opportunities for participation in decision-making related to urban education (C7), and develop public transportation systems and the creation of accessible access environments to educational centers (C9) are dependent variables. These variables have strong dependence and weak direction. These variables generally have a high influence and little dependence on the system. The rest of the criteria are of the interface type; these variables have high dependence and driving power. In other words, the influence of these criteria is very high, and any small change in these variables causes fundamental changes in the system.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.
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