نوع مقاله : مقاله پژوهشی
1 دانشجوی دکتری مدیریت آموزش عالی، دانشگاه شهید بهشتی
2 دانشیار گروه رهبری و توسعه آموزشی ،دانشگاه شهید بهشتی
3 استاد گروه علم و جامعه، مرکز تحقیقات سیاست علمی کشور
عنوان مقاله [English]
Purpose: The present research was conducted to study the co-authorship network of faculty members in the field of education in public universities of Tehran and reviewing their authorship patterns in 199 articles published in non-Iranian journals and indexed in Web of Science and Scopus databases up to March 2017.
Methodology: This study was conducted using a scientometric approach and social network analysis. Configuration of the co-authorship network for the publication of international papers was investigated using macro indicators of network analysis such as density, clustering coefficient, mean distance, connectedness and diameter of the network and for analyzing the performance of each faculty member with a compilation in the network, the micro indicators of the analysis of networks such as degree centrality, betweenness, closeness, and Eigen vector were utilized. To analyze and draw information obtained from the network, UCINet software and its complementary package NetDrew were used.
Findings: The pattern of three authorship has been the most important collaborative pattern in the articles studied (%30). The network is composed of 106 nodes and 41 ties. The study of micro indicators showed that individuals such as Abbas Abbaspour, Khosrow Bagheri , Ali Delavar, Hasan Maleki and Ismail Zarei With the highest degree of scientific collaboration with other writers, they are the most participant in the network. The analysis of macro indicators also showed that the density of the mentioned network was 0.05, so the network did not have sufficient Cohesion.
Conclusion: Micro-level indicators showed that Abbas Abbaspour, Khosro Bagheri, Ali Delavar, Hasan Maleki and Esmaeil Zarei are the most contributable people in the network as they have the highest level of collaboration. The network is density was 0.5; therefore, it is a network with low cohesion.