Premium
Anatomy of Scholarly Collaboration in Engineering Education: A Big‐Data Bibliometric Analysis
Author(s) -
Xian Hanjun,
Madhavan Krishna
Publication year - 2014
Publication title -
journal of engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.896
H-Index - 108
eISSN - 2168-9830
pISSN - 1069-4730
DOI - 10.1002/jee.20052
Subject(s) - bibliometrics , social network analysis , ideal (ethics) , discipline , network topology , bridging (networking) , engineering education , value (mathematics) , sociology , computer science , topology (electrical circuits) , engineering ethics , engineering , social science , political science , engineering management , library science , social capital , computer network , electrical engineering , machine learning , law , operating system
Abstract Background Engineering education has become a large community with an increasing number of scholars and publications. As the number of publications has grown, it has become increasingly difficult to understand the epistemic nature and diffusion characteristics of knowledge generated by this community. Techniques to study community topology require nontrivial computational workflows. Purpose/Hypothesis The present study characterizes the topology of scholarly collaboration and important factors affecting this topology in engineering education research. Design/Methods A bibliometric analysis was conducted of 24,172 papers in engineering education research journals and conference proceedings for the years 2000–2011. A total of 29,116 unique authors are present. Social network analyses were used to characterize the network topology of overall scientific collaboration. Analyses based on grouping scholars by disciplinary backgrounds, research areas, and geographical locations were performed. Results The results show that the engineering education research community is in its early stage of forming a small‐world network that relies primarily on 5% of scholars to build capacity. Typical small‐world networks provide some very clear characterizations about the state, stability, and growth of the community. Deviations from this ideal model suggest the need for rethinking collaboration in engineering education. Scholars with interdisciplinary backgrounds play a critical role in bridging isolated research teams. Conclusions Compared with other disciplines and the ideal small‐world network model, the topology of collaboration in engineering education shows significant barriers to the fast diffusion of innovations. This study demonstrates the value of big‐data bibliometrics in understanding scholarly collaboration within a research community.