San Antonio, Texas
June 10, 2012
June 10, 2012
June 13, 2012
Educational Research and Methods
25.96.1 - 25.96.13
A quantitative study of collaboration patterns of engineering education researchersAcademic collaboration in engineering education has been known as a critical factor inimproving research quality and increasing researchers’ satisfaction. However,engineering education scholars have still been identified as working in isolation based ontheir academic publication pattern. Prior studies have revealed primary strategiesresearchers used to identify collaborators and their behavioral changes aftercollaboration. However, few efforts have investigated why some scholars tend tocollaborate and choose the collaborators. There are many factors that influence aresearcher's collaboration decisions such as fields of study, awareness of other academicwork, levels of competition, perceived usefulness of collaboration, and work ethics.Among these possible factors, fields of study have been recognized as the mostsignificant characteristic in determining researchers’ collaboration decisions. In thisstudy, we focus on how fields of study in engineering education influence researchers’decisions on whether and who to collaborate. Our study will answer the followingresearch questions: 1. Do certain research topics in engineering education tend to encourage/discourage collaboration? 2. Who do researchers in various fields of study tend to collaborate with and what are the academic attributes of these collaborators?In this study, we narrow the definition of collaboration to inter-individual collaborationbetween engineering education researchers funded by NSF. As a classic proxy measurefor academic collaboration, co-authorship is used to quantify a researcher's degree ofcollaboration. A researcher's field of study is characterized by key terms extracted fromfunded NSF grant proposals using a key-phrase extraction algorithm called GenEx.We first collect in total 6,815 NSF grant proposals in the Division of EngineeringEducation and Centers (EEC) and the Division of Undergraduate Education (DUE) from2000 to current. We build a distributed implementation of text-mining algorithms such asGenEx to extract key-phrases and keywords from each proposal abstract. Fields of studyof a researcher who has received an award will be characterized by the key terms fromhis/her proposals. Using large-scale social network analysis, we then identify how fieldsof study influence the collaboration patterns among engineering education researchers.Our preliminary results show that engineering education researchers studying retentionhave the highest tendency to collaborate. We also find that the average number ofcollaborators is 3.51 per researcher for EEC and DUE, which is significantly higher thanthe average (2.54) of the whole Engineering (ENG) directorate in NSF. We discuss otherinsights derived from the data in our full paper.Our research results will provide comprehensive and insightful understandings ofcollaboration patterns for the engineering education research community based on thefunding information available from NSF. It helps engineering education scholars identifypotential collaborators and also benefits the research community by offering guidelines topromote collaboration in certain areas in engineering education.
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