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A Quantitative Study of Collaboration Patterns of Engineering Education Researchers

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Collection

2012 ASEE Annual Conference & Exposition

Location

San Antonio, Texas

Publication Date

June 10, 2012

Start Date

June 10, 2012

End Date

June 13, 2012

ISSN

2153-5965

Conference Session

Research in Engineering Education I

Tagged Division

Educational Research and Methods

Page Count

13

Page Numbers

25.96.1 - 25.96.13

Permanent URL

https://peer.asee.org/20856

Download Count

19

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Paper Authors

biography

Hanjun Xian Purdue University, West Lafayette

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Hanjun Xian is a Ph. D. student in the School of Engineering Education at Purdue University. He holds a master's degree and a bachelor's degree in computer science and started to pursue his Ph.D. degree in engineering education in 2009. He is working with Dr. Madhavan to implement the iKNEER web portal to allow intuitive navigation of the knowledge products of engineering education research. His major roles in this project are to retrieve, mine, and manage knowledge products; provide multiple visualization tools to represent the large problem space in engineering education research; and analyze and predict the shift of research focuses and collaboration.

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Krishna Madhavan Purdue University, West Lafayette

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Abstract

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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