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Informing the Sharing and Access of Engineering Education Research Data through Comparative Analysis

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2015 ASEE Annual Conference & Exposition


Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015





Conference Session

Data Analysis and Assessment

Tagged Division

Computers in Education

Page Count


Page Numbers

26.964.1 - 26.964.16



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Paper Authors


Elaheh Molla Allameh Purdue University

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Elaheh Molla Allameh received a BSc in Public Management from Shahid Beheshti University in 2011. Currently, she is perusing an MSc in Technology, Leadership and Innovation at Purdue University. Her research interests are centered in online communication, data sharing and social media analysis.

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Mihaela Vorvoreanu Purdue University, West Lafayette


Seungwon Yang George Mason University

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Seungwon Yang is a Postdoctoral Associate in the Department of Applied Information Technology at George Mason University. His current research interests include applying data mining techniques to learning-related data. More specifically, his work examines the informal learning occurring in an online community which supports its users to create and share content. His other academic interests include designing and developing visual analytics tools, developing large archives of disaster events, and visualizing information. He enjoys hiking and practicing Zen in his free time.

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Aditya Johri George Mason University Orcid 16x16

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Aditya Johri is Associate Professor and Chair in the Applied Information Technology Department. Dr. Johri studies the use of information and communication technologies (ICT) for learning and knowledge sharing, with a focus on cognition in informal environments. He also examine the role of ICT in supporting distributed work among globally dispersed workers and in furthering social development in emerging economies. He received the U.S. National Science Foundation’s Early Career Award in 2009. He is co-editor of the Cambridge Handbook of Engineering Education Research (CHEER) published by Cambridge University Press, New York, NY. Dr. Johri earned his Ph.D. in Learning Sciences and Technology Design at Stanford University and a B.Eng. in Mechanical Engineering at Delhi College of Engineering.

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Krishna Madhavan Purdue University, West Lafayette

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Dr. Krishna Madhavan is an Assistant Professor in the School of Engineering Education at Purdue University. He is Co-PI and Education Director of the NSF-funded Network for Computational Nanotechnology ( which serves over 330,000 global researchers and learners annually). Dr. Madhavan was the Chair of the IEEE/ACM Supercomputing Education Program 2006. In January 2008, he was awarded the US National Science Foundation (NSF) CAREER award for work on learner-centric, adaptive cyber-tools and cyber-environments. He was one of 49 faculty members selected as the nation’s top engineering educators and researchers by the US National Academy of Engineering to the Frontiers in Engineering Education symposium. Dr. Madhavan leads a major NSF funded effort called Deep Insights Anytime, Anywhere (DIA2) that attempts to characterize the impact of NSF and other federal investments in the area of science, technology, engineering, and mathematics education using interactive knowledge mining and visual analytics for non-experts in data mining. DIA2 is currently deployed inside the NSF and is already starting to affect federal funding policy. Dr. Madhavan also served as Visiting Research Scientist at Microsoft Research, Internet Services Research Group. His research has been published in Nature Nanotechnology, IEEE Transactions on Computer Graphics and Applications, IEEE Transactions on Learning Technologies, and several other top peer-reviewed venues. Dr. Madhavan currently serves as PI or Co-PI on federal and industry funded projects totaling over $20M.

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Informing the Sharing and Access of Engineering Education Research Data through Comparative AnalysisThe rapid growth of engineering education as a field of rigorous research has resulted inan explosion of available data and research results. There are numerous research effortscurrently underway that gather data on a variety of topics that have the potential to helpus better understand how students learn engineering. However, there are currently noeasy methods to synthesize research results, share research data, and indeed validateresearch studies effectively. In general, topics related to data and data sharing are largelytreated as taboos in the engineering education research space. Data sharing mechanismsto enable fundamental research in engineering education that has the potential to addresssystemic problems have not yet been clarified. The research goal of this paper is toidentify and understand patterns for data sharing mechanisms in order to inform designrequirements for data sharing practices and infrastructure in engineering education.Unlike the physical sciences, research data in engineering education is often qualitative innature and deeply linked to individual characteristics, psychographics and demographics.As such, it can be harder to de-identify, and harder to store and disseminate in shareddatabases, because it lacks the structure of quantitative data sets. These particularchallenges are possible reasons why there is very little, if any, sharing of research data inengineering education.Data sharing in engineering education is important because it can enable researchers toscaffold their findings, to triangulate their research with other data sets – in other words,it can enable researchers to scale up their research efforts and to result in greater impactson how students learn engineering. Existing literature in engineering educationestablishes the urgent need for infrastructure that scaffolds the field. The current draftreport of the project Engineering education for the global economy: Research,innovation, and practice (ASEE, 2008) identifies the need to develop an infrastructurethat supports engineering education at the local and national scales. Furthermore, thereport Educating the engineer of 2020: Adapting engineering education to the newcentury released by the National Academy of Engineering states that “our goal to ensureeffective engineering education should be pursued within the context of a comprehensiveexamination of all relevant aspects of the interrelated system of systems of engineeringeducation, engineering practice, the K-12 feeder system, and the global economicsystem” (NAE, 2005).One first step towards establishing data sharing practices in engineering education is tolook at similar fields and their public data repositories. In this study, we examine anumber of successful data repositories and compare their features and mechanisms forsharing social science and qualitative data similar to that likely to be generated inengineering education research. We identify patterns for sharing of these data that can beused to inform data sharing practices in engineering education. Specifically, we examinein depth the following data repositories:, Inter-university Consortium forPolitical and Social Research (ICPRS), National Center for Educational Statistics(NCES), Qualitative Data Repository (QDR), and UK Data Service. These have beenidentified as successful data repositories that host data similar to those generated inengineering education research.ReferencesASEE. (2008). Engineering Education for the Global Economy: Research, Innovation, and Practice (Draft Report).National Academy of Engineering (NAE). (2005). Educating the engineer of 2020: Adapting engineering education to the new century. Available online at

Molla Allameh, E., & Vorvoreanu, M., & Yang, S., & Johri, A., & Madhavan, K. (2015, June), Informing the Sharing and Access of Engineering Education Research Data through Comparative Analysis Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24301

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