June 14, 2015
June 14, 2015
June 17, 2015
Computers in Education
26.964.1 - 26.964.16
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: data.gov, 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 http://books.nap.edu/openbook.php?record_id=11338&page=17.
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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