Unleashing the Power of Data Analytics to Examine Engineering Students’ Experiences and Outcomes
- Conference
- Location
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Virtual On line
- Publication Date
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June 22, 2020
- Start Date
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June 22, 2020
- End Date
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June 26, 2021
- Conference Session
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Data-informed Approaches to Understanding Student Experiences and Outcomes
- Tagged Division
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Educational Research and Methods
- Page Count
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16
- DOI
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10.18260/1-2--35431
- Permanent URL
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https://peer.asee.org/35431
- Download Count
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530
Paper Authors
Qin Liu University of Toronto
Dr. Qin Liu is a senior research associate at the Institute for Studies in Transdisciplinary Engineering Education and Practice, Faculty of Applied Science & Engineering, University of Toronto. Her research interests include learning experiences and outcomes assessment in postsecondary education, research methodologies and data analytics in engineering education.
Greg Evans
University of Toronto
orcid.org/0000-0002-9641-4499
GREG EVANS PhD, P.Eng, FCEA, FAAAS is the Director of the Institute for Studies in Transdisciplinary Engineering Education and Practice (ISTEP), Director of the Collaborative Specialization in Engineering Education, a 3M national Teaching Fellow, and a member of the University of Toronto President’s Teaching Academy. He has been learning and teaching Chemical Engineering for several decades as a Professor in the Department of Chemical Engineering and Applied Chemistry at the University of Toronto. His contributions to teaching have been recognised through the 2015 Ontario Confederation of University Faculty Associations Award, the 2014 Allan Blizzard Award for collaborative teaching, the 2013 Northrop Frye Award for integrating research and teaching, the 2010 Engineers Canada Medal for Distinction in Engineering Education. Greg is also the Director of the Southern Ontario Centre for Atmospheric Aerosol Research whose research on air pollution been recognised both nationally and internationally.
Abstract
In this theory paper, we integrate literature from different fields. We argue that efforts to expand engineering education research through data analytics need to be grounded in the established literature and understanding of student development. We discuss the opportunities and challenges associated with using data analytics to examine engineering students’ experiences and outcomes. We suggest that engineering schools should enhance data infrastructure, along with data governance policies, to foster a culture of collaboration among units and divisions, and better utilize existing student data sources through greater data integration. We also suggest that engineering education researchers equip themselves with knowledge on data science, in addition to knowledge about different types of student experiences, and actively explore a wider range of data sources for research. Thereby, we envision a new research landscape with expanded data sources, integrated data systems, and new analytical techniques to enable predictive analysis and more actionable findings.
Liu, Q., & Evans, G. (2020, June), Unleashing the Power of Data Analytics to Examine Engineering Students’ Experiences and Outcomes Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35431
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