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Engineering Education and Quantified Self: Utilizing a Student-Centered Learning Analytics Tool to Improve Student Success

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

ERM Technical Session 6: Technology-enhanced Instruction and Assessment

Tagged Division

Educational Research and Methods

Tagged Topic

Diversity

Page Count

16

DOI

10.18260/1-2--32723

Permanent URL

https://peer.asee.org/32723

Download Count

134

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Paper Authors

biography

Brandon Xavier Karcher Bucknell University

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Brandon is a Digital Pedagogy & Scholarship Specialist at Bucknell University. His work centers around instructional design, educational technology, and pedagogy. Current interests are reflective learning, student-centered design, and learning analytics. He received his B.S. at Southeast Missouri State in Graphics and Multimedia and an M.S. in Computer Graphics Technology at Purdue University.

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biography

Beth M. Holloway Purdue University, West Lafayette

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Beth Holloway is the Assistant Dean for Diversity and Engagement and the Leah H. Jamieson Director of Women in Engineering (WIEP) in the College of Engineering at Purdue University. She is also an Assistant Professor of Mechanical Engineering, by courtesy. Holloway received B.S. and M.S. degrees in Mechanical Engineering and a Ph.D. in Engineering Education, all from Purdue University.

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biography

Brittany Pierson Purdue University, West Lafayette

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Brittany Pierson is an assessment and data analyst at Purdue University in the Office of Institutional Research, Assessment and Effectiveness. She received a bachelor's degree in industrial engineering from Purdue University and is pursuing her master's in industrial engineering.

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Abstract

This evidence-based practice paper assessed the implementation of a quantified-self learning analytics tool, called Pattern, and how it impacted study behaviors across multiple sections of engineering courses at Purdue University. The goals of the implementation of Pattern and subsequent research was to explore: (a) student study activities that correlated with success, (b) student study behavior change from exam-to-exam, and (c) whether the use of Pattern impacted study habits. Results indicated that simply studying longer does not correlate with success and that students spend the most amount of time doing activities they rate the lowest in effectiveness (e.g., reading). Additionally, while students do make behavioral changes from exam-to-exam, those changes are only moderate in size and scope. Gender differences were also found to be significant in how students studied. Based on the results of this study, recommendations for instructors are to 1) use technology that is familiar and facilitates peer comparison, 2) conduct analysis of recommended study strategies to assess effectiveness, and 3) stress to students that how they study is much more important than how long they study.

Karcher, B. X., & Holloway, B. M., & Pierson, B. (2019, June), Engineering Education and Quantified Self: Utilizing a Student-Centered Learning Analytics Tool to Improve Student Success Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32723

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