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Embedded Tagging and Radar Map Shape Analysis for Assessing Student Outcomes

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Technical Session 2: Embedded Systems

Tagged Division

Computers in Education

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Paper Authors


Gabriel R. Burks University of Illinois, Urbana-Champaign Orcid 16x16

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Gabriel Burks is a postdoctoral research associate at University of Illinois Urbana-Champaign. He is from Louisiana where he also completed his Baccalaureate Degree in Physics with a minor in Chemistry from Grambling State University. Dr. Burks received his doctoral degree from Drexel Unviersity in Materials Science & Engineering and was also a National Science Foundation GK-12 STEM Fellow for 3 years, in the School District of Philadelphia. Primary research interests are: 1) synthesis and characterization of functional polymeric materials, 2) polymer crystallization phenomena, and 3) pedagogical assessment of conventional and nontraditional STEM spaces.

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Jennifer R. Amos University of Illinois, Urbana-Champaign

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Dr Amos joined the Bioengineering Department at the University of Illinois in 2009 and is currently a Teaching Associate Professor in Bioengineering and an Adjunct Associate Professor in Educational Psychology. She received her B.S. in Chemical Engineering at Texas Tech and Ph.D. in Chemical Engineering from University of South Carolina. She completed a Fulbright Program at Ecole Centrale de Lille in France to benchmark and help create a new hybrid masters program combining medicine and engineering and also has led multiple curricular initiative in Bioengineering and the College of Engineering on several NSF funded projects.

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Colin Widmer Castleberry University of Illinois, Urbana-Champaign

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Computer-based assessment has been shown to offer many benefits on the outcomes of student performance [1]. The computational strengths of computer-based platforms allow for more in-depth collection and analysis of data from students of today than the many years previous. This access to data and performance outcomes allow us to learn more about the individual student instantly and to use this feedback to tailor adapted teaching and learning experiences on the fly. Here the University of Illinois Urbana-Champaign-developed PrairieLearn (PLN) platform is used to administer, assess, and collect minimally invasive tagged data from quizzes, homework, and exams of undergraduate students in sophomore-level engineering course. The information gathered from this platform is represented using an accessible radar plot format then analyzed using a novel method, based on the shape of the radar plot, to develop a greater understanding of the individual strengths and deficiencies of students. Finally, to establish appropriate context for this data it is correlated to a common student success metric and analyzed for potential trends.

Burks, G. R., & Amos, J. R., & Castleberry, C. W. (2019, June), Embedded Tagging and Radar Map Shape Analysis for Assessing Student Outcomes Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32702

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