Animation Analytics in an Interactive Textbook for Material and Energy Balances

Sidney Stone; Breanne Crockett; Kevin Xu; Matthew Liberatore

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: Pedagogy in Chemical Engineering Education
Page Count: 18
DOI: 10.18260/1-2--41361
Permanent URL: https://peer.asee.org/41361
Download Count: 312

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Kevin S. Xu received the B.A.Sc. degree from the University of Waterloo in 2007 and the M.S.E. and Ph.D. degrees from the University of Michigan in 2009 and 2012, respectively. He is a recipient of the NSF CAREER award, and his research has been supported by several NSF and NIH grants. He is currently an assistant professor in the Electrical Engineering and Computer Science Department at the University of Toledo where he leads the Interdisciplinary Data Engineering and Science (IDEAS) Laboratory. Prior to that, he held industry research positions at Technicolor and 3M. His main research interests are in machine learning and network science with applications to human dynamics, health care, education, and wearable computing.

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Matthew Liberatore The University of Toledo

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Matthew W. Liberatore is a Professor in the Department of Chemical Engineering at the University of Toledo. He earned a B.S. degree from the University of Illinois at Chicago and M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign, all in chemical engineering. From 2005 to 2015, he served on the faculty at the Colorado School of Mines. In 2018, he served as an Erskine Fellow at the University of Canterbury in New Zealand. His research involves the rheology of complex fluids, especially traditional and renewable energy fluids and materials, polymers, and colloids. His educational interests include developing problems from YouTube videos, active learning, learning analytics, and interactive textbooks. His interactive textbooks for Material and Energy Balances, Spreadsheets, and Thermodynamics are available from zyBooks.com. His website is: https://www.utoledo.edu/engineering/chemical-engineering/liberatore/

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Abstract

Interactive textbooks generate big data through student reading participation, including animations, question sets, and auto-graded homework. Animations are multi-step, dynamic visuals with text captions. By dividing new content into smaller chunks of information, student engagement is expected to be high, which aligns with tenets of cognitive load theory. Specifically, students’ clicks are recorded and measure usage, completion, and view time per step and for entire animations. Animation usage data from an interactive textbook for a chemical engineering course in Material and Energy Balances accounts for 60,000 animation views across 140+ unique animations. Data collected across five cohorts between 2016 and 2020 used various metrics to capture animation usage including watch and re-watch rates as well as the length of animation views. Variations in view rate and time were examined across content, parsed by book chapter, and five animation characterizations (Concept, Derivation, Figures and Plots, Physical World, and Spreadsheets). Important findings include: 1) Animation views were at or above 100% for all chapters and cohorts, 2) Median view time varies from 22 s (2-step) to 59 s (6-step) - a reasonable attention span for students and cognitive load, 3) Median view time for animations characterized as Derivation was the longest (40 s) compared to Physical World animations, which resulted in the shortest time (20 s).

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Stone, S., & Crockett, B., & Xu, K., & Liberatore, M. (2022, August), Animation Analytics in an Interactive Textbook for Material and Energy Balances Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41361