Effects of problem type on completion and attempts on auto-graded homework problems for Material and Energy Balances

Samantha Yanosko; Grant Valentine; Matthew W Liberatore

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Improving Student Problem Solving and Performance
Tagged Division: Chemical Engineering Division (ChED)
Permanent URL: https://peer.asee.org/47227

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Grant Valentine is an undergraduate student studying chemical engineering at the University of Toledo expecting to graduate in 2025.

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biography

Matthew W Liberatore University of Toledo orcid.org/0000-0002-5495-7145

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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

Auto-graded online homework and interactive textbooks engage students and generate big data. Several new research questions investigate students’ usage of and success on over 700 auto-graded questions within an interactive tool titled the Material and Energy Balances zyBook. Auto-grading occurs in real time, so students, teaching assistants, and faculty can see progress without waiting for assignments to be graded. Previous research examined reading participation and auto-graded problems at the course level; Findings included median reading participation over 93% for seven cohorts and median correct on auto-graded problems of 91% or higher for six cohorts. More specifically, auto-graded problems allowed unlimited attempts, so students received feedback and persisted until correct for these randomized problems. Here, two recent cohorts’ responses on hundreds of auto-graded questions examined specific types of auto-graded problems. From one perspective, formative, single calculation problems with scaffolding appeared in most sections, while more summative, multi-concept problems appeared at the end of each chapter. From another perspective, many problems required numerical answers within a tolerance, and other problems were multiple choice. Our research questions examined these different types and locations of auto-graded problems. New findings showed that median percent correct was high (above 80%) for all problem types. Attempts before correct provided a valuable metric to distinguish between problem types with numeric problems taking more attempts than multiple choice. Finally, a metric combining both correct and attempts, called the deliberate practice score, provided another quantitative aggregate measure. Of note, end-of-chapter numeric response problems had a much larger fraction of problems at higher deliberate practice scores than in-chapter, numeric questions.

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Yanosko, S., & Valentine, G., & Liberatore, M. W. (2024, June), Effects of problem type on completion and attempts on auto-graded homework problems for Material and Energy Balances Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47227