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Quantifying success and attempts on auto-graded homework when using an interactive textbook

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2020 ASEE Virtual Annual Conference Content Access


Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Chemical Engineering in the Sophomore Year

Tagged Division

Chemical Engineering

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Matthew W Liberatore The University of Toledo Orcid 16x16

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Matthew W. Liberatore is a Professor 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. His current research involves the rheology of complex fluids as well as active learning, reverse engineering online videos, and interactive textbooks. His website is:

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Megan Davidson The University of Toledo


Kayla Chapman

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Kayla Chapman is currently studying chemical engineering at the University of Toledo and expects to earn a B.S. degree in 2021. She has assisted with multiple areas of research and data analysis regarding zyBooks reading participation and challenge activities. She became interested in performing research after completing a chemical engineering course that used zyBooks.

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Online homework and interactive textbooks provide big data that can help answer many questions about student engagement and learning. In addition, some data is available to instructors in real time, so individual or class-level interventions do not need to wait for the next exam. In this study, a fully interactive online textbook, Material and Energy Balances zyBook, has been employed with several cohorts of students. Recent findings focused on quantify reading across three cohorts representing over 280 students and almost 300,000 reading interactions. Median reading rates were as high as 99% for over 1,300 interactions per student were found, but these reading clicks only quantify effort and not competence in the course material. Now, auto-graded homework questions with randomized numbers and content are explored. Students are allowed to attempt any question as many times as needed with each new attempt containing new numbers, content, or both. Three different, question-level metrics will be explored, namely percent correct, number of attempts before answering correctly, and total attempts. With over 500 auto-graded questions across 9 chapters, almost 200,000 student attempts are analyzed by chapter, question type, and cohort. Overall, students were successful on 88% of the questions; success varied by 10% between multiple choice, single numerical answer, and multiple numerical answer.

Liberatore, M. W., & Davidson, M., & Chapman, K. (2020, June), Quantifying success and attempts on auto-graded homework when using an interactive textbook Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35116

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