Board 80: Integrating Adaptive Learning Lessons in a Flipped STEM Course: Development, Learning Gains, and Data Analytics

Autar Kaw; Renee M. Clark; Eleonora Emma Delgado; Nicholas Abate

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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: NSF Grantees Poster Session
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 7
DOI: 10.18260/1-2--32434
Permanent URL: https://peer.asee.org/32434
Download Count: 533

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Paper Authors

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Autar Kaw University of South Florida

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Autar Kaw is a professor of Mechanical Engineering at the University of South Florida. He is a recipient of the 2012 U.S. Professor of the Year Award (doctoral and research universities) from the Council for Advancement and Support of Education and the Carnegie Foundation for Advancement of Teaching. Professor Kaw’s main scholarly interests are in engineering education research, adaptive, blended and flipped learning, open courseware development, and the state and future of higher education. Funded by National Science Foundation, under Professor Kaw's leadership, he and his colleagues from around the nation have developed, implemented, refined, and assessed online resources for an open courseware in Numerical Methods. This courseware annually receives 1,000,000+ page views, 2,000,000+ views of the YouTube lectures, and 90,000+ visitors to the "numerical methods guy" blog. He has written more than 100 refereed technical papers and his opinion editorials have appeared in the Tampa Bay Times, Tampa Tribune and Chronicle Vitae. His work has been covered/cited/quoted in many media outlets including Chronicle of Higher Education, Inside Higher Education, U.S. Congressional Record, Florida Senate Resolution, ASEE Prism, and Voice of America.

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Renee M. Clark University of Pittsburgh

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Renee M. Clark is a research assistant professor of Industrial Engineering and Director of Assessment in the Swanson School of Engineering and the Engineering Education Research Center (EERC). She received her MS in Mechanical Engineering from Case Western and her PhD in Industrial Engineering from the University of Pittsburgh while working for Delphi Automotive. Her research interests focus on the propagation and assessment of active and experiential learning in engineering education.

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Eleonora Emma Delgado University of South Florida

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Eleonora Delgado is a master's student in the Department of Mechanical Engineering at the University of South Florida. She graduated magna cum laude from the University of South Florida with a B.S. in Mechanical Engineering. Her areas of interest include vibrations and machine design. As an undergraduate, she codeveloped adaptive lessons for a course in Numerical Methods and cofacilitated a workshop on the use of adaptive learning in flipped classrooms.

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Nicholas Abate University of South Florida

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Nicholas Abate attended the University of South Florida where he obtained a Bachelor’s degree in Mechanical Engineering, graduating Summa Cum Laude. During his time at USF, he was an active member of the Society of Aeronautics and Rocketry, as well as the Society of Automotive Engineers. In his final year of academia, Nicholas worked with Professor Kaw as an undergraduate research and learning assistant for a numerical methods course to study the effectiveness of flipped classrooms with adaptive learning.

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Abstract

One of the major challenges in a successful flipped classroom is ensuring pre-class preparation by all students. Currently, in most cases, such pre-class preparation involves the assignment of a few short videos, textbook content, and a quiz that may be given online or in-class. However, this one-size-fits-all approach does not account for differential needs and may not be motivating enough for all students. To improve the flipped classroom in a Numerical Methods course, with funding through an exploratory NSF IUSE development grant, we prepared 17 adaptive lessons for half of the topics in the course. The lessons were developed using the popular adaptive learning platform (ALP) of Smart Sparrow. This ALP allowed combining the elements of videos, textbook content, simulations, and quizzes. The quizzes consisting of multiple-choice, fill-in-the-blank, and algorithmic questions provided students with immediate feedback on how they were doing and directed them along personalized paths based on how they responded to the questions on the quiz.

In this paper, we discuss how the adaptive lessons were developed for the course, the metrics collected through the ALP, and their usefulness and interpretation. The student-level ALP metrics included the number of attempts to complete a lesson, the raw score (based on all attempts made), the lesson score (based on the maximum score for the various attempts), the time spent on a lesson, and the number of hours before the deadline that a lesson was completed. Lesson-level metrics included the percentage of students who completed the lesson and percentage of adaptive feedback in use. This latter percentage was based on the number of custom states or states with adaptive feedback that were triggered and seen by at least one student.

In assessing the relationship between exam performance and ALP use, the correlation between the final examination results and most of the lesson metrics were not sizable or statistically significant. For example, there was almost no relationship between the final examination score and the total hours spent on the lesson (r = -0.003). Most students also received high lesson scores since they could pursue multiple attempts. We hence sought what differentiates the lesser-performing from the better-performing students. This differentiation was evident in the relationship between the final examination score and the raw score, where we measured a correlation of r=0.35 with p<0.0005. This demonstrated that students who answered the questions correctly in the initial attempt achieved better performance on the final examination, suggesting that stronger preparation or due diligence leads to better exam performance. Based on these results, we believe that the study should be extended to gain additional insights by developing adaptive lessons for the whole course and implementing and assessing them at multiple universities.

Citation
Format

Kaw, A., & Clark, R. M., & Delgado, E. E., & Abate, N. (2019, June), Board 80: Integrating Adaptive Learning Lessons in a Flipped STEM Course: Development, Learning Gains, and Data Analytics Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32434

TY - CPAPER
AB - One of the major challenges in a successful flipped classroom is ensuring pre-class preparation by all students. Currently, in most cases, such pre-class preparation involves the assignment of a few short videos, textbook content, and a quiz that may be given online or in-class. However, this one-size-fits-all approach does not account for differential needs and may not be motivating enough for all students.

To improve the flipped classroom in a Numerical Methods course, with funding through an exploratory NSF IUSE development grant, we prepared 17 adaptive lessons for half of the topics in the course. The lessons were developed using the popular adaptive learning platform (ALP) of Smart Sparrow. This ALP allowed combining the elements of videos, textbook content, simulations, and quizzes. The quizzes consisting of multiple-choice, fill-in-the-blank, and algorithmic questions provided students with immediate feedback on how they were doing and directed them along personalized paths based on how they responded to the questions on the quiz.

In this paper, we discuss how the adaptive lessons were developed for the course, the metrics collected through the ALP, and their usefulness and interpretation. The student-level ALP metrics included the number of attempts to complete a lesson, the raw score (based on all attempts made), the lesson score (based on the maximum score for the various attempts), the time spent on a lesson, and the number of hours before the deadline that a lesson was completed. Lesson-level metrics included the percentage of students who completed the lesson and percentage of adaptive feedback in use. This latter percentage was based on the number of custom states or states with adaptive feedback that were triggered and seen by at least one student.

In assessing the relationship between exam performance and ALP use, the correlation between the final examination results and most of the lesson metrics were not sizable or statistically significant. For example, there was almost no relationship between the final examination score and the total hours spent on the lesson (r = -0.003). Most students also received high lesson scores since they could pursue multiple attempts. We hence sought what differentiates the lesser-performing from the better-performing students. This differentiation was evident in the relationship between the final examination score and the raw score, where we measured a correlation of r=0.35 with p&lt;0.0005. This demonstrated that students who answered the questions correctly in the initial attempt achieved better performance on the final examination, suggesting that stronger preparation or due diligence leads to better exam performance. Based on these results, we believe that the study should be extended to gain additional insights by developing adaptive lessons for the whole course and implementing and assessing them at multiple universities.
AU - Autar Kaw
AU - Renee M. Clark
AU - Eleonora Emma Delgado
AU - Nicholas Abate
CY - Tampa, Florida
DA - 2019/06/15
PB - ASEE Conferences
TI - Board 80: Integrating Adaptive Learning Lessons in a Flipped STEM Course: Development, Learning Gains, and Data Analytics
UR - https://peer.asee.org/32434
DO - 10.18260/1-2--32434
ER -