Development, Implementation, Refining and Revising of Adaptive Platform Lessons for an Engineering Course

Autar Kaw; Ali Yalcin; Rafael Braga Gomes; Luis Serrano; Yingyan Lou; Andrew Scott; Renee Clark

Download Paper | Permalink

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: Computers in Education 6 - Best of CoED
Page Count: 14
DOI: 10.18260/1-2--40477
Permanent URL: https://peer.asee.org/40477
Download Count: 237

Request a correction

Paper Authors

biography

Autar Kaw University of South Florida

visit author page

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 Carnegie Foundation for Advancement of Teaching. His primary scholarly interests are in engineering education research, adaptive, blended, and flipped learning, open courseware development, composite materials mechanics, and higher education's state and future. His work in these areas has been funded by the National Science Foundation, Air Force Office of Scientific Research, Florida Department of Transportation, and Wright Patterson Air Force Base. Funded by National Science Foundation, under his leadership, he and his colleagues from around the nation have developed, implemented, refined, and assessed online resources for open courseware in Numerical Methods (http://nm.MathForCollege.com). 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. This body of work has also been used to measure the impact of the flipped, blended, and adaptive settings on how well engineering students learn content, develop group-work skills and perceive their learning environment. He has written more than 150 refereed technical papers, and his opinion editorials have appeared in the Tampa Bay Times, the Tampa Tribune, and the Chronicle Vitae.

visit author page

biography

Ali Yalcin University of South Florida

visit author page

Ali Yalcin, is an Associate Professor of Industrial and Management Systems in the College of Engineering at the University of South Florida. He is the co-founder of Collaborative for Research & Education in Aging and Technology. Previously he was part of the leadership team who founded the Patel College of Global Sustainability at USF. His research interests include Data Analytics, Ambient Intelligence, Internet of Things, Time-series Data Mining and Analytics Applications in Healthcare. His research has been funded by federal and state agencies, and private industry. He has taught courses in the areas of systems modeling, analysis and simulation, information systems, predictive analytics and dynamic systems. He also co-authored, Design of Industrial Information Systems, by Elsevier named the Joint Publishers textbook of the year.

visit author page

biography

Rafael Braga Gomes University of South Florida

visit author page

Rafael Braga Gomes recently received a B.S. degree in Mechanical Engineering from the University of South Florida (USF). He currently works as a Project Engineer focused on HVAC design for new constructions and renovations. Before transferring to USF, he was recognized as one of the top 30 graduates of 2018 by the Florida College System and honored as part of the All-Florida Academic Team. While at USF, Rafael conducted research in melting mechanisms and personalized learning. He was also a teaching assistant for a course in Numerical Methods.

visit author page

author page

Professor of EE, AAMU

visit author page

biography

Renee Clark University of Pittsburgh

visit author page

Renee Clark is Research Assistant Professor of Industrial Engineering and Director of Assessment for the Engineering Education Research Center (EERC) in the Swanson School of Engineering at the University of Pittsburgh. She conducts education research that focuses on active learning and engineering professional development. Renee's current research includes the use of adaptive learning and systematic reflection in the mechanical engineering flipped classroom to drive pre-class preparation and metacognitive development, respectively. She received the Ph.D. in Industrial Engineering from the University of Pittsburgh and the MS in Mechanical Engineering from Case Western. She has 30 years of experience as an engineer, IT analyst, and researcher in industry and academia. She completed her post-doctoral studies in engineering education at the University of Pittsburgh.

visit author page

Download Paper | Permalink

Abstract

Since the 2014 high-profile meta-analysis of undergraduate STEM courses, active learning has become a standard in higher education pedagogy. One way to provide active learning is through the flipped classroom. However, finding suitable pre-class learning activities to improve student preparation and the subsequent classroom environment, including student engagement, can present a challenge in the flipped modality. To address this challenge, adaptive learning lessons were developed for pre-class learning for a course in Numerical Methods. The lessons would then be used as part of a study to determine their cognitive and affective impacts. Before the study could be started, it involved constructing well-thought-out adaptive lessons. This paper discusses developing, refining, and revising the adaptive learning platform (ALP) lessons for pre-class learning in a Numerical Methods flipped course.

In a prior pilot study at a large public southeastern university, the first author had developed ALP lessons for the pre-class learning for four (Nonlinear Equations, Matrix Algebra, Regression, Integration) of the eight topics covered in a Numerical Methods course. In the current follow-on study, the first author and two other instructors who teach Numerical Methods, one from a large southwestern urban university and another from an HBCU, collaborated on developing the adaptive lessons for the whole course.

The work began in Fall 2020 by enumerating the various chapters and breaking each one into individual lessons. Each lesson would include five sections (introduction, learning objectives, video lectures, textbook content, assessment). The three instructors met semi-monthly to discuss the content that would form each lesson. The main discussion of the meetings centered on what a student would be expected to learn before coming to class, choosing appropriate content, agreeing on prerequisites, and choosing and making new assessment questions. Lessons were then created by the first author and his student team using a commercially available platform called RealizeIT. The content was tested by learning assistants and instructors. It is important to note that significant, if not all, parts of the content, such as videos and textbook material, were available through previously done work. The new adaptive lessons and the revised existing ones were completed in December 2020.

The adaptive lessons were tested for implementation in Spring 2021 at the first author's university and made 15% of the students' grade calculation. Questions asked by students during office hours, on the LMS discussion board, and via emails while doing the lessons were used to update content, clarify questions, and revise hints offered by the platform. For example, all videos in the ALP lessons were updated to HD quality based on student feedback. In addition, comments from the end-of-semester surveys conducted by an independent assessment analyst were collated to revise the adaptive lessons further. Examples include changing the textbook content format from an embedded PDF file to HTML to improve quality and meet web accessibility standards.

The paper walks the reader through the content of a typical lesson. It also shows the type of data collected by the adaptive learning platform via three examples of student interactions with a single lesson.

Citation
Format

Kaw, A., & Yalcin, A., & Braga Gomes, R., & Serrano, L., & Lou, Y., & Scott, A., & Clark, R. (2022, August), Development, Implementation, Refining and Revising of Adaptive Platform Lessons for an Engineering Course Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40477

TY  - CPAPER
AB  - Since the 2014 high-profile meta-analysis of undergraduate STEM courses, active learning has become a standard in higher education pedagogy. One way to provide active learning is through the flipped classroom. However, finding suitable pre-class learning activities to improve student preparation and the subsequent classroom environment, including student engagement, can present a challenge in the flipped modality. To address this challenge, adaptive learning lessons were developed for pre-class learning for a course in Numerical Methods. The lessons would then be used as part of a study to determine their cognitive and affective impacts. Before the study could be started, it involved constructing well-thought-out adaptive lessons. This paper discusses developing, refining, and revising the adaptive learning platform (ALP) lessons for pre-class learning in a Numerical Methods flipped course.

In a prior pilot study at a large public southeastern university, the first author had developed ALP lessons for the pre-class learning for four (Nonlinear Equations, Matrix Algebra, Regression, Integration) of the eight topics covered in a Numerical Methods course. In the current follow-on study, the first author and two other instructors who teach Numerical Methods, one from a large southwestern urban university and another from an HBCU, collaborated on developing the adaptive lessons for the whole course.

The work began in Fall 2020 by enumerating the various chapters and breaking each one into individual lessons. Each lesson would include five sections (introduction, learning objectives, video lectures, textbook content, assessment). The three instructors met semi-monthly to discuss the content that would form each lesson. The main discussion of the meetings centered on what a student would be expected to learn before coming to class, choosing appropriate content, agreeing on prerequisites, and choosing and making new assessment questions. Lessons were then created by the first author and his student team using a commercially available platform called RealizeIT. The content was tested by learning assistants and instructors. It is important to note that significant, if not all, parts of the content, such as videos and textbook material, were available through previously done work. The new adaptive lessons and the revised existing ones were completed in December 2020.

The adaptive lessons were tested for implementation in Spring 2021 at the first author's university and made 15% of the students' grade calculation. Questions asked by students during office hours, on the LMS discussion board, and via emails while doing the lessons were used to update content, clarify questions, and revise hints offered by the platform. For example, all videos in the ALP lessons were updated to HD quality based on student feedback. In addition, comments from the end-of-semester surveys conducted by an independent assessment analyst were collated to revise the adaptive lessons further. Examples include changing the textbook content format from an embedded PDF file to HTML to improve quality and meet web accessibility standards.

The paper walks the reader through the content of a typical lesson. It also shows the type of data collected by the adaptive learning platform via three examples of student interactions with a single lesson.
AU  - Autar Kaw
AU  - Ali Yalcin
AU  - Rafael Braga Gomes
AU  - Luis Serrano
AU  - Yingyan Lou
AU  - Andrew Scott
AU  - Renee Clark
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Development, Implementation, Refining and Revising of Adaptive Platform Lessons for an Engineering Course
UR  - https://peer.asee.org/40477
DO  - 10.18260/1-2--40477
ER  -