2023 ASEE Annual Conference & Exposition

Board 422: Using Adaptive Learning Platform Metrics for Early Identification and Personalized Support of Low-Performing Students

Presented at NSF Grantees Poster Session

In the last two decades, flipped learning has become one of the pedagogies to integrate active learning in a classroom. Although flipped learning has tangible benefits for learning and engagement, student resistance remains a challenging issue. This struggle is most evident in pre-class learning, which leads to inadequate preparation for the in-class engagement exercises, including answering conceptual questions and solving procedural problems.

In a course in Numerical Methods taught in the department of mechanical engineering at a large southeastern university, we alleviated this resistance to pre-class preparation via adaptive learning platform (ALP) lessons. ALPs use machine learning algorithms to deliver personalized learning activities and valuable feedback to students reliably.

One of the benefits of using an ALP is the significant amount of data it collects about student behavior and engagement with the course material. Since these ALP lessons were used from the beginning of the semester onward, we proactively identified low-performing students who may have needed support to improve their performance in the course. Based on the results of a descriptive analysis performed on ALP collected during Fall 2021 and Spring 2022, we found that students who were most likely to exhibit low performance (as defined by a course grade of C or below) did not complete their ALP lessons on time, scored lower on the ALP assessments, made a higher number of attempts, and spent less time on the instructional content versus the assessment questions with the platform.

In Fall 2022, we used decision tree models with the ALP data to identify potentially low-performing students in the academic semester's second, third, and fourth weeks. Based on this, we extended an official invitation for one-on-one support and advising to 18 students out of a class of 62 who were identified as potentially low performing during the first four weeks. Six of them accepted the invitation and met regularly at a scheduled time with the instructor or the teaching assistants. In this paper, we will discuss the performance of all 18 students and compare those who accepted versus declined the invitation. Considering that the decision tree models may not have identified some low-performing students, necessary adjustments to the model will be made for better identification in Spring 2023, with subsequent results available at the time of the conference.

Authors

Prof. Autar Kaw http://orcid.org/0000-0002-3976-6375 University of South Florida [biography]

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. His primary scholarly interests are 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 1M+ page views, 1.6M+ views of the YouTube lectures, and 90K+ 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 115 refereed technical papers, and his opinion editorials have appeared in the Tampa Bay Times, the Tampa Tribune, and the Chronicle Vitae.
Dr. Ali Yalcin http://orcid.org/0000-0002-9543-0681 Montana State University - Bozeman [biography]

Ali Yalcin, is currently an Associate Professor of Industrial and Management Systems in the College of Engineering at the Montana State University.

He is the co-founder of Collaborative for Research & Education in Aging and Technology and was part of the leadership team who founded the Patel College of Global Sustainability at the University of South Florida.

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.
Dr. Renee M. Clark University of Pittsburgh [biography]

Renee Clark serves as the Director of Assessment for the Swanson School of Engineering at the University of Pittsburgh. She received her PhD from the Department of Industrial Engineering, where she also completed her post-doctoral studies. Her research has primarily focused on the application of data analysis techniques to engineering education research studies as well as industrial accidents. She has over 20 years of experience in various engineering, IT, and data analysis positions within academia and industry, including ten years of manufacturing experience at Delphi Automotive.

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