Asee peer logo

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

Download Paper |


2023 ASEE Annual Conference & Exposition


Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

NSF Grantees Poster Session

Tagged Topics

Diversity and NSF Grantees Poster Session

Page Count




Permanent URL

Download Count


Request a correction

Paper Authors


Autar Kaw University of South Florida Orcid 16x16

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 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 ( 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.

visit author page


Ali Yalcin University of Montana

visit author page

Dr. Ali Yalcin received his B.S., M.S., and Ph.D. degrees in Industrial and Systems Engineering from Rutgers University, New Brunswick New Jersey in 1995, 1997 and 2000. He is currently an Associate Professor at the University of South Florida, Industrial

visit author page


Renee M. Clark University of Pittsburgh

visit author page

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.

visit author page

Download Paper |


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.

Kaw, A., & Yalcin, A., & Clark, R. M. (2023, June), Board 422: Using Adaptive Learning Platform Metrics for Early Identification and Personalized Support of Low-Performing Students Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42761

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2023 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015