Board 193: Adaptive v. Faulty Adaptive Learning: The Interplay Between Knowledge About Task and Self-Regulation
- Conference
- Location
-
Portland, Oregon
- Publication Date
-
June 23, 2024
- Start Date
-
June 23, 2024
- End Date
-
June 26, 2024
- Conference Session
- Tagged Topic
-
NSF Grantees Poster Session
- Page Count
-
8
- DOI
-
10.18260/1-2--46758
- Permanent URL
-
https://peer.asee.org/46758
- Download Count
-
77
Paper Authors
Oenardi Lawanto Utah State University
Dr. Oenardi Lawanto is a professor in the Department of Engineering Education at Utah State University, USA. He received his B.S.E.E. from Iowa State University, his M.S.E.E. from the University of Dayton, and his Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Lawanto has a combination of expertise in engineering and education and has more than 30 and 14 years of experience teaching engineering and cognitive-related topics courses for his doctoral students, respectively. He also has extensive experience in working collaboratively with several universities in Asia, the World Bank Institute, and USAID to design and conduct workshops promoting active-learning and life-long learning that is sustainable and scalable. Dr. Lawanto’s research interests include cognition, learning, and instruction, and online learning.
Angela Minichiello
Utah State University
orcid.org/0000-0002-4545-9355
Angela (Angie) Minichiello is a military veteran, licensed mechanical engineer, and associate professor in the Department of Engineering Education at Utah State University. Her research examines issues of access, equity, and identity in the formation of engineers and a diverse, transdisciplinary 21st century engineering workforce. Angie received an NSF CAREER award in 2021 for her work with student veterans and service members in engineering.
Talha Naqash Utah State University
Mr.Talha Naqash is currently pursuing his doctoral studies in Engineering Education at Utah State University. With a profound educational background spanning multiple disciplines, he holds an MS in Telecommunication and networking. His extensive research contributions are reflected in numerous publications and presentations at prestigious IEEE & ASEE conferences, Wiley’s, and Springer Journals. His research primarily revolves around understanding Cognitive Engagement Analysis, Assessing Methods in Engineering Education, and Facial Expressions (emotions) in the Learning process. He is a member of various technical committees, serving as a reviewer for esteemed journals and international conferences including ASEE, Springer (JAIHC), and IEEE Transaction on Education. His commitment to advancing education, paired with his extensive academic and professional experiences, positions him as a promising researcher in engineering education.
Zain ul Abideen Utah State University
Zain ul Abideen is a Graduate Research Assistant and Ph.D. student in the Department of Engineering Education at Utah State University (USU). With an undergraduate degree in Computer Engineering and a Master’s in Engineering Management, coupled with over 12 years of teaching experience with undergraduate engineering students, Zain is currently dedicated to pursuing a Ph.D. in Engineering Education at USU in Logan, UT, USA. His current focus is on coursework and literature exploration, with a particular interest in studying Meta-cognitive processes and how engineering students self-regulate their cognition and motivation strategies during problem solving activities.
Abstract
This paper reports preliminary findings from a National Science Foundation (NSF) funded research targeting the enhancement of Engineering and Mathematics (EM) education. The project's central objective revolves around explaining the critical role of students’ metacognitive knowledge about task (MKT) and self-regulation in action (SRA) during problem-solving activities. This research paper seeks to understand the interplay between MKT and SRA, and how it leads to their problem-solving performance in two second-year engineering and mathematics (EM) courses, Engineering Statics and Ordinary Differential Equations.
Qualitative data were collected through one-on-one interviews before, and think aloud verbalization while, solving problem. Qualitative data were generated with 20 undergraduate students (i.e., 7 females, and 13 males) across both courses (i.e., 11 and 9 students from mathematics and engineering, respectively) through one-on-one interviews before, and think aloud verbalization while, solving problem. During data generation, each student engaged in four EM content-driven problem-solving activities of varying levels of difficulty. Data generation resulted in a total of 80 problem-solving qualitative data generation events with 20 unique participants.
The qualitative data is analyzed by using systematic and iterative techniques based on constant comparative analysis (CCA). Further, the analysis involves the deployment of initial and focused level codes, where initial codes directly reflect the raw data, while focused codes refine the seven significant problem-solving cases or patterns observed across the dataset.
Based on the analysis, the seven cases were clustered into four quadrants based on their low/high MKT level and low/high SRA levels. Each case describes a unique interplay between students’ knowledge about task and self-regulation. In this paper, we focus on two possible cases belonging to the second quadrant (i.e., Adaptive Learning, and Faulty Adaptive Learning). In the adaptive learning environment, effective self-regulating deployment could enhance students’ inadequate metacognitive knowledge about tasks to achieve a satisfactory task performance. Faulty adaptive illustrates a problem-solving episode where adequate self-regulating strategies with lacking metacognitive knowledge about task could also potentially lead to an unsatisfactory task performance. Brief discussion is included at the end of the paper.
Lawanto, O., & Minichiello, A., & Naqash, T., & Abideen, Z. U. (2024, June), Board 193: Adaptive v. Faulty Adaptive Learning: The Interplay Between Knowledge About Task and Self-Regulation Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46758
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: © 2024 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