Asee peer logo

Metacognition in Graduate Engineering Courses

Download Paper |

Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Graduate Studies Division (GSD) Technical Session 2: Innovative Approaches to Teaching and Learning in Engineering Graduate Programs

Tagged Division

Graduate Studies Division (GSD)

Page Count

16

DOI

10.18260/1-2--43629

Permanent URL

https://peer.asee.org/43629

Download Count

176

Request a correction

Paper Authors

visit author page

Dr. Olesova is Assistant Professor of Educational Technology in College of Education, the University of Florida. Her research interests are Community of Inquiry, cognitive presence, metacognition, learning analytics, social network analysis, online engagement and interactions and online instructional strategies.

visit author page

biography

Duoduo Liao George Mason University

visit author page

Dr. Duoduo Liao is an Associate Professor in the Department of Information Sciences and Technology at George Mason University. Her latest research interests focus more on Multimodal Artificial Intelligence (AI), computational neuroscience, and AI for arts. She has published over sixty peer-reviewed publications, including two books on real-time three-dimensional graphics and one book chapter on Big Data computing with a new computational brain model. Dr. Liao earned her M.S. & Ph.D. degrees in Computer Science from Purdue University & George Washington University, respectively.

visit author page

biography

Ioulia Rytikova George Mason University

visit author page

Ioulia Rytikova is a Professor and an Associate Chair for Graduate Studies in the Department of Information Sciences and Technology at George Mason University. She received a B.S./M.S. and Ph.D. degrees in Automated Control Systems Engineering and Information Processing. Her research interests lie at the intersection of Data Science and Big Data Analytics, Cognitive and Learning Sciences, Educational Data Mining, Personalized Learning, and STEM Education.

visit author page

biography

Mihai Boicu George Mason University Orcid 16x16 orcid.org/0000-0002-6644-059X

visit author page

Mihai Boicu, Ph.D., is Assistant Professor of Information Technology at George Mason University. He published over 120 peer-reviewed publications, including 4 books. He performs theoretical and applied research in Artificial Intelligence, Machine Learning, Probabilistic Reasoning, Crowdsourcing and Engineering Education. He received more than 3M in funding from NSF, DARPA, IARPA, AFOSR, IC and other government agencies.

visit author page

biography

Harry J. Foxwell George Mason University

visit author page

Harry is currently Associate Professor at George Mason University's Department of Information Sciences and Technology. He earned his doctorate in Information Technology in 2003 from George Mason University's Volgenau School of Engineering (Fairfax, VA), and has since taught graduate courses there in big data analytics and ethics, operating systems, computer architecture and security, cloud computing, and electronic commerce.

visit author page

Download Paper |

Abstract

Graduate Engineering students need to understand and use the concepts and methods of applied research when they enter the engineering workforce. To enhance the quality of learning and to create a meaningful experience for engineering students, it is important to understand collaborative learning and instructional approaches that can support the development of students’ metacognitive processes. Metacognition is defined as “a set of higher knowledge and skills to monitor and regulate cognitive processes of self and others” (Garrison & Akyol, 2015, p.184). Metacognition is a required cognitive ability to achieve deep and meaningful learning that must be viewed from both an individual and social perspective. Metacognition is central to the cognitive presence and collaborative inquiry process. However, according to Garrison (2022), the role of metacognition in developing the necessary awareness and regulation for responsible thinking and learning in shared learning environments has not been sufficiently emphasized. This study developed and implemented a suite of customizable online learning modules to guide students in applied research. The learning modules were divided into three steps: research question, literature research, and conducting research using a generic template with the following sections: annotated open-source learning materials, a random knowledge test with a pool of case study questions, a discussion board for brainstorming which required students to post their draft ideas, to comment on other student's posts and integrate the comments in their submission, a self-reflection assignment to summarize the lesson learned, and a written assignment to organize their findings, make connections, elaborate ideas, and construct an argument based on their research. The modules were implemented in five courses in two MS programs. A post-course self-assessment survey about perceived cognitive presence and metacognition was administered to the 167 enrolled students; 126 responses were received, resulting in an overall 86.2% responding Satisfied or Extremely Satisfied with the research modules. While the sample size was small there was a strong indication that the modules were successful in promoting understanding and use of applied research concepts and methods as the students found the learning modules effective and helpful. To validate the initial results, we are currently performing a new survey in new sections of the same courses. A post-course self-assessment survey about perceived self-regulated and shared metacognition will be administered to the enrolled students at the end of the fall semester. The metacognition survey includes 5-point Likert-scale questions assessing the students’ levels of perceived self-regulation and perceived satisfaction working with others. The survey will examine responses for differences between online vs in-person instruction, individual courses, and programs of study. Based on the survey results, this study will indicate whether the learning modules helped students self-regulate their learning while working with others in a shared learning environment. Furthermore, the research modules may be applicable to general research education, in addition to applied research. The early and broad introduction of applied research instruction in the engineering curriculum may enhance efforts to increase STEM participation in future research employment opportunities and doctoral education, especially for underrepresented groups and first-generation graduate students.

Olesova, L., & Liao, D., & Rytikova, I., & Boicu, M., & Foxwell, H. J. (2023, June), Metacognition in Graduate Engineering Courses Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43629

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