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Board # 28 : Beginning to Understand Student Indicators of Metacognition

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

16

Permanent URL

https://peer.asee.org/27820

Download Count

178

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Paper Authors

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Patrick Cunningham Rose-Hulman Institute of Technology

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Patrick Cunningham is an Associate Professor of Mechanical Engineering at Rose-Hulman Institute of Technology. During the 2013-14 academic year he spent a sabbatical in the Department of Engineering Education at Virginia Tech. Dr. Cunningham's educational research interests are student metacognition and self-regulation of learning and faculty development. His disciplinary training within Mechanical Engineering is in dynamic systems and control with applications to engine exhaust aftertreatment.

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Holly M Matusovich Virginia Tech

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Dr. Matusovich is an Assistant Professor and Assistant Department Head for Graduate Programs in Virginia Tech’s Department of Engineering Education. She has her doctorate in Engineering Education and her strengths include qualitative and mixed methods research study design and implementation. She is/was PI/Co-PI on 8 funded research projects including a CAREER grant. She has won several Virginia Tech awards including a Dean’s Award for Outstanding New Faculty. Her research expertise includes using motivation and related frameworks to study student engagement in learning, recruitment and retention in engineering programs and careers, faculty teaching practices and intersections of motivation and learning strategies. Matusovich has authored a book chapter, 10 journal manuscripts and more than 50 conference papers.

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Deirdre-Annaliese Nicole Hunter La Gran Familia De Gregory

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Dr. Deirdre Hunter is a lecturer at the Oshman Engineering Design Kitchen at Rice University. She develops and teaches courses in support of the engineering design minor. Her current research is in the areas of problem-based learning facilitation and teaching metacognition. Her research strengths include research design and implementation using qualitative methods. She has a Ph.D. in Engineering Education from Virginia Tech and a B.S. in Mechanical Engineering from Syracuse University

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Sarah Anne Blackowski Virginia Tech

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Sreyoshi Bhaduri Virginia Tech

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Sreyoshi Bhaduri is a Ph.D. candidate at Virginia Tech Department of Engineering Education. She is a proponent for use of technology in the classroom as well as education research. Sreyoshi is a Mechanical Engineer by training, who likes programming and algorithms to make life easier and more efficient. For her doctoral dissertation, she is exploring ways in which machine learning algorithms can be used by instructors in engineering classrooms.

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Abstract

Metacognition, defined as the knowledge and regulation of one’s own cognitive processes, is critically important to student learning and particularly instrumental in problem-solving. Despite the importance of metacognition, much of the research has occurred in controlled research settings such that much less is known about how to help students develop metacognitive skills in classroom settings. Further, there are significant bodies of research on the role of metacognition in writing and solving math problems, but little work has been done on the role of metacognition within engineering disciplines.

The purpose of this project is to generate transferable tools which can be used to teach and evaluate undergraduate engineering students’ metacognitive skills. This present paper reports on our development of a metacognitive indicator rubric for assessing students’ metacognitive processes and tracking their growth. Up to this point in the project we have created a six-module metacognitive intervention, piloted the intervention in a sophomore engineering course at a small private undergraduate-focused institution and translated the intervention to two more engineering education contexts including a first-year and upper-level engineering course each at different universities. Each module is made up of paired pre-class video, in-class activity, and post-class assignment elements. The videos provide a general view of metacognition situated within a STEM higher education context, while the in-class activities and post-class assignments are specialized for the particular context (e.g., problem solving, lab, or project based courses).

To develop a metacognitive indicator rubric, we analyzed student responses to the metacognitive module assignments collected during intervention pilot. We tested and refined the indicators using student data from subsequent implementations. Later we will work with instructors to ensure their utility and ease-of-use. In developing the indicator rubric, we first identified a question from each assignment that exemplified the main purpose of each module. Then all of the student responses from that question were pooled and ranked on a “low”, “medium”, or “high” level of metacognitive processing for that question. Since each module had a main topic, students responses with at least a mention of the topic were ranked as a “medium”. A “high” level answer related topics from the current module to ones they had seen before and made plans for implementing their new knowledge. A “low” level answer generally revealed that the student made little attempt to engage in the metacognition module. As such, the metacognitive indicator rubric serves as a translation of common student behavior to the formal elements of metaconition.

The metacognitive indicator rubric is designed to assist instructors in assessing how their students are engaging in the metacognition modules and in giving students specific and actionable feedback to improve their approaches to learning in their course. The rubric provides specific examples of student behavior in the students’ own words categorized by level and metacognitive dimension. As students progress through the modules, instructors will be able to track individual students’ metacognitive growth and target their feedback accordingly, praising progress and gently challenging less effective approaches to learning.

Cunningham, P., & Matusovich, H. M., & Hunter, D. N., & Blackowski, S. A., & Bhaduri, S. (2017, June), Board # 28 : Beginning to Understand Student Indicators of Metacognition Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/27820

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