A Secure, Scalable Approach to Student-Graded Homework for Self-Reflection

Matthew Jordan Ford; Heather Dillon

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Student Self-assessment in Mechanics Courses
Tagged Division: Mechanics Division (MECHS)
Permanent URL: https://peer.asee.org/46489

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Paper Authors

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Matthew Jordan Ford University of Washington orcid.org/0000-0002-1053-7149

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Matthew J. Ford (he/him) received his B.S. in Mechanical Engineering and Materials Science from the University of California, Berkeley, and went on to complete his Ph.D. in Mechanical Engineering at Northwestern University. After completing a postdoc with the Cornell Active Learning Initiative, he joined the School of Engineering and Technology at UW Tacoma to help establish its new mechanical engineering program. His teaching and research interests include solid mechanics, engineering design, and inquiry-guided learning. He has supervised undergraduate and master's student research projects and capstone design teams.

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Heather Dillon University of Washington orcid.org/0000-0002-4467-2306

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Dr. Heather Dillon is Professor and Chair of Mechanical Engineering at the University of Washington Tacoma. Her research team is working on energy efficiency, renewable energy, fundamental heat transfer, and engineering education.

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Abstract

A large body of research shows that deliberate practice is essential to developing expertise in any skill. The essential elements of deliberate practice are: (1) motivation, (2) intentional plan of practice, (3) repetition, and (4) timely feedback. We assign homework to provide students with repetitive, intentionally designed practice opportunities, but ensuring that students receive timely, effective feedback is resource-intensive and does not scale well to large classes. In addition, our experience with traditional homework grading suggests that many students do not even view detailed feedback when it is provided. One solution to both problems is for students to grade their own homework assignments.

Direct evidence of effectiveness of student-grading for learning is scant, but suggests that self-grading is more effective than peer-grading for achieving learning objectives [1]. A search of the engineering literature on student-graded assignments turned up a handful of studies [13-17]. The most common concerns are (1) scalability and instructor workload, (2) accuracy and reliability of student scores, (3) student perception and experience, and (4) academic integrity.

We present a methodology for student-graded homework which addresses these 4 concerns, and data demonstrating its effectiveness. Our specific contributions are (1) a scalable workflow using our Learning Management System API (Application Programming Interface), (2) a generic, flexible rubric which maps to ABET student outcomes, and (3) a straightforward approach to encourage academic integrity. We have collected data from three mechanical engineering courses for juniors and seniors: Machine Design, System Dynamics & Controls, and Heat Transfer. Our results indicate that students’ self-assessments are accurate and reliable, with an average bias < 6% and RMS error < 15%. Qualitative data from surveys and reflective journals suggest that students find the process intuitive and useful, and that self-grading prompts deeper reflection on their work.

Citation
Format

Ford, M. J., & Dillon, H. (2024, June), A Secure, Scalable Approach to Student-Graded Homework for Self-Reflection Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46489