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Self-Corrected Homework for Incentivizing Metacognition

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Electrical and Computer Division Poster Session

Tagged Division

Electrical and Computer

Page Count

12

DOI

10.18260/p.26155

Permanent URL

https://peer.asee.org/26155

Download Count

398

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Paper Authors

biography

Paul Douglas Kearsley Western Washington University

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Paul Kearsley has been teaching in Western Washington University's Department of Engineering since 2013. His focus is primarily Industrial Design and Visual Communication. He is passionate about sustainable design and is pursuing a Masters in Education.

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biography

Andrew G. Klein Western Washington University Orcid 16x16 orcid.org/0000-0002-6464-9269

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Andrew G. Klein joined Western Washington University (WWU) in 2014 as an Assistant Professor with a joint appointment to the department of Engineering and Design (Electrical Engineering Program) and the graduate faculty of Computer Science. He received the B.S. degree in electrical engineering from Cornell University, and the M.S. degree in electrical engineering and computer sciences from the University of California, Berkeley. He then worked for awhile at several Silicon Valley startup companies before returning to Cornell to pursue a Ph.D. in electrical and computer engineering in 2006. Prior to his arrival at WWU, he worked as a postdoctoral researcher at Supélec/LSS near Paris, France, and was an assistant professor at Worcester Polytechnic Institute.

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Abstract

With the widespread availability of online solution manuals, ever more intelligent search engines, and repositories containing solutions from previous course offerings, the utility of traditional homework as a form of summative or formative assessment is increasingly unclear. Traditional homework models reward correct answers, incentivizing students to consult online sources for answers; studies have shown that 90% of students consult (questionably obtained) online solution manuals when completing homework. While there are means to counteract this somewhat (e.g., creating new problems or variations each course offering), it would seem that with the rampant use of solution manuals by students, traditional graded homework assignments are an unreliable indicator of student learning.

In this study, which was conducted in two junior/senior-level electrical engineering classes as well as a third freshmen-level introduction to engineering course, we explore the use of a self-grading and self-correcting homework model as means for encouraging metacognition. While previous studies have investigated the use of self-grading and self-correcting, most of them have left traditional incentive mechanisms in place. This work considers an alternate incentive mechanism with the aim of encouraging students to attempt problems on their own while removing the incentive to plagiarize. Instead of assigning students a grade on the accuracy of their homework, we grade them on their own ability to evaluate and correct their homework. Under this model, students submit homework twice: the initial submission where there is no penalty for wrong answers (only incomplete problems), and a second submission (after detailed solutions are provided) where they have graded and corrected their own homework. The recorded grade that they ultimately earn for the assignment is based on how well they grade and correct their homework, encouraging a self-reflective analysis of their own learning.

The first question that we attempt to verify in this study is how well students in engineering courses grade their own homework when a self-graded homework model is in place. This is a question that has been addressed previously in the literature in other disciplines, and we seek to compare our results with others. To do so, we graded a photocopied version of each student's initial homework submission without the students' knowledge; meanwhile, the students used an answer key and grading rubric to grade their own initial homework submission, which they subsequently resubmitted, and the results were compared. Our findings are largely in agreement with the majority of the literature on self-graded homework assignments, confirming that the widely reported results from other disciplines appear to also be relevant to self-graded homework in electrical engineering courses. The second question that we attempt to address regards the students' perceptions of the self-graded and self-corrected homework model, compared to the traditional model. Our intention is to assess whether self-reflection via self-correction encourages deeper personal involvement and awareness of the learning process. Metacognition is a complex construct that is not directly observable, and in this study we resort to the common but limiting approach of self-reported survey results from students as a means to assess metacognition. Results from the survey show that students do indeed report a higher awareness of their learning process, suggesting that this alternative grading model incentivizes metacognition.

Kearsley, P. D., & Klein, A. G. (2016, June), Self-Corrected Homework for Incentivizing Metacognition Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26155

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