ASEE PEER - Keys to Success for an Alternative Grading Scheme in a Large Enrollment Differential Equations Course
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Keys to Success for an Alternative Grading Scheme in a Large Enrollment Differential Equations Course

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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

Mathematics Division (MATH) Technical Session 2

Tagged Division

Mathematics Division (MATH)

Permanent URL

https://peer.asee.org/47710

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Paper Authors

biography

Hadas Ritz Cornell University Orcid 16x16 orcid.org/0000-0002-5396-2962

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Hadas Ritz is a senior lecturer in Mechanical and Aerospace Engineering, and a Faculty Teaching Fellow at the James McCormick Family Teaching Excellence Institute (MTEI) at Cornell University, where she received her PhD in Mechanical Engineering. Among other teaching awards, she received the 2021 ASEE National Outstanding Teaching Award.

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biography

Stephan Wagner Cornell University

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Stephan Wagner is a senior undergraduate student in Mechanical Engineering with a professional background in consumer software and technology.

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Abstract

In this work-in-progress paper, we describe the key changes we made to our efforts to implement a standards-based, mastery-based grading scheme in a large enrollment Differential Equations course at an R1 university. In this continuation of prior work, we have addressed many of the challenges we faced in our first effort to transition from traditional grading to an alternative grading scheme in this course last year.

In the first version of our implementation of an alternative grading scheme, we identified inconsistency in grading as a major shortcoming. In this paper, we will describe the measures we instituted to improve grading fidelity. Other significant shortcomings in the first version were a consequence of scale: alternative grading schemes in large enrollment courses (in this case, 400+ students) bring unique challenges. The steps we have taken include improving our own pre-semester preparedness and increasing automation of all logistics.

We will describe a collection of Python and MATLAB programs written to address each aspect of the logistics that proved a major obstacle in the past. These programs are specific to the learning technology in use at our school: Canvas as the Learning Management Software (LMS) and Gradescope for grading most hand-written work. Our "lessons learned" from two versions of this alternative grading scheme are presented here as "best practices" which we hope will be useful for other faculty wishing to implement standards-based grading on a large scale.

Ritz, H., & Wagner, S. (2024, June), Keys to Success for an Alternative Grading Scheme in a Large Enrollment Differential Equations Course Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47710

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