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Labor-based Grading in Computer Science: A Student-Centered Practice

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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

Computing and Information Technology Division (CIT) Technical Session 3

Tagged Division

Computing and Information Technology Division (CIT)

Tagged Topic

Diversity

Page Count

17

DOI

10.18260/1-2--43927

Permanent URL

https://peer.asee.org/43927

Download Count

168

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Paper Authors

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Chris Marriott

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Menaka Abraham

biography

Heather E. Dillon University of Washington Orcid 16x16 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. Before joining academia, she worked for the Pacific Northwest National Laboratory (PNNL) as a senior research engineer working on both energy efficiency and renewable energy systems, where she received the US Department of Energy Office of Science Outstanding Mentor Award.

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Abstract

Innovation in teaching in STEM fields was explored widely during the COVID pandemic in 2020. This paper describes the adaptation of labor based grading for computer science courses. Labor based grading has been developed for language and writing courses by shifting the grading focus from summative exams to formative and reflective assessments. The method was tested in several computer science courses with two different instructors during the 2020-2021 academic year. Students were surveyed to understand how they perceived grading methods in the course and their own level of anxiety. A total of 69 students completed the survey where 84% reported the method reduced anxiety (4 or 5 on a Likert scale). The study found that labor based grading was an effective way to reduce student anxiety, reduce academic integrity issues, and improve student motivation.

Marriott, C., & Abraham, M., & Dillon, H. E. (2023, June), Labor-based Grading in Computer Science: A Student-Centered Practice Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43927

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