2023 ASEE Annual Conference & Exposition

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

Presented at Computing and Information Technology Division (CIT) Technical Session 3

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.

Authors

Chris Marriott
Menaka Abraham http://orcid.org/https://0009-0000-8250-5915 [biography]

Menaka Abraham is a Teaching Professor of Computer Science & Systems at the University of Washington Tacoma. Her research interests are in student-support and evidence-based pedagogical practices.
Dr. Heather E. Dillon http://orcid.org/0000-0002-4467-2306 University of Washington [biography]

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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