Labor Based Grading in Computer Science - A Student Centered Practice

AUTHORS

• Chris Marriott, School of Engineering & Technology, UW Tacoma
• Heather Dillon, School of Engineering & Technology, UW Tacoma
• Menaka Abraham, School of Engineering & Technology, UW Tacoma

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ABSTRACT

Innovation in teaching in STEM fields was explored widely during the COVID pandemic in 2020. This project describes the adaptation of labor based grading for undergraduate computer science courses. Labor based grading has been developed for language and writing courses, and shifts the grading focus from summative exams to formative and reflective assessments. Traditional grading is based on the idea that there is one correct response, often that of the dominant culture or viewpoint, and is widely used in STEM classes. Traditional grading systems rarely value the time and work invested in gaining knowledge, which may be very different for each student. In contrast, labor based grading rewards students for investing time and effort to gain new knowledge or skills, making it an important tool for inclusive teaching.

The research question considered is to what extent labor based grading enhances the learning experience and reduces anxiety for STEM students? The method was tested in several undergraduate computer science courses with two different instructors during the 2020-2021 academic year. Students were asked to self-assess labor based grading with a survey. The survey was completed by 69 students and analyzed using statistical software. The study found that labor based grading was an effective way to reduce student anxiety, reduce academic integrity issues, and improve student motivation. The findings also support wider adoptation of labor based grading in STEM classrooms.

SUMMARY

RESEARCH QUESTION

What extent labor based grading enhances the learning experience and reduces anxiety for STEM students?

RESEARCH METHODS / SCHOLARLY BASIS

The method was tested in several undergraduate computer science courses with two different instructors during the 2020-2021 academic year. Students were asked to self-assess labor based grading with a survey. The survey was completed by 69 students and analyzed using statistical software.

RESULTS

The study found that labor based grading was an effective way to reduce student anxiety, reduce academic integrity issues, and improve student motivation.

APPLICATION

The findings also support wider adoption of labor based grading in STEM classrooms.