We implemented a model for grading weekly assignments in an intermediate data science course that explicitly gave students useful feedback on their code while not evaluating it on the traditional metrics of correctness or style. This ungrading policy was used in a 150-student course led by 2 instructors and 11 Teaching Assistants (TAs).

Our ungrading policy was designed to extend empathy towards students and to give them useful, actionable feedback. Our policy reduced the stress that students felt each week, stabilized the amount of time they spent on assignments, and ask them to reflect on their code to request feedback from the teaching team.

Students could receive full credit for a homework assignment even if it was incomplete, making office hours less hectic. Students could also submit their homework after working on it for a certain number of hours, even if a bug still existed.

Our ungrading policy also helped our TAs feel valued. They gave feedback based on general expectations rather than a point-based rubric, allowing them to share their own expertise. TAs gave feedback only when requested, which was therefore more likely to be read.