Academic integrity continues to be in issue in computing education and education more broadly. Students grades are often computed using a collection of evidence that varies in its trustworthiness (e.g., a proctored exam can be trusted more than an out-of-class programming project). For many courses, the less trustworthy assessments represent a choice between the burden of making a cheating accusation or rewarding cheaters.

In this position paper, we propose the concept of “Bayesian Grading” as a means of cutting this Gordian knot. Rather than compute grades by performing arithmetic on both trusted and untrusted assessments, we instead try to infer a latent variable, the student’s mastery of the course material, from these observed performances and their potential for cheating. Key to this approach is that grades can be assigned that discount suspicious work without needing to explicitly make a cheating allegation. In addition, following this chain of thought suggests collecting a variable number of trusted pieces of evidence.