Harder courses award higher raw grades but lower adjusted ones, school-wide.

Schools compare student grades across different classes, but teachers grade differently and courses rarely share a common test. To see if grades alone can reveal grading differences, researchers built a network connecting students and the courses they took at one high school. Because students take multiple classes, the model can separate a student's typical performance from how strictly a course is graded. They found that raw grades were higher in advanced classes, but adjusting for student performance flipped this pattern: advanced and college-level courses graded more strictly than lower tiers. Rigor explained a large share of grading variation, though the exact patterns varied across course subjects. Furthermore, external exam data confirmed these grading differences, and differences between courses mattered much more than differences between teachers of the same course. However, the researchers emphasize that these patterns reflect the overall grading environment rather than direct proof of teacher bias.

Why it matters

Schools constantly compare grades across courses, but without a common test it's hard to know if a grade reflects learning or just how hard the class is. This study shows that internal grade networks can reveal systematic patterns—like harder courses actually grading more leniently after adjusting for students—and can even predict AP exam outcomes. That gives schools a tool to spot grading inconsistencies without adding any tests.

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