Hybrid auto-grading and grading at scale

MAP is built for hybrid grading: automatically check what the entity system can verify, then give teachers fast tools for everything that still needs a human.

What auto-grades well

Structured answers tied to parametric entities—numeric results, many constrained formats, and similarly checkable fields—can be evaluated against the instance the student received.

What needs manual attention

Canvas work, long answers, and other open-ended responses are flagged for teacher review. The dashboard can surface when manual grading is still required so nothing sits invisible in a queue.

Question-batch grading

Instead of only grading student-by-student, teachers can grade the same question slot across the class. That is dramatically faster for shared structure—especially when many students answered the same blueprint item in different numeric costumes.

Attempt review and overrides

Open an attempt review to inspect responses, adjust scores, and finalize. Overrides exist because real classrooms need judgment—partial credit, misread work, or special cases.

Why it scales

• Auto-grade removes busywork on deterministic fields
• Batch views keep open-ended grading focused
• Dashboard signals prevent forgotten piles of ungraded work

FAQ

Do students see grades immediately?

Release behavior is configurable (see the grades-control article). Auto-grade does not force instant publication.

Can I regrade after release?

Teachers can revisit attempts and adjust; communicate policy clearly when scores change.