canvas-bulk-grading（画布批量分级）

Canvas Bulk Grading

Grade Canvas LMS assignments efficiently using rubric-based workflows. This skill requires the Canvas MCP server to be running and authenticated with an instructor or TA token.

Prerequisites

• Canvas MCP server running and connected
• Authenticated with an educator (instructor/TA) Canvas API token
• Assignment must exist and have submissions to grade
• Rubric must already be created in Canvas and associated with the assignment (Canvas API cannot reliably create rubrics -- use the Canvas web UI for that)

Workflow

Step 1: Gather Assignment and Rubric Information

Before grading, retrieve the assignment details and its rubric criteria.

get_assignment_details(course_identifier, assignment_id)

Then get the rubric. Use get_assignment_rubric_details if the rubric is already linked to the assignment, or list_all_rubrics to browse all rubrics in the course:

get_assignment_rubric_details(course_identifier, assignment_id)
list_all_rubrics(course_identifier)
get_rubric_details(course_identifier, rubric_id)

Record the criterion IDs (often prefixed with underscore, e.g., _8027) and rating IDs from the rubric response. These are required for rubric-based grading.

Step 2: List Submissions

Retrieve all student submissions to determine how many need grading:

list_submissions(course_identifier, assignment_id)

Note the user_id for each submission and the workflow_state (submitted, graded, pending_review). Count the submissions that need grading to determine which strategy to use.

Step 3: Choose a Grading Strategy

Use this decision tree based on the number of submissions to grade:

How many submissions need grading?
|
+-- 1-9 submissions
|   Use grade_with_rubric (one call per submission)
|
+-- 10-29 submissions
|   Use bulk_grade_submissions (concurrent batch processing)
|   Set max_concurrent: 5, rate_limit_delay: 1.0
|   ALWAYS run with dry_run: true first
|
+-- 30+ submissions OR custom grading logic needed
    Use execute_typescript with bulkGrade function
    99.7% token savings -- grading logic runs locally
    ALWAYS run with dry_run: true first

Strategy A: Single Grading (1-9 submissions)

Call grade_with_rubric once per student:

grade_with_rubric(
  course_identifier,
  assignment_id,
  user_id,
  rubric_assessment: {
    "criterion_id": {
      "points": <number>,
      "rating_id": "<string>",    // optional
      "comments": "<string>"      // optional per-criterion feedback
    }
  },
  comment: "Overall feedback"     // optional
)

Strategy B: Bulk Grading (10-29 submissions)

Always dry run first. Build the grades dictionary mapping each user ID to their grade data, then validate before submitting:

bulk_grade_submissions(
  course_identifier,
  assignment_id,
  grades: {
    "user_id_1": {
      "rubric_assessment": {
        "criterion_id": {"points": 85, "comments": "Good analysis"}
      },
      "comment": "Overall feedback"
    },
    "user_id_2": {
      "grade": 92,
      "comment": "Excellent work"
    }
  },
  dry_run: true,          // VALIDATE FIRST
  max_concurrent: 5,
  rate_limit_delay: 1.0
)

Review the dry run output. If everything looks correct, re-run with dry_run: false.

Strategy C: Code Execution (30+ submissions)

For large classes or custom grading logic, use execute_typescript to run grading locally. This avoids loading all submission data into the conversation context.

execute_typescript(code: `
  import { bulkGrade } from './canvas/grading/bulkGrade.js';

  await bulkGrade({
    courseIdentifier: "COURSE_ID",
    assignmentId: "ASSIGNMENT_ID",
    gradingFunction: (submission) => {
      // Custom grading logic runs locally -- no token cost
      const notebook = submission.attachments?.find(
        f => f.filename.endsWith('.ipynb')
      );

      if (!notebook) return null; // skip ungraded

      return {
        points: 100,
        rubricAssessment: { "_8027": { points: 100 } },
        comment: "Graded via automated review"
      };
    }
  });
`)

Use search_canvas_tools("grading", "signatures") to discover available TypeScript modules and their function signatures before writing code.

Token Efficiency

The three strategies have very different token costs:

The key insight: as submission count grows, sending grading logic to the server (code execution) is far cheaper than bringing all submission data into the conversation.

Safety Rules

1. Always dry run first. For bulk_grade_submissions, set dry_run: true before the real run. Review the output for correctness.
2. Verify the rubric before grading. Confirm criterion IDs, point ranges, and rating IDs match the assignment rubric. Mismatched IDs cause silent failures or incorrect grades.
3. Spot-check before bulk. For Strategy B and C, grade 1-2 submissions manually with grade_with_rubric first. Verify in Canvas that the grade and rubric feedback appear correctly.
4. Respect rate limits. Use max_concurrent: 5 and rate_limit_delay: 1.0 (1 second between batches). Canvas rate limits are approximately 700 requests per 10 minutes.
5. Do not grade without explicit instructor confirmation. Always present the grading plan (rubric mapping, point values, number of students affected) and wait for approval before submitting grades.

Example Prompts

• "Grade Assignment 5 using the rubric"
• "Show me the rubric for the midterm project and grade all submissions"
• "Bulk grade all ungraded submissions for Assignment 3 -- give full marks on criterion 1 and 80% on criterion 2"
• "How many submissions still need grading for the final paper?"
• "Dry run bulk grading for Assignment 7 so I can review before submitting"
• "Use code execution to grade all 150 homework submissions with custom logic"

Error Recovery