grading-level-division-reference-calibrated

Align grading boundaries with reference score distributions and emit level_division.json.

Updated Aug 27, 2026

Search Tags:#education #calibration #rubric #grading #level-division #reference-scores

Ready to run

npx skills add https://github.com/AKCqhzdy/dse-subject-grading --skill grading-level-division-reference-calibrated

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: grading-level-division-reference-calibrated
Source: https://github.com/AKCqhzdy/dse-subject-grading/tree/main/skills-v3/grading-level-division-reference-calibrated
Command: npx skills add https://github.com/AKCqhzdy/dse-subject-grading --skill grading-level-division-reference-calibrated

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill computes a reference-calibrated level division for a grading regime. It uses the empirical score-to-level mapping from reference students, combined with rubric descriptors, and applies a reference-year bias prevention protocol before finalising the boundaries.

Core Features & Use Cases

Determines initial level boundaries from reference score distributions and rubric means.
Applies anti-bias adjustments to align the current cohort with reference-year expectations.
Produces a deterministic level_division.json that records boundaries, the applied adjustments, and per-student final levels.

Quick Start

Run the reference-calibrated level division workflow to produce rubric/${SCOPE_GRADING_YEAR}/level_division.json.

Frequently Asked Questions about grading-level-division-reference-calibrated

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calibrate grade boundaries using reference scores?▼

To calibrate grade boundaries, align grading boundaries with reference score distributions and rubric descriptors to mitigate year-to-year bias. The process applies anti-bias adjustments to match current cohorts with reference-year expectations, generating a deterministic level division file.

What is reference-calibrated level division in grading?▼

Reference-calibrated level division computes score-to-level mapping using empirical data from reference students combined with rubric means. It applies a bias prevention protocol before finalizing boundaries, ensuring grade levels reflect actual rubric performance rather than arbitrary cutoffs.

How do I prevent year-to-year grading bias in rubric scoring?▼

Prevent year-to-year grading bias by applying anti-bias adjustments that align current cohort scores with reference-year expectations. This protocol compares current distributions against reference scores and rubric descriptors before finalizing grade boundaries.

What files do I need to generate a deterministic level division?▼

Generating a deterministic level division requires final_scores.json, rubric reference_scores.json, and reference_calibration.md files. These inputs provide the score distributions and rubric descriptors needed to compute boundaries and emit per-student final levels.

Can I use reference score distributions to set grade boundaries for different cohorts?▼

Yes, reference score distributions determine initial level boundaries from empirical mapping and rubric means. The system applies anti-bias adjustments to align different cohorts with reference-year expectations, ensuring consistent grading standards across varying student populations.

Why does my level division output include adjustment notes?▼

Adjustment notes document the anti-bias adjustments applied to align current scores with reference-year expectations. These notes accompany the per-student final levels in the output, providing transparency about boundary modifications made during the calibration process.