This workflow keeps feedback specific and privacy-aware: anonymize learner output, diagnose a small number of priority issues, and turn feedback into a next learning action.
从学习者产出到反馈循环
A workflow for turning anonymized learner output into focused feedback and follow-up practice.
Workflow
Introduction
Workflow Steps
1. Prepare safe learner samples
Remove names, contact information, grades, and sensitive details before using AI assistance.
Output: A small anonymized sample set with task context.
2. Diagnose priority patterns
Use the learner error feedback prompt or a rubric prompt to identify recurring errors and strengths.
Output: A short list of feedback priorities, not an exhaustive error inventory.
3. Draft feedback language
Generate teacher-reviewed feedback sentences for the whole class, groups, or individual anonymous patterns.
Output: Feedback wording that is specific, limited, and learner-facing.
4. Create follow-up practice
Design one short task that lets learners immediately apply the feedback.
Output: A follow-up activity and evidence of whether the feedback helped.
Information
- Asset typeWorkflow
- Published date2026/05/27
Asset Fit
Audience
Teacher
Scenario
Assessment FeedbackClassroom Activity