Learning Analytics Skill for ChatGPT: Report Participation and Outcomes
Plan an education report around enrollments, submissions, and evidence of mastery. Give each measure a source, denominator, and audience before interpreting results.
中文Install in ChatGPT
Use an eligible ChatGPT workspace with permission to create skills and, for uploads, upload permission. The installation path for this text-only adaptation was checked against OpenAI documentation. Workspace runtime verification is pending.
- Download skills/learning-analytics/SKILL.md from the pinned GitHub source. Keep the name, description, and instructions for learning-analytics together as a standalone text skill.
- In a ChatGPT workspace with skills and uploads enabled, open Plugins, Skills, Create, and Upload from your computer. Choose a skill package format accepted by the file picker. You can also choose Create with editor and enter the original name, description, and instructions.
- After the scan completes, check that learning-analytics appears in Installed. Type @ in a new chat, select the skill, and use the fictional example on this page to check the output.
Example task
A fictional adult writing course had 30 enrollments, 24 submissions, and 18 submissions that met two core criteria. Draft measures, calculations, and a follow-up question for the instructor.What to check
Show 24/30 = 80% submitted, 18/24 = 75% mastery among submitters, and 18/30 = 60% mastery among all enrollees. State the denominators and flag why some learners did not submit as unknown.
Original SKILL.md on GitHub · Updated
Frequently asked question
Does a high submission rate demonstrate learning?
It measures participation. Use outcomes scored against the rubric to assess learning, explain the denominator, and account for people whose work was missing.