dojo-ascension

Impact Measurement

This document turns the public metrics into a repeatable reporting workflow for pilots, grants, fellowships, and nonprofit adoption.

Core questions

• Are learners returning?
• Are learners completing missions?
• Are learners gaining confidence and belonging?
• Are learners becoming contributors, authors, or facilitators?

Quantitative metrics

1. 14-day learner return rate

Definition: percent of learners with a second logged session within 14 days.

python measure_retention.py --data-dir <dir>

2. Median missions completed per active learner

Definition: middle value of mission completions across learners in a cohort.

python export_learner_data.py --data-dir <dir> --format json

3. Contributor growth

Definition: unique merged PR authors per month.

Source:

• GitHub PR activity
• CHANGELOG.md

Qualitative metrics

• learner confidence after each session
• whether learners felt included or excluded
• facilitator notes about where support was required
• examples of non-code contribution: mission writing, translation, accessibility review, documentation, facilitation

Recommended cohort evidence bundle

• anonymized export from local save files
• retention rollup
• 2-3 short learner quotations
• 1 facilitator summary
• 1 note on what changed in docs or missions after feedback

Suggested reporting cadence

• per session: learner/facilitator notes
• per cohort: short pilot summary
• monthly: CHANGELOG.md metrics update

Minimal public summary template

• cohort size
• missions started and completed
• 14-day return rate
• median missions completed
• confidence / belonging themes
• contribution outcomes
• next experiment