Community Learning Path Finder

Help a learner choose a feasible sequence from an explicit catalog of workshops and resources. Combine prerequisite graphs, stated goals and a time budget, explain why each resource was selected, and export an editable HTML learning map with a machine-readable path for another course platform.

The implementation uses Python 3.10+ and standard libraries. Samples are original teaching data. Read API.md for exact input, output and error contracts.

Build your version

python3 scripts/project_test.py community-learning-path-finder --init learning-artifacts/community-learning-path-finder
python3 scripts/project_test.py community-learning-path-finder --stage 1 --path learning-artifacts/community-learning-path-finder --strict

A fresh starter fails clearly until you implement the first contract. Later stages retain your source file. The supplied CLI and presentation helpers are scaffolding; all domain decisions call your implementation.

python3 scripts/project_test.py community-learning-path-finder --all --path learning-artifacts/community-learning-path-finder --strict
cd learning-artifacts/community-learning-path-finder
python3 cli.py samples/input.json --output my-output

Run the reference and your own input

python3 scripts/project_test.py community-learning-path-finder --all --solution --strict
cd projects/community-learning-path-finder/solution
python3 cli.py samples/input.json --output demo-output
python3 cli.py /absolute/path/to/your-input.json --output your-output

Input JSON contains resources, goals, budget and optional completed IDs. Outputs learning-map.html, path.json, unmet-prerequisites.json. Edit the HTML progress JSON and download decisions.json, then rerun with --decisions decisions.json. A course platform imports path.json path entries in array order, retaining id, prerequisites, minutes and completed fields.

Worked example and limits

Neighborhood mapping starts with coordinate basics, then field collection, then publishing. Advanced analysis also asks for an external statistics prerequisite.

HTML edits represent self-reported progress. The importer validates dependency consistency and fingerprints, not learner identity or assessment success.

Stages

1. Model resources, goals and prerequisite relationships
2. Find feasible paths and expose missing prerequisites
3. Rank alternatives under an explicit time budget
4. Export an editable learning map and integration contract

Primary references

• Technical reference