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Teaching Guide — Using This Curriculum in a Classroom

Home: README

This guide helps instructors adapt the learn.python curriculum for classroom use, whether you are running a university course, a bootcamp cohort, or a community workshop.

Semester Mapping

16-Week University Semester

Week Content Projects Assigned Deliverable
1 Computer Literacy Primer (Doc 00), Setup (Doc 03) Level 00: exercises 01–05 Python installed, first script runs
2 Roadmap + Glossary (Docs 01–02), begin Foundations (Doc 04) Level 00: exercises 06–15 All absolute beginner exercises complete
3 Foundations continued — variables, loops, functions Level 0: projects 01–05 First passing test
4 Foundations continued — files, errors, collections Level 0: projects 06–10 File I/O project working
5 Foundations wrap-up, Level 0 capstone Level 0: projects 11–15 Gate A: Setup + first test
6 Quality & Testing (Doc 09) — pytest, ruff, black Level 1: projects 01–08 Tests written for own code
7 Input validation, CSV, JSON Level 1: projects 09–15 CSV reader handles malformed input
8 Midterm — practical exam (timed project) Midterm project Working solution under time pressure
9 Data structures, cleaning, error handling Level 2: projects 01–08 Data cleaning pipeline
10 Level 2 completion + Module 01 (Web Scraping) intro Level 2: projects 09–15 Gate B: Resilient data tools
11 Packages, logging, TDD Level 3: projects 01–08 Package with tests
12 Level 3 completion + Module 02 or 03 Level 3: projects 09–15 CLI tool or API client
13 Schema validation, data contracts Level 4: projects 01–08 Schema-validated pipeline
14 Level 4 completion, begin Level 5 Level 4: projects 09–15 Transformation pipeline
15 Level 5 — scheduling, monitoring, resilience Level 5: projects 01–10 Retry-enabled system
16 Final project + oral defense Capstone selection Gate C or D

10-Week Bootcamp (Intensive)

Week Content Pace
1 Docs 00–04, Level 00, Level 0 (projects 01–08) Foundations blitz
2 Level 0 (09–15), Level 1 (01–08) Testing + validation
3 Level 1 (09–15), Level 2 (01–08) Data structures
4 Level 2 (09–15), Level 3 (01–08) Packages + TDD
5 Level 3 (09–15), Module 01 or 03 Midpoint check
6 Level 4 (01–10) Pipelines
7 Level 4 (11–15), Level 5 (01–08) Operations
8 Level 5 (09–15), Module 04 or 07 Applied skills
9 Level 6 (selected projects) SQL + ETL
10 Capstone week — final project + presentations Demo day

Weekend Workshop (2 Days)

Focus on Level 00 and Level 0 only. Goal: participants leave with Python installed, a working script, and a passing test.

Session Duration Content
Saturday AM 3 hours Setup, Computer Literacy Primer, exercises 01–08
Saturday PM 3 hours Foundations intro, Level 0 projects 01–05
Sunday AM 3 hours Level 0 projects 06–10, debugging practice
Sunday PM 3 hours Level 0 projects 11–15, first test, wrap-up

Fork Instructions for Teachers

Setting Up Your Class Repository

  1. Fork the repository on GitHub
  2. Enable GitHub Discussions on your fork for student Q&A
  3. Create a branch for your semester: fall-2026, spring-2027, etc.
  4. Customize CLAUDE.md with your learner context if using AI tutoring
  5. Optionally remove levels beyond your course scope to reduce student confusion

Per-Student Setup

# Students fork YOUR fork (not the upstream repo)
git clone https://github.com/YOUR_ORG/learn.python.git
cd learn.python
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

Collecting Assignments

Option A — Pull Requests: Students submit projects as PRs to your fork. Review inline.

Option B — Branch per student: Each student works on their own branch. You review branches.

Option C — Separate repos: Students clone the curriculum, work locally, push to their own GitHub. You review by visiting their repos.

Option D — GitHub Classroom (see section below).

Recommendation: Option A for small classes (under 20), Option D for classes with automated grading needs, Option C for larger classes without GitHub Classroom.

Using GitHub Classroom

GitHub Classroom automates repository creation, assignment distribution, and grading. It works well with this curriculum because every project already has tests.

Initial Setup

  1. Create a GitHub organization for your course (e.g., python-fall-2026)
  2. Go to classroom.github.com and create a classroom linked to your organization
  3. Add your student roster (manually, via CSV, or via LMS integration)

Creating Assignments From Curriculum Projects

Each project directory can become a GitHub Classroom assignment:

  1. Fork the learn.python repository to your organization
  2. In GitHub Classroom, create a new assignment
  3. Set the template repository to your fork
  4. Under "Starter code," point to the specific project directory path
  5. Enable autograding and add a test command:
# For Level 0+ projects with pytest tests:
python -m pytest tests/ -v

# For Level 00 exercises (no tests):
python exercise.py

Autograding Configuration

For projects with tests, add this autograding step in GitHub Classroom:

Field Value
Test name pytest
Setup command pip install pytest
Run command python -m pytest tests/ -v
Timeout 60 seconds
Points 10 (adjust as needed)

For code quality checks, add a second grading step:

Field Value
Test name ruff
Setup command pip install ruff
Run command ruff check .
Timeout 30 seconds
Points 5

Tracking Student Progress

GitHub Classroom provides a dashboard showing:

  • Which students have accepted the assignment
  • Whether tests pass or fail
  • Most recent commit timestamp

Combine this with the curriculum's built-in PROGRESS.md for a comprehensive view of each student's journey.

Tips for GitHub Classroom

  • Create one assignment per project (not per level) to get granular progress data
  • Use group assignments for the capstone projects and expansion modules
  • Set deadlines that align with your semester mapping above
  • Enable "feedback pull requests" so you can leave inline code review comments

Assessment Materials

Grading Rubrics by Level

Level 00 — Absolute Beginner (Pass/Fail)

Criterion Pass Fail
Script runs without errors Yes No
Output matches expected Yes No
Code was written by student (not copied) Evidence of iteration Exact match to solution

Levels 0–2 — Foundations (4-Point Scale)

Points Criterion
4 All tests pass, code is clean, edge cases handled, notes.md filled in
3 All tests pass, code works but could be cleaner
2 Most tests pass, student attempted all parts
1 Partial attempt, some tests pass
0 Not submitted or clearly copied

Levels 3–5 — Intermediate (4-Point Scale + Code Review)

Points Criterion
4 Tests pass, follows package conventions, logging present, TDD evidence
3 Tests pass, mostly follows conventions
2 Functional but conventions not followed
1 Partial, some functionality works

Add 1 bonus point for: meaningful git history (multiple commits showing iteration).

Levels 6–10 — Advanced (Rubric + Oral Defense)

Component Weight Criteria
Code quality 30% Tests pass, types used, error handling, documentation
Architecture 30% Clear separation of concerns, appropriate patterns
Oral defense 25% Can explain design decisions, tradeoffs, failure modes
Documentation 15% README, notes.md, architecture decisions recorded

Grading Alter/Break/Fix/Explain Exercises

Many projects include TRY_THIS.md or notes.md prompts that ask students to alter, break, fix, or explain code. Grade these on understanding, not correctness:

Exercise Type What to Look For
Alter Did the student change the right thing? Does their modification work?
Break Did the student identify a real failure mode? Can they explain why it breaks?
Fix Did the student diagnose the root cause, not just suppress the symptom?
Explain Can the student describe what the code does in their own words?

Award full credit for thoughtful wrong answers that show reasoning. Deduct for correct answers that show no understanding (copied from AI without comprehension).

Suggested Assignment Structure

Weekly Cadence

Day Activity
Monday Introduce concept (lecture or guided reading of concept doc)
Tuesday In-class coding: first 2–3 projects from the level
Wednesday Quiz on the concept (use the built-in quizzes)
Thursday Independent project work (remaining projects)
Friday Code review session — students present one project to a partner

Project Submission Checklist (Give to Students)

Before submitting a project, verify:

  • python -m pytest tests/ passes
  • ruff check . has no errors
  • notes.md has your observations (what you learned, what was hard)
  • Code has meaningful variable names (not x, y, z)
  • You can explain every line of your code

Capstone Projects

At the end of each major section, assign a capstone that combines skills:

After Level Capstone Suggestion Duration
2 Build a CSV data cleaner that handles 3 types of malformed input 1 week
5 Build a monitoring tool that polls an API and logs anomalies 2 weeks
7 Build an ETL pipeline with caching, retry, and observability 2 weeks
10 Production-grade system with tests, docs, Docker, and deployment 3 weeks

Group Project Ideas Using Expansion Modules

The expansion modules work well as group projects because they involve multiple components that can be divided among team members.

Module Group Size Division of Work Duration
01: Web Scraping 2-3 One student builds the scraper, one handles data cleaning, one writes CSV export 2 weeks
02: CLI Tools 2-3 Each student builds a different subcommand for the same CLI application 2 weeks
03: REST APIs 2-3 One student handles authentication, one builds the client, one writes retry logic 2 weeks
04: FastAPI Web 3-4 One builds endpoints, one handles auth/JWT, one writes tests, one does Pydantic models 3 weeks
06: Databases & ORM 2-3 One designs the schema, one writes queries, one handles migrations 2 weeks
07: Data Analysis 3-4 Each student analyzes a different aspect of the dataset, combine into a report 2 weeks
09: Docker & Deployment 2-3 One writes the Dockerfile, one configures docker-compose, one sets up CI/CD 2 weeks
10: Django Full-Stack 4-5 Models, views, templates, API (DRF), and tests split across the team 4 weeks

Group Project Guidelines (Give to Students)

  1. Use git branches. Each team member works on their own branch. Merge to main via pull requests reviewed by another team member.
  2. Write tests for your component. If you build the scraper, write tests for the scraper. Your teammates depend on your code working.
  3. Document interfaces. If another team member's code calls your function, write a clear docstring explaining inputs and outputs.
  4. Present together. Each member explains their component. The team demonstrates the integrated system.

Tracking Student Progress

Using PROGRESS.md

Each student maintains a PROGRESS.md file in their fork. This file tracks completed projects, quiz scores, and current position. Have students commit updates to this file after each session.

Progress Check Schedule

Week Check What to Look For
3 First check All Level 00 exercises done, at least 3 Level 0 projects started
5 Gate A Level 0 complete, first passing test confirmed
8 Midterm Levels 0-1 complete, midterm project submitted
10 Gate B Level 2 complete, data cleaning pipeline works
14 Pre-final Levels 0-4 complete, expansion module started
16 Final Capstone submitted and defended

Handling Different Skill Levels

Student Profile Adaptation
Complete beginner (never typed code) Let them spend 2 weeks on Level 00 instead of 1. Pair with a patient partner.
Knows another language Point them to Fast Track. They can skip Level 00 and start at Level 0.
Advanced student (already knows Python) Assign expansion modules as primary work. Have them mentor beginners.
Struggling student Reduce project count per level (do 8 of 15 instead of all 15). Focus on understanding, not completion.
Student with accessibility needs See Accessibility Guide. Provide extra time and alternative assessment options.

Classroom Tips

For Students With No Tech Background

The Level 00 exercises assume zero knowledge. Let students spend a full week here if needed. Rushing past this level creates compounding confusion later.

For Mixed-Skill Classes

  • Advanced students: assign expansion modules as bonus work
  • Struggling students: pair with advanced students for code review sessions
  • Everyone benefits from explaining code out loud — make this a regular activity

Common Student Mistakes

Mistake How to Address
Copying code without understanding Require oral explanations of submitted code
Skipping tests Make test passage a hard requirement for submission
Not reading error messages First week: practice reading tracebacks as a class exercise
Fear of breaking things Explicitly assign "break this code" exercises
Trying to memorize syntax Emphasize that looking things up is normal and expected

Using AI Tools in Your Classroom

This curriculum includes AI tutoring guidelines (see CLAUDE.md). If you allow AI tools:

  1. Set clear boundaries: AI can explain concepts, not write solutions
  2. Require students to explain AI-suggested code in their own words
  3. Use the "predict before running" technique: ask students what code will do before executing
  4. Grade understanding, not just output

If you prohibit AI tools, the curriculum works equally well — every concept is explained in the docs, and the project READMEs provide sufficient guidance.

Support

Questions about using this curriculum in a classroom? Open an issue on GitHub with the teaching label.

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