How the courses map to the New Zealand Curriculum

Our Python, Pygame and Pygame Zero courses are built around the refreshed New Zealand Curriculum — the Year 10 Computer Science strand and Year 11 Digital Technologies | Hangarau Matihiko. Below is the alignment, element by element, so you can see exactly where each course fits before you show a colleague or your HOD.

Based on the October 2025 (Year 10) and June 2026 (Year 11) curriculum drafts. As the curriculum is finalised we keep this mapping in step.

Year 10

Technology · Computer Science strand

The New Zealand Curriculum — Technology (Years 9–10). A seven-week Pygame Zero course that takes a beginner from their first coloured window to a finished, tested game, covering the Computer Science strand’s programming practices: functions with parameters and return, collections, Boolean logic, and testing.

  1. Week 1 Window, colour, coordinates, variables

    BuildsBeginner-friendly language; variables; sequence

  2. Week 2 Sprites & movement — actors and the game loop

    BuildsImplementing; variables for position; abstraction; the draw/update loop

  3. Week 3 Selection & input — if/elif/else, keyboard & mouse

    BuildsSelection; sequence + selection; Boolean reasoning (intro)

  4. Week 4 Iteration & lists — loops and collections

    BuildsIteration; collections (lists); comparing simple solutions

  5. Week 5 Functions with parameters & return

    BuildsModularity (functions with parameters & return); abstraction & decomposition

  6. Week 6 Capstone — build a real game

    BuildsIntegrates all of the above; decomposition; designing, testing & debugging

  7. Week 7 Polish, Boolean stretch & testing (optional)

    BuildsBoolean logic / truth tables (and / or / not); table-of-cases testing

Year 11

Digital Technologies | Hangarau Matihiko

The refreshed curriculum, Phase 5 (Years 11–13). One complete strand taught across the year with Python and Pygame Zero — a game is the draft’s “Interactive Experience”, and satisfies both the Computational Thinking and the Digital Solution Project elements in one coherent context. Feeds Years 12–13 Computer Science.

1 · Computational thinking & data representation

  • Computational thinking foundations

    What the curriculum expects

    Abstraction, decomposition, algorithmic thinking, pattern recognition and evaluation. Analyse a problem’s purpose, inputs, outputs, constraints, users and context.

    How the course delivers it

    Decompose a simple game (e.g. Pong) into components; read working code and predict its output.

  • Algorithms & planning

    What the curriculum expects

    Algorithms use sequence, selection and iteration, represented in plain English, pseudocode, flowcharts or code. Trace, check and refine before coding.

    How the course delivers it

    Flowchart the game loop; complete trace tables; order an algorithm with Parsons problems.

  • Python core — sequence, selection, iteration, variables, I/O

    What the curriculum expects

    Variables, input()/print(), if/elif/else, for and while loops; data types int, float, str, bool. Test with expected, boundary and invalid inputs.

    How the course delivers it

    PRIMM in Pygame Zero — predict, run & read, trace, order, complete, modify, then make; plus debugging and retrieval quizzes.

  • Functions, data types & modularity

    What the curriculum expects

    def with parameters and return; simple modularity; lists. Meaningful names, indentation and comments make programs maintainable.

    How the course delivers it

    Worked examples with subgoals, code-completion and refactoring; write your own draw_enemy(); explain code in plain English.

  • Data representation

    What the curriculum expects

    Represent information as binary, decimal, hexadecimal, ASCII text and RGB colour values.

    How the course delivers it

    Tie it to Pygame colours and sprites — RGB to bytes with bin(), int('1011', 2), ord() and chr(); predict-the-output tasks.

  • Testing, debugging & program comprehension

    What the curriculum expects

    Program comprehension across levels of abstraction; debug with test data, trace outputs and error messages; compare actual vs intended behaviour.

    How the course delivers it

    Debugging challenges, spot-the-difference, and table-of-cases testing.

2 · Digital solution project — define & design

  • Investigate & define

    What the curriculum expects

    A manageable project has a purpose, users, scope, requirements, constraints and responsible-practice considerations. Interactive experiences draw on HCI/UX and game design.

    How the course delivers it

    Investigate two or three Pygame Zero games; write a game brief; identify the target player, plus a responsible-practice plan (accessibility, privacy, attribution, Te Tiriti).

  • Design & refine

    What the curriculum expects

    Generate and communicate design ideas, then refine through modelling, testing and feedback. A final design gives enough detail to guide development.

    How the course delivers it

    Storyboard the screens, wireframe the HUD, and prototype the core mechanic in the editor before a feedback round.

3 · Digital solution project — develop, release & evaluate

  • Iterative development

    What the curriculum expects

    Build components, test, improve and combine them; maintain an organised workflow of files, versions, assets and backups.

    How the course delivers it

    Build up from steps to an independent game — movement, input, collision, score, lives and win/lose — with a debugging harness and version save-points.

  • Test, feedback & release

    What the curriculum expects

    Test functionality, usability and playability; gather user feedback; release an outcome that follows conventions and responsible practice.

    How the course delivers it

    Run peer playtesting rounds and table-of-cases tests, then fix and release the game.

  • Evaluate

    What the curriculum expects

    Judge fitness for purpose against the goal, requirements, testing and feedback; identify strengths, limitations and next steps.

    How the course delivers it

    Write a structured, evidence-based evaluation report against the success criteria.

4 · Artificial intelligence & emerging technologies

  • What AI is; machine learning in familiar tools

    What the curriculum expects

    AI uses algorithms, data, models and pattern recognition; machine learning improves from examples, unlike fixed-rule automation.

    How the course delivers it

    Try a Teachable Machine demo, classify inputs, and contrast AI with fixed rules (tied to “game AI”).

  • Data, bias & prompting

    What the curriculum expects

    AI outputs are shaped by data, model and prompt; data quality and labelling affect reliability and bias. Effective prompting is purposeful and iterative.

    How the course delivers it

    Prompt-engineering tasks and a bias investigation; evaluate generative AI (including any AI-made sprites or code, with a declaration).

  • Ethics & impact

    What the curriculum expects

    Generative AI outputs are not automatically accurate, neutral, ethical or owned. Weigh privacy, fairness, copyright, authorship and cultural representation.

    How the course delivers it

    Write an impact analysis on an AI or emerging technology, and declare any AI used in the project.

See it in your own classroom

Set up a course and hand these activities to your class — free to start, all in the browser.

Start for free Back to home