The brief
Playing a game feels immediate. Understanding its rules requires a different kind of attention.
The curriculum helps learners connect familiar play experiences with precise instructions, inputs, events, variables and feedback. It introduces AI-assisted creation alongside manual building, with the expectation that learners can explain the game’s logic rather than only produce an attractive result.
Learning approach
Experience the rule before coding it.
The opening lesson combines a four-corners introduction, a precise-instructions activity, an overview of the pathway, 40 minutes of guided building and a gallery reflection. The first digital success is a sprite that moves with buttons. Fast finishers add a “stay in screen” constraint and test edge behavior: a small extension that deepens understanding without changing the task for everyone.
Reconstructed curriculum map. Sequence and planned scope are grounded in the supplied documents.
Try an example
Give the computer precise instructions
Move from the top-left square to the target at the bottom-right. Avoid the blocked square. Build a sequence, then run it.
More about the design
Discovery & objectives
The audience is Grade 7 learners without prior programming experience. The first lesson makes algorithmic precision physical through a “Human Computer” activity before moving to MakeCode Arcade. This sequence addresses an explicit novice-learning challenge; no learner interviews or classroom observations were supplied.
Intended learning performance
Learners should be able to build an interactive game, explain the relationship between input and response, test the rules and use feedback to improve play.
Design focus
Programming logic, experimentation, playtesting
Development & storyboard
MakeCode Arcade provides the initial programming environment. Rosebud AI and Canva appear in the broader curriculum for assisted development and assets. The final project is designed around a small complete game, a prototype proving its core mechanic, a playable link or QR code and an explanation of AI’s contribution.
The original work available for review is the bilingual syllabus and five-page first-session plan. The grid exercise below is newly built for this portfolio. It translates precise-instruction learning into a keyboard-accessible browser activity; it is not a student game or a MakeCode export.
Screen-level storyboard
New portfolio specification for the demonstration below.
| Screen | Content | Learner action | Feedback / rationale |
|---|---|---|---|
| 01 · Plan | A marker must reach a target without crossing a blocked cell. | Build a sequence of directional commands. | The rule and target are visible before action. |
| 02 · Execute | The commands run in order. | Run the sequence. | Show success or the first invalid move. |
| 03 · Iterate | The learner revises the sequence. | Clear commands or reset and try again. | Connect one changed instruction to its effect. |
Multilingual design
Shared technical concepts are introduced in Hebrew and Arabic, while tool names remain recognizable. The original Arabic controls presentation referenced by the lesson was not supplied.
משחקים דיגיטליים
الألعاب الرقمية
קלטים, אירועים ותגובה
المدخلات، الأحداث والاستجابة
Short terms from the paired source documents, newly typeset. Latin tool names use their original spelling.
Evaluation & next steps
The planned checks include visible sprite movement, gallery interaction, reflection, playtesting, difficulty balancing and classification of feedback. The syllabus asks learners to select improvements and explain decisions. No actual playtest notes, completed games, scoring rubric or learning-gain data were supplied.
A proposed next iteration
A proposed improvement is to define observable criteria for each milestone: a controllable sprite, a consistent win condition and a player who can start without verbal help. A short test script would help separate interface confusion from game-balance issues.
Skills & sources
Source basis
Grade 7 digital-games syllabi, Hebrew and Arabic, pp. 1–3; first lesson, pp. 1–5.
Contribution is based on Saja’s identification of these materials as her work. Exact collaborator boundaries and implementation dates remain part of the private review. Curriculum tool references describe the planned learning environment, not a claim of production expertise in every tool.