Classes 4-5 · Year one • Mera BasecampConcrete & visible

Make the machine do exactly what you meant.

Class 4 begins with one foundational idea: computers execute representations of our intentions. Students learn to make those representations precise, inspect what actually happened, debug deliberately, work with simple data and investigate why learned systems make different predictions.

“I can control and explain small computational systems.”

Who owns whatThe problem, structure and substantial conceptual scaffolding are supplied; the learner increasingly owns important logic choices, prediction, tracing, repair and explanation.

“I can follow what a computer does.”

“I can control, test, debug and explain a small computational system.”

What changes this year

Algorithms exist before programs: students represent the logic before they build.
Prediction before Run becomes a permanent habit.
Events, repetition, decisions and changing state become understandable system behavior—not just blocks.
Debugging becomes an evidence-led routine rather than random editing.
Testing begins with the idea that one successful run is weak evidence.
Data begins as records, attributes, questions and claims—not chart decoration.
Machine learning begins as training examples influencing model behavior, not as magic.
Students explain what they built, what changed and why they trust the result.

Under the visual surface

Try the thinking

A minute of Mera Basecamp thinking.

An illustrative interaction, not a lesson from the curriculum — but the reasoning is the reasoning.

Can you make the machine do exactly what you meant?

Interactive example

A computer cannot fill in the missing steps the way a person often does. Build a precise route, predict what happens, then run it.

Column 1, row 3 · facing North

Build the route

Your program is empty.

What they produce

Interactive visual systems
Written/visual algorithms
State/variable maps
Trace evidence
Debugging records
Simple data tables and visualizations
ML training-data investigations
How My System Works explanations

What you should be able to see

What your child intended the program to do
The algorithm or plan created before execution
A prediction or trace
A bug investigated and how the fix was verified
A simple data/model claim and the evidence behind it
A short explanation in the child's own technical language

Support matters. A heavily guided success is not reported as independent mastery.

Twelve Projects. One climb.

A Band is not a pile of unrelated activities. Each standard Band contains 12 Projects arranged so that capability is established, combined, transferred and increasingly owned.

P1–P4Establish / Acquire
P5–P8Combine / Extend
P9–P12Transfer / Own

Canonical Project example

Robot Rescue Mission

Canonical Project 1 example

Students move from vague mission goals to precise executable programs, prediction, tracing, deliberate debugging, testing, constraint reasoning, safe final execution, transfer and technical defense.

Where Mera Basecamp lands on each capability

These are Band end-states, not lesson titles. Follow any of them across all eight years in the Capability Telescope.

Algorithms & problem solving: Turn a clear goal into exact ordered instructions; compare simple solutions for correctness and clarity.
Programming & representation: Predict before Run, trace visible state and read/modify small visual programs.
Debugging: What did I expect? What happened? Check one thing. Change one thing. Run again.
Testing & reliability: Try more than one case and compare expected vs actual; learn that one successful run proves very little.
Data & evidence: Understand rows as observations, columns as attributes, make a simple visualization and ask what the data does not prove.
AI & machine learning: Notice that learned systems behave differently depending on the examples they learned from, and distinguish simple rules from learned behavior.
Systems & architecture: See a system as interacting parts with changing state; understand that an event/rule changes what is true now.
Technical judgment: A solution can work and still be unclear or unfair; ask who benefits and what evidence supports the claim.
Security, privacy & human-centered computing: Begin with simple users, permissions, user testing and the idea that computing choices affect people.
Explanation, transfer & technical defense: Explain what starts the program, what changes, what bug was found, how it was tested and what the data/model evidence shows.

Open the Capability Telescope

Questions parents ask about this Band

Is this just Scratch?

No. A visual medium is used because syntax is not the learning target yet. Students are already learning algorithms, state, prediction, debugging, testing, data, model behavior and technical explanation. The representation matures later; the computational habits begin now.

Why not start with Python immediately?

Because early syntax friction can hide the thinking we actually want to develop. AI Pathway first makes the computational model visible, then deliberately bridges those ideas into text in Lobuche.

What becomes possible next

What Mera Peak adds

Class 5 takes the same foundation and makes the systems richer: more state, interacting rules, reusable procedures, deliberate model training, stronger data reasoning and larger internal logic that the learner increasingly owns.

Climb to Mera Peak

See the whole climb

The whole climb