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Classes 6-7 · Year one • Lobuche BasecampAnalytical & representational

See the algorithm underneath every representation.

Class 6 is the deliberate bridge from visual making to text-based problem solving. The learner repeatedly sees the same computational idea as a diagram, pseudocode, blocks and code—so Python becomes a new notation for known reasoning, not a reset to zero.

“I can solve structured computational problems.”

Who owns whatProblem and structure are largely supplied; the learner owns substantial algorithmic pieces and becomes computationally bilingual.

  1. Classes 4–5 · Year oneMera Basecamp
  2. Classes 4–5 · Year twoMera Peak
  3. Classes 6–7 · Year oneLobuche Basecamp
  4. Classes 6–7 · Year twoLobuche Summit
  5. Classes 8–9 · Year oneAma Dablam Basecamp
  6. Classes 8–9 · Year twoAma Dablam Summit
  7. Classes 11–12 · Year onePumori Summit
  8. Classes 11–12 · Year twoCho Oyu Summit

“I can reason about computational logic mainly through visual representations.”

→

“I can represent, read, test and implement the same structured logic across visual and text forms.”

What changes this year

  • Text code appears early through reading, prediction, modification, Parsons problems and repair.
  • State becomes an explicit mental model: current state + event/rule → new state.
  • Lists become the first serious collection structure; key-value thinking begins.
  • Functions/procedures turn decomposition into named responsibilities.
  • Algorithms are verified on inputs before being trusted.
  • Program comprehension becomes a formal strand, including AI-generated code critique.
  • Structured datasets become computational material rather than decoration.
  • Students compare explicit rules and simple learned models and reason about uncertainty.

The year of computational bilingualism

Try the thinking

A minute of Lobuche Basecamp thinking.

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

The algorithm is not the syntax.

Interactive example

Switch representations. The notation changes; the computational idea does not.

The taskFor every temperature reading, count it if it is below 0°C.

Hover, tap or tab through any line — the same idea lights up in every notation.

Natural language

Pseudocode

Block semantics

Python

Which line in Python corresponds to the decision in the pseudocode?

What they produce

  • Flowchart/pseudocode + matching text implementation
  • Small structured text programs
  • Test and debugging records
  • Small structured dataset analyses
  • Rule-based simulations
  • Simple ML experiments
  • Rule-vs-model comparisons
  • AI-generated-code repairs

What you should be able to see

  • ✓The algorithm before it became code
  • ✓A visual/text representation match
  • ✓A test that revealed a failure
  • ✓The learner's debugging hypothesis
  • ✓A small dataset question and visualization
  • ✓A rule-vs-model explanation
  • ✓AI-generated code the learner inspected and repaired

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.

  1. P1–P4Establish / Acquire
  2. P5–P8Combine / Extend
  3. P9–P12Transfer / Own
Illustrative synthesis example

Expedition Safety Advisor

A structured decision system combines state, rules, text code, simple algorithmic processing, a small dataset and a learned model. Students compare the rule-based and learned approaches rather than assuming the AI approach wins.

Design archetype: Build and Compare a Smart Decision System

Illustrative synthesis archetype from current Band design; not a guaranteed final Project title.

Where Lobuche 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.

AAlgorithms & problem solving
Move an algorithm across flowchart, pseudocode, blocks and text; verify it on given inputs and use simple search/filter/count/rank patterns.
BProgramming & representation
Recognize the same algorithm across natural language, diagrams, pseudocode, blocks and early text programs.
CDebugging
Use error messages, variable/state inspection and a repeatable expected → observed → localize → hypothesize → verify routine.
DTesting & reliability
Use structured validation and test the logic separately from the visual surface where useful.
EData & evidence
Work with small structured datasets; sort, filter, group, summarize and identify obvious quality problems.
FAI & machine learning
Explain rule-based vs data-driven behavior, experiment with simple learned models and treat AI output as uncertain.
GSystems & architecture
Reason about user → device/program → network/service → data → response and map problems into procedures/components.
HTechnical judgment
Compare algorithms, rule-based systems and simple learned models for task fit.
ISecurity, privacy & human-centered computing
Understand authentication/authorization, common privacy/security risks and why some data should not be shared with AI.
JExplanation, transfer & technical defense
Explain an algorithm before code, a debugging hypothesis, a rule-vs-model comparison and unfamiliar code repaired.
Open the Capability Telescope→
What becomes possible next

What Lobuche Summit adds

Class 7 removes more representation support. Text becomes normal, functions gain parameters/returns, algorithms become more explicit and the learner owns much more of the implementation.

Climb to Lobuche Summit
← What prepared them: Mera PeakSee the whole climb

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