Algorithms for patterns
Computational thinking on real inquiry data
What pattern in our inquiry data can an algorithm reveal — and when does it mislead us?
Technology inquiry
Algorithms for patterns
Computational thinking on real inquiry data
Digital Technologies · Computational thinking · Algorithms
Wero
What pattern in our inquiry data can an algorithm reveal — and when does it mislead us?
First step
Define inputs/outputs; write pseudocode; test empty, typical, and edge cases before polishing code.
What you will show
Test-case table with pass/fail evidence and honest failure modes documented.
Local place context
What decision or pattern are you automating — and how will you test fairness?
Digital Technologies · Computational thinking · Algorithms
First step
Define inputs/outputs; write pseudocode; test empty, typical, and edge cases before polishing code.
Expected outcome
Test-case table with pass/fail evidence and honest failure modes documented.
You will design, code, test, and visualise science data — documenting each step so others can trust your process. Whether your algorithm correctly processes a real science dataset and what debugging reveals. Design documents, trace tables, code commits, and visualisation output.
Five ways you could investigate
Pick one to start — or write your own question. The AI mentor supports you gently inside your investigation.
Idea 1
Sort algorithm fairness
Does your sort treat equal inputs consistently?
Start with this question →Idea 2
Edge case hunt
What input breaks your program — and how do you handle it?
Start with this question →Idea 3
Efficiency comparison
When does a simple loop beat a clever trick for your data size?
Start with this question →Idea 4
Test data design
Did you test empty, single, and messy inputs on purpose?
Start with this question →Idea 5
Output vs expectation
Where do actual outputs differ from predicted — why?
Start with this question →
Five things you could build
Fabrication ideas linked to makerspace tools — 3D print, laser cut, Arduino, data products, and more.
Build 1
Flowchart poster
Show algorithm steps tied to your test cases.
Open in outcome selector →Build 2
Test case table
Publish inputs, expected, actual, pass/fail.
Open in outcome selector →Build 3
Debug log infographic
Visualise where the program spent time or failed.
Open in outcome selector →Build 4
Engraved desk plaque
Vinyl-cut team name + challenge title for showcase.
Open in outcome selector →Build 5
Physical sorting demo
Laser-cut tiles to demonstrate your algorithm live.
Open in outcome selector →
AI mentor (inside your investigation)
No separate mentor page — support appears in your investigation workspace. It starts gentle: short prompts about your research context, data, and analysis. You or your teacher can turn assistance off for unassisted work, or request more help when you need it. It also guides fabrication choices tied to your evidence.