TECHNOLOGY-04 · Secondary · ENGLISH · PLANNED
Follow a real task through a system, then design and troubleshoot it.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Technology | Programming and Computer Science Foundations
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Sign in to save this learning pathPractical outcome: Turn a simple daily routine into testable pseudocode.
Explain decomposition using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Explain abstraction using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Explain algorithms using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Explain variables using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Use the first concepts from this section. Turn a simple daily routine into testable pseudocode. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain conditions using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Explain loops using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Explain functions using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Explain tracing state using a visual and one worked real-world example. Context: Turn a simple daily routine into testable pseudocode. End with a prediction or check-for-understanding.
Turn a simple daily routine into testable pseudocode. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Decomposition, Abstraction, Algorithms and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Choose a structure for a library lending model.
Explain arrays using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Explain linked lists using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Explain stacks using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Explain queues using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Use the first concepts from this section. Choose a structure for a library lending model. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain hash tables using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Explain trees using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Explain graphs using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Explain choosing representations using a visual and one worked real-world example. Context: Choose a structure for a library lending model. End with a prediction or check-for-understanding.
Choose a structure for a library lending model. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Arrays, Linked lists, Stacks and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare search strategies using counted operations.
Explain linear search using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Explain binary search using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Explain sorting by comparison using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Explain recursion using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare search strategies using counted operations. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain big-o notation using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Explain time-space tradeoffs using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Explain correctness invariants using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Explain test cases using a visual and one worked real-world example. Context: Compare search strategies using counted operations. End with a prediction or check-for-understanding.
Compare search strategies using counted operations. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Linear search, Binary search, Sorting by comparison and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Compare search strategies using counted operations. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Programming and Computer Science Foundations, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
System diagrams, design reviews and bounded prototypes. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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