TECHNOLOGY-12 · Undergraduate · 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 | Human-Computer Interaction and Digital Media
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Sign in to save this learning pathPractical outcome: Test a paper prototype with a consent-based observation plan.
Explain mental models using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Explain affordances using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Explain feedback design using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Explain error prevention using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Use the first concepts from this section. Test a paper prototype with a consent-based observation plan. 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 task analysis using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Explain usability testing using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Explain inclusive design using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Explain assistive technology using a visual and one worked real-world example. Context: Test a paper prototype with a consent-based observation plan. End with a prediction or check-for-understanding.
Test a paper prototype with a consent-based observation plan. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Mental models, Affordances, Feedback design and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare image and audio encodings for a small learning asset.
Explain raster versus vector using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Explain colour spaces using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Explain image compression using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Explain audio sampling using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare image and audio encodings for a small learning asset. 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 video frame rates using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Explain video codecs using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Explain captions using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Explain media licensing using a visual and one worked real-world example. Context: Compare image and audio encodings for a small learning asset. End with a prediction or check-for-understanding.
Compare image and audio encodings for a small learning asset. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Raster versus vector, Colour spaces, Image compression and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Explain how a simple scene becomes pixels.
Explain coordinate systems using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain transform pipelines using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain projection using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain rasterisation using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Use the first concepts from this section. Explain how a simple scene becomes pixels. 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 shading using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain animation curves using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain interaction loops using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain rendering performance using a visual and one worked real-world example. Context: Explain how a simple scene becomes pixels. End with a prediction or check-for-understanding.
Explain how a simple scene becomes pixels. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Coordinate systems, Transform pipelines, Projection and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Explain how a simple scene becomes pixels. 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 Human-Computer Interaction and Digital Media, 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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