Assessment method: Examination, end of year
Assessment medium: Online digital examination
Candidates will be required to respond in short and /or extended answers (800–1500 words
in total) to questions relating to their choice of ONE of the following computer science
concepts:
artificial intelligence
encryption
For 2025, questions on impacts will focus on ethical issues and future-proofing.
For encryption, questions may cover any of the following:
AES (Advanced Encryption Standard)
privacy
remote garage door openers
SHA-256 (Secure Hash Algorithm)
the key exchange problem
uses in healthcare.
Achieved – analyse/explain:
identifying the computer science concept
providing details of how the concept is used, is implemented, or occurs
explaining how the concept has been or could be applied to address an opportunity
explaining relevant mechanisms that shape the concept.
Using information, including research and/or classwork that they have previously undertaken, students identify the computer science concept chosen from the achievement standard. They provide clarity by giving a detailed account of how the concept is used, is implemented, or occurs. They explain how concept has been or could be applied to address an opportunity. Students will explain relevant mechanisms that shape the concept. For example, how the concept of complexity and tractability can be used to more efficiently pack bins and explaining the ‘greedy approximation algorithm’ to help solve the bin packing problem with some efficiency.
Merit – analyse in depth:
The student explains the impact of the computer science concept is explained. For example: the ethical issues relevant to artificial intelligence used in self-driving cars and the potential for the loss of human life.
Excellence – comprehensively analyse:
Students provide insightful discussion on the key issues related to the computer science concept and provide examples to illustrate their conclusions supported by research.
For example: in a discussion about artificial intelligence, the student discusses the human impact of self-driving trucks on employment and the subsequent social issues that might be caused, supported by examples and research.