11–12Software Engineering 11–12 Syllabus

Implementation from 2024

Expand for detailed implementation advice

Content

Year 11

Year 12

Teaching advice

Software automation

Algorithms in machine learning

Investigate how machine learning (ML) supports automation through the use of DevOps, robotic process automation (RPA) and business process automation (BPA)
Distinguish between artificial intelligence (AI) and ML
Explore models of training ML
Including:
- supervised learning
- unsupervised learning
- semi-supervised learning
- reinforcement learning
Investigate common applications of key ML algorithms
Including:
- data analysis and forecasting
- virtual personal assistants
- image recognition
Research models used by software engineers to design and analyse ML
Including:
- decision trees
- neural networks
Describe types of algorithms associated with ML
Including:
- linear regression
- logistic regression
- K-nearest neighbour

Programming for automation

Design, develop and apply ML regression models using an OOP to predict numeric values
Including:
- linear regression
- polynomial regression
- logistic regression
Apply neural network models using an OOP to make predictions

Significance and impact of ML and AI

Assess the impact of automation on the individual, society and the environment
Including:
- safety of workers
- people with disability
- the nature and skills required for employment
- production efficiency, waste and the environment
- the economy and distribution of wealth
Explore by implementation how patterns in human behaviour influence ML and AI software development
Including:
- psychological responses
- patterns related to acute stress response
- cultural protocols
- belief systems
Investigate the effect of human and dataset source bias in the development of ML and AI solutions

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