11–12Enterprise Computing 11–12 Syllabus
The new Enterprise Computing 11–12 Syllabus (2022) is to be taught from 2024.
2023
- Plan and prepare to teach the new syllabus
2024, Term 1
- Start teaching new syllabus for Year 11
- Start implementing new Year 11 school-based assessment requirements
- Continue to teach the Information Processes and Technology Stage 6 Syllabus (2009) for Year 12
2024, Term 4
- Start teaching new syllabus for Year 12
- Start implementing new Year 12 school-based assessment requirements
2025
- First HSC examination for new syllabus
Content
Year 12
Explore the difference between quantitative and qualitative data
Determine which data types are used to represent quantitative and qualitative data
Explore nominal, ordinal, interval and ratio levels of measurement applied to data
Investigate data sampling, including manual and computerised methods of active and passive data collection
Assess the relevance, accuracy, validity and reliability of primary and secondary data
Investigate how informatics supports the development of a deeper understanding of data
Interpret and present data using graphs, infographics, dashboards, reports, network diagrams and maps
Investigate structured and unstructured datasets
Explore the use of likes, emoticons and memes as forms of alternative data as sources of feedback
Examine the impact of errors, uncertainty and limitations in data
Including:- data sources
- raw data versus processed data
- data bias
Explain how blockchain technology is used to manage and verify data
Including:- online voting
- online identities
- tracking items of value
- recordkeeping
Examine software features that affect the privacy and security of data
Including:- autofill
- public or private connections
- checkbox
- terms of agreement
Explore the use of big data and data warehousing, considering volume, variety and velocity
Explore the risks and benefits of data mining
Analyse the impact of data scale
Including:- volume of raw data
- storage
- real-time and continuous streaming
- opportunities for machine learning (ML)
- changes in human behaviour
- ethical implications, including digital footprints
Evaluate the effectiveness of different methods for data storage
Including:- local storage
- cloud storage
- portable storage media
- data warehouses
Investigate the ethical use of data for social or enterprise research purposes
Explore social, ethical and legal issues associated with using data
Including:- bias
- accuracy of the collected data
- metadata
- copyright and acknowledgement of source data
- intellectual property and respect for ownership, including Indigenous Cultural and Intellectual Property (ICIP)
- permissions, rights and privacy of individuals, including cultural responsibility
- security
Investigate the legal issues surrounding data collection and handling
Including:- legislation
- authorities responsible for data protection
- data sovereignty of Aboriginal and Torres Strait Islander Peoples
Investigate the influence of curated and communicated data on social behaviour
Including:- data literacy
- timeframes
- signals impacting on behaviour
- data swamps
- educating users
Summarise data using a spreadsheet
Collate information using spreadsheet analysis features, including charts, statistical analysis and what-if modelling
Filter, group and sort data in a spreadsheet to process and display information
Including:- linking multiple sheets to extract data and create summaries
- applying conditional formatting
- making data comparisons
- designing forms and reports
Apply spreadsheet analysis features to develop a data dashboard
Including:- graphs
- pivot tables and slicers
Develop a flat-file database
Apply computational thinking to design a relational database with appropriate user views
Including:- develop a data dictionary
- linking tables via key fields
- sort and search data, including using structured query language (SQL)
- using forms and reports
Explore how machine learning and statistical modelling are used in data analytics to analyse big data, and as a prediction tool