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Unpack the Curriculum

Understand the Australian Curriculum: Digital Technologies

Australian Digital Technologies Curriculum:

Data interpretation

The process of extracting meaning from data. Methods include modelling, statistical analysis, and visualisation.
Data interpretation
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F-2

Collect, explore and sort data, and use digital systems to present the data creatively (ACTDIP003)

3-4

Collect, access and present different types of data using simple software to create information and solve problems (ACTDIP009)

5-6

Acquire, store and validate different types of data, and use a range of software to interpret and visualise data to create information (ACTDIP016)

7-8

Analyse and visualise data using a range of software to create information, and use structured data to model objects or events (ACTDIP026)

9-10

Analyse and visualise data to create information and address complex problems, and model processes, entities and their relationships using structured data (ACTDIP037)
Organise data

Organise data explores the ways we order, sort and arrange data to assist us with interpretation in different contexts.

F-2

Organise data

Organise data by classifying, grouping and sorting.

Students explore data by classifying, grouping, and sorting (e.g. ordering students by height; grouping photos of pets by their type).

3-4

Interpret data

Organise data to answer questions.

Students answer simple questions by classifying, grouping, and sorting data (e.g. what is the most common car colour? how many female convicts were in the First Fleet?)

5-6

Interpret data

Organise data to answer questions.

Students reveal patterns by classifying, grouping, and sorting data (e.g. a team rarely wins when playing away) and make predictions (e.g. when playing at home with a big crowd the team is more likely to win).

7-8

Model data

Model objects and events in terms of their attributes.

Students model objects and events (e.g. products in the canteen and the sale of those products) as structured data i.e. the attributes relevant to the task (e.g. product name, price, quantity, nutritional value).

9-10

Analyse data

Summarise data and infer relationships and trends to create information.

Students summarise data, its attributes, and their relationships (e.g. electorates and their demographics) and identify trends and outliers (e.g. national swings and exceptions) to draw conclusions and make predictions (e.g. predicting the election outcome).

Model data

Model objects and events in terms of their attributes.

Students model entities and processes (e.g. a movie, a user, and a user's movie review), their attributes (e.g. a movie has a title, genre, and release date), and the relationships between them (e.g. a review has a movie, a user, and their rating and comments).
Visualise data

Visualise data describes the many ways we present data in its raw and summarised form for communication and further analysis.

F-2

Visualise data

Present data in various ways to summarise data.

Students present data in different ways to answer simple questions (e.g. use a table or pictograph to discover the most common pets at home).

3-4

Visualise data

Present data in various ways to summarise data.

Students present acquired data in different ways to answer questions (e.g. graph plant height to determine if sunlight increases growth).

5-6

Visualise data

Present data to reveal patterns, trends, outliers, or other information.

Students visualise data using graphs (e.g. a scatter plot of student race times with age) and diagrams (e.g. a cricket wagon wheel showing a player's scoring shots) to reveal trends.

7-8

Visualise data

Present data to reveal patterns, trends, outliers, or other information.

Students visualise multi-dimensional data by choosing appropriate graphs (e.g. a scatter plot of food prices and sales, coloured by sugar content), diagrams (e.g. a social network diagram), and maps (e.g. crime rates by location) to reveal trends, outliers, or other information.

9-10

Visualise data

Present data to reveal patterns, trends, outliers, or other information.

Students develop interactive visualisations (e.g. population, life expectancy and fertility rate in motion charts) for exploring complex data.