This document provides an overview of key concepts related to teaching digital technologies, including computational thinking, systems thinking, design thinking, and futures thinking. It discusses important problems in the world like global warming, armed conflicts, and overpopulation that could be addressed through computational thinking. The document also outlines key concepts for different year levels, including creating interactive games, databases, and computer systems. It provides examples of concepts like algorithms, binary search, and the travelling salesman problem.

1. Teaching Digital Technologies
Technologies Education

2. 5:02

3. Systems Thinking
Computational Thinking
Design Thinking
Futures Thinking
Strategic Thinking

4. Systems Thinking
Computational Thinking
Design Thinking
Futures Thinking
Strategic Thinking
Solutions Thinking

5. Global Warming
Armed Conflicts
Food Scarcity
Clean Water
Ageing Population
Obesity
Overpopulation
Alternative Energy
Education
Health Care
Epidemics
Housing and Shelter
Big Problems / Ideas

6. Big Problems / Ideas
Futures Thinking

7. Computational Thinking
Analyse the problem

8. Computational Thinking
Analyse the problem
Collecting, managing and
analysing data about the
problem and solution

9. Computational Thinking
Analyse the problem
Systems Thinking

10. Computational Thinking
Creating a digital solution
• defining the problem
• designing solutions
• implementing a design
• evaluating the solution
• collaborating on and managing

11. Systems Thinking
Computational Thinking
Design Thinking
Futures Thinking
Strategic Thinking

12. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

13. Abstraction
The process of reducing complexity to formulate generalised
fundamental ideas or concepts removed from the specific
details or situation. For example, the idea that a cricket ball is a
sphere in the same way that a soccer ball is, or the concept that
data can be organised in records made up of fields irrespective
of whether the data are numbers, text, images or something else.

14. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

15. Data collection,
representation and
interpretation
The properties of data, how they are collected and represented,
and how they are interpreted in context to produce information.

16. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

17. Specification, algorithms and
implementation
Specification describes the process of defining and
communicating a problem precisely and clearly.

18. Specification, algorithms and
implementation
Algorithms describe the steps and decisions needed to solve a
problem.

19. Specification, algorithms and
implementation
Implementation of the algorithm using software or writing a
computer program.

20. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

21. Digital systems
Hardware and software (computer architecture and the
operating system), and networks and the internet (wireless,
mobile and wired networks and protocols).

22. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

23. Interactions and impacts
Interactions (people and digital systems, data and processes)
and impacts (sustainability and empowerment).

24. Digital Technologies
Intellectually challenging and engaging problems remain to be
understood and solved. The problems and solutions are limited
only by our own curiosity and creativity

25. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

26. Procedural Thinking

27. 0:52

28. Computational Thinking

29. 3:36

30. Computational Thinking
The curriculum is designed so that students will develop and use
increasingly sophisticated computational thinking skills, and
processes, techniques and digital systems to create solutions to
address specific problems, opportunities or needs.

31. Computational Thinking
Computational thinking is a process of recognising aspects of
computation in the world and being able to think logically,
algorithmically, recursively and abstractly. Students will also
apply procedural techniques and processing skills when
creating, communicating and sharing ideas and information, and
managing projects.

32. 3:43

33. Key Concepts

34. Abstraction
Abstraction, which underpins all content, particularly the
content descriptions relating to the concepts of data
representation and specification, algorithms and
implementation

35. Abstraction
Abstraction involves hiding details of an idea, problem or
solution that are not relevant, to focus on a manageable number
of aspects. Abstraction is a natural part of communication:
people rarely communicate every detail, because many details
are not relevant in a given context. The idea of abstraction can be
acquired from an early age. For example, when students are
asked how to make toast for breakfast, they do not mention all
steps explicitly, assuming that the listener is an intelligent
implementer of the abstract instructions.

36. Abstraction
Central to managing the complexity of information systems is
the ability to ‘temporarily ignore’ the internal details of the
subcomponents of larger specifications, algorithms, systems or
interactions. In digital systems, everything must be broken down
into simple instructions.

38. Data collection,
representation and
interpretation
Data collection (properties, sources and collection of data), data
representation (symbolism and separation) and data
interpretation (patterns and contexts)

39. Data collection,
representation and
interpretation
The concepts that are about data, focus on the properties of data,
how they are collected and represented, and how they are
interpreted in context to produce information. These concepts in
Digital Technologies build on a corresponding Statistics and
Probability strand in the Mathematics curriculum.

40. Data collection,
representation and
interpretation
The Digital Technologies curriculum provides a deeper
understanding of the nature of data and their representation, and
computational skills for interpreting data. The data concepts
provide rich opportunities for authentic data exploration in other
learning areas while developing data processing and
visualisation skills.

41. Data collection,
representation and
interpretation
Data collection describes the numerical, categorical and textual
facts measured, collected or calculated as the basis for creating
information and its binary representation in digital systems.

42. Data collection,
representation and
interpretation
Data collection is addressed in the processes and production
skills strand. Data representation describes how data are
represented and structured symbolically for storage and
communication, by people and in digital systems, and is
addressed in the knowledge and understanding strand.

43. Data collection,
representation and
interpretation
Data interpretation describes the processes of extracting
meaning from data and is addressed in the processes and
production strand.

44. Specification (descriptions and techniques), algorithms
(following and describing) and implementation (translating and
programming)
Specification, algorithms and
implementation

45. Specification, algorithms and
implementation
The concepts specification, algorithms and implementation
focus on the precise definition and communication of problems
and their solutions. This begins with the description of tasks and
concludes in the accurate definition of computational problems
and their algorithmic solutions. This concept draws from logic,
algebra and the language of mathematics, and can be related to
the scientific method of recording experiments in science.

46. Specification, algorithms and
implementation
Specification describes the process of defining and
communicating a problem precisely and clearly. For example,
explaining the need to direct a robot to move in a particular way.

47. Specification, algorithms and
implementation
An algorithm is a precise description of the steps and decisions
needed to solve a problem. Algorithms will need to be tested
before the final solution can be implemented. Anyone who has
followed or given instructions, or navigated using directions, has
used an algorithm.

48. Specification, algorithms and
implementation
These generic skills can be developed without programming. For
example, students can follow the steps within a recipe or
describe directions to locate items. Implementation describes
the automation of an algorithm, typically by using appropriate
software or writing a computer program. These concepts are
addressed in the processes and production skills strand.

49. Digital systems
Digital systems (hardware, software, and networks and the
internet)

50. Digital systems
The digital systems concept focuses on the components of
digital systems: hardware and software (computer architecture
and the operating system), and networks and the internet
(wireless, mobile and wired networks and protocols).

51. Interactions and impacts
Interactions (people and digital systems, data and processes)
and impacts (sustainability and empowerment).

52. Interactions and impacts
The interactions and impacts concepts focus on all aspects of
human interaction with and through information systems, and
the enormous potential for positive and negative economic,
environmental and social impacts enabled by these systems.
Interactions and impacts are addressed in the processes and
production skills strand.

53. Interactions and impacts
Interactions refers to all human interactions with information
systems, especially user interfaces and experiences, and
human–human interactions including communication and
collaboration facilitated by digital systems. This concept also
addresses methods for protecting stored and communicated
data and information.

54. Interactions and impacts
Impacts describes analysing and predicting the extent to which
personal, economic, environmental and social needs are met
through existing and emerging digital technologies; and
appreciating the transformative potential of digital technologies
in people’s lives. It also involves consideration of the
relationship between information systems and society and in
particular the ethical and legal obligations of individuals and
organisations regarding ownership and privacy of data and
information.

55. F-2
Identifying and explaining how common computer systems can
be used to solve problems
Creating algorithms to solve problems
Creating a classroom database to solve problems
How can computers make our lives easier?
Taking the roll Backing up work Forming Groups
Lunch orders Reading Lists Sports Day
Adding an absent student to a photo Working together on a story

56. 3-4
Creating an interactive guessing game to gather data on
environmental impact
Creating a computer system to monitor our environmental
impact
Creating a database to analyse and understand their
environmental impact
Do we cause changes to our environment?
Data Sensors Shared Data Input Interactive Maps
Choices and Data Entry Analysis and Display
Selection Randomisation Data Storage

57. 5-6
Exploring computer systems that collect data on your location,
and how this can be used to solve problems
Using a database of geographical information and ways in which
this can be used to solve problems
Creating a maze game where the computer can determine the
best solutions
How can computers help us solve problems?
Creating an expert system that can store expert knowledge and
be used by others to solve problems
Integration Optimisation
Mapping Data GIS
Mobile GPS

58. Error Correction Example
http://www.csfieldguide.org.nz/en/chapters/coding-error-control.html

59. Binary Search Example
http://www.csfieldguide.org.nz/en/interactives/searching-algorithms/index.html?level=3

61. Travelling Salesman
http://www.csfieldguide.org.nz/en/chapters/complexity-tractability.html

62. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

63. 3:02

64. Bee Bots

66. 0:45

67. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

68. Guessing Game

69. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

70. Computer Games

71. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

72. Mobile Apps

73. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

74. Dynamic Websites

75. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

76. Mapping

77. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

78. Robotics

79. 3:53

80. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

81. Interfaces

82. Picoboard

83. 2:14

84. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

85. Wearables

86. 5:25

87. 1:22

88. Big Problem
Project Based Learning
Thinking Skills
Curriculum Outcomes

89. Expert Systems

90. Expert Systems

91. Artificial Intelligence

92. 2:09

93. Griffith University
Dr Jason Zagami
www.zagami.info