1.1.1 The definition and the purpose of computational thinking.

Computational thinking is a structured way of approaching problem-solving that breaks down complex challenges into smaller, more manageable steps. Its purpose is not just to enable coding or programming but to apply logical thinking, pattern recognition, abstraction, and algorithmic design to a wide range of problems. In practice, it allows individuals to analyse issues systematically, identify repeated processes, and develop step-by-step strategies to resolve them efficiently. For example, when managing a digital support service desk, computational thinking helps technicians triage faults by categorising common errors, identifying patterns in system failures, and applying logical troubleshooting steps to reach a resolution. This approach ensures consistency, reduces wasted time, and supports effective decision-making when multiple possible solutions exist. Within the digital support and security sector, the application of computational thinking is vital for safeguarding networks, systems, and data.

In cyber security incident response, analysts use abstraction to filter irrelevant noise from security logs, decomposition to break down an intrusion into stages, and algorithmic thinking to design repeatable response playbooks. Similarly, in support roles, staff apply pattern recognition when monitoring system performance or spotting trends in user behaviour that may signal a phishing attack or malware infection. By embedding computational thinking into daily practice, professionals can ensure that problem-solving is both systematic and adaptable, which is essential for maintaining resilience and compliance in modern digital environments.

Exam Question (8 Marks)

Explain the purpose of computational thinking and analyse how it is applied within the digital support and security sector. Use real-world examples to support your answer.
 

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1.1.2 When to use computational thinking.

Computational thinking should be used whenever problems are too complex to be solved through guesswork or ad-hoc approaches, and instead require a structured, logical process. It is particularly valuable when dealing with problems that are repeated, involve large amounts of data, or where accuracy and security are critical. For example, in cyber security monitoring, support staff use computational thinking to identify patterns in suspicious login attempts, separating genuine user errors from potential brute-force attacks.

In digital support, computational thinking helps technicians decide when to automate routine processes such as password resets or system backups, ensuring consistency and efficiency. It may not be necessary for simple, one-off issues, such as a single user forgetting a password, but becomes essential in system-wide incidents or long-term projects like cloud migrations. Within the digital support and security sector, applying computational thinking ensures resilience by breaking problems into manageable parts, filtering irrelevant information, and creating structured, repeatable solutions that safeguard both system performance and data security.

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Exam Question (8 marks)
Explain when computational thinking should be used and analyse its importance in the digital support and security sector. Use real-world examples to support your answer.

1.1.3 The benefits and drawbacks of using computational thinking.

1.1.4 The components of computational thinking:

• decomposition

• pattern recognition

• abstraction

• algorithmic design.

1.1.5 The benefits and drawbacks of using the components of computational thinking.

1.1.6 The purpose of decomposition.

1.1.7 The tasks of decomposition:

• identify the main features of a problem

• characterise each identified feature

• break problems down into smaller, more manageable parts

• break solutions down into smaller, more manageable parts.

1.1.8 How to use decomposition for problem solving.

1.1.9 The methods to represent decomposition:

• block diagrams

• information flow diagrams

• flowcharts

• written descriptions.

1.1.10 How to use methods to represent decomposition.

1.1.11 The purpose of pattern recognition

1.1.12 How to use pattern recognition for problem solving:

• find and interpret trends and similarities within and between problems and processes

• find and interpret common features between a given problem and existing solutions

• make predictions and assumptions based on identified patterns.

1.1.13 The purpose of abstraction.

1.1.14 The tasks of abstraction:

• identify information that is needed

• filter out unnecessary details

• hide details of internal workings.

1.1.15 How to use abstraction:

• what inputs are needed

• what the expected outputs and outcomes are

• things that will vary

• things that will remain constant

• key actions the solution must perform

• repeated processes the solution will perform.

1.1.16 How to use abstraction in problem solving.

1.1.17 The interrelationships between components of computational thinking and make judgements about the suitability of using the components in digital support and security

1.2 Algorithmic design

1.2.1 The definition and understand the characteristics and purpose of algorithms

1.2.2 The methods to express algorithms:

• flowcharts:

o terminators

o processes

o sub-processes

o decisions

o inputs/outputs

o arrows

o labels

• written descriptions using hierarchical markers to indicate sequence.

1.2.3 The benefits and drawbacks of expressing algorithms in flowcharts.

1.2.4 The benefits and drawbacks of expressing algorithms in written descriptions.

1.2.5 The actions to control ordering of steps in algorithms:

• sequence

• selection

• iteration.

1.2.6 How to determine the purpose of an algorithm and how it works.

1.2.7 How to determine the output of an algorithm given an input.

1.2.8 How to identify errors in an algorithm.

1.2.9 How to correct errors in an algorithm.

1.2.10 How to design algorithms and solutions that use actions.

1.3 Strategies

1.3.1 The different approaches to solving problems and understand their purpose and when they are used:

• top-down

• bottom-up

• modularisation.

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1.3.2 The benefits and drawbacks of using the different approaches to solving problems

1.3.3 The purpose of root cause analysis and when it is used.

1.3.4 The approaches to root cause analysis:

• five whys

• failure mode and effects analysis (FMEA)

• event tree analysis (ETA)

• actions to take after using root cause analysis:

o log

o close

o escalate to an appropriate manager, specialist or external third party.

1.3.5 The process of the high-level problem-solving strategy:

• define the problem

• gather information

• analyse the information

• make a plan of action

• implement a solution

• review the solution.

1.3.6 The definition of a digital incident, in incident management:

• a single unplanned event

• that disrupts service operations

• that negatively impacts service quality

1.3.7 The definition of a digital problem, in incident management, as the cause of the incident.

1.3.8 The process of incident management:

• detection: report, record, prioritise

• response: identify owner, resolve and restore, record resolution

• intelligence: record lessons, identify cause, share lessons.

1.3.9 The interrelationships between problems and problem-solving strategies and make judgements about the suitability of strategies for solving the problems in digital support and security