week 1

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.
 

"Compute This Activity Worksheet"

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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.

Benefits of Computational Thinking

• Efficiency in problem-solving

  • Helps organisations save time and resources by breaking down complex issues into smaller steps.

  • Example: In logistics, Amazon uses computational thinking to optimise delivery routes, reducing costs and speeding up delivery.

• Consistency and repeatability

  • Solutions designed with computational thinking are structured and can be reused in similar situations.

  • Example: Banks use algorithms created through computational thinking to detect fraudulent transactions consistently across millions of accounts.

• Better collaboration

  • Breaking a large project into smaller steps makes it easier for teams to share responsibilities.

  • Example: Software development teams use computational thinking to divide tasks like coding, testing, and debugging among specialists.

• Supports automation

  • Computational thinking often leads to solutions that can be automated, reducing human error.

  • Example: NHS data analysis systems use computational thinking to automate the tracking of disease spread.

Drawbacks of Computational Thinking

1. Over-simplification

   • Sometimes breaking a problem down can ignore important human, social, or ethical factors.

   • Example: Automated CV-scanning systems used in recruitment can unintentionally filter out strong candidates if the algorithm is too rigid.

2. Dependence on data quality

   • If the data used is poor or biased, computational thinking can lead to flawed solutions.

   • Example: Predictive policing algorithms in the US produced unfair results because they were based on biased crime data.

3. Lack of flexibility

   • Computational thinking is strong for logical problems but less effective when creativity, emotion, or human intuition is needed.

   • Example: Customer service chatbots sometimes frustrate users because they cannot handle emotional or unusual queries.

4. Resource-heavy

   • Developing algorithms and systems can be expensive in terms of time, money, and expertise.

   • Example: Small businesses may struggle to implement complex systems compared to large organisations like Google or Microsoft.

Situations Where Computational Thinking Has Helped an Organisation

• Amazon: Optimising delivery routes and stock levels in warehouses.

• NHS: Analysing patient data to predict hospital bed shortages.

• Airlines: Scheduling flights and crew assignments using computational algorithms.

Situations Where Computational Thinking Has Caused Drawbacks

• Uber: Pricing algorithms caused public backlash when they increased fares dramatically during emergencies.
   

  Consider this video on points made, is it right (Ethical) ?

• Recruitment systems: Algorithms sometimes reject candidates unfairly due to biased keyword scanning.

• Social media platforms: Algorithms recommending content have been criticised for spreading misinformation.

Investigative Activity – Directed to You

Task: Exploring the Real Impact of Computational Thinking
Work individually or in pairs. Spend around 25 minutes investigating and presenting your findings.

Research two case studies – one where computational thinking had a positive impact, and one where it had a drawback.
Use online resources, news articles, or case studies provided by your tutor.

For each case study, answer the following:
What was the problem?
How was computational thinking applied? (Decomposition, patterns, abstraction, algorithms)
What benefits or drawbacks resulted?
Could the drawbacks have been reduced with human involvement or different design choices?

Present your findings in one of the following formats (your choice):
A short written report (200–300 words).
A one-slide summary (PowerPoint/Google Slide).
A mind map showing the situation and the outcome.

Stretch challenge: Suggest how computational thinking could be applied in your own college life (e.g., timetabling, coursework management, esports tournaments) and whether it would be beneficial or harmful.

1.1.4 The components of computational thinking:

Computational thinking isn’t just “thinking like a computer.” It’s about breaking problems down and solving them in logical, structured ways that make sense to both humans and machines. Four key components make up computational thinking:

Decomposition

What it is:
Breaking a big, complex problem into smaller, more manageable parts.

Example in use:

• In IT support, when a user’s PC won’t turn on, you don’t immediately assume the whole machine is broken. You test smaller components (power cable → power supply → motherboard → software).

• In project management, a large software project is split into smaller tasks like design, coding, and testing.

Benefit: Makes complex problems less overwhelming and easier to solve.
Drawback: If broken down incorrectly, you may miss important connections between parts.

Pattern recognition

What it is:
Identifying similarities or repeated trends to speed up problem-solving.

Example in use:

• Cybersecurity teams spot patterns in network traffic that indicate a possible attack.

• Helpdesks notice repeated reports of the same printer error across multiple sites, helping them find the root cause faster.

Benefit: Saves time by applying solutions to similar problems.
Drawback: Over-relying on patterns may cause missed exceptions (e.g., assuming every PC crash is the same issue).

Abstraction

What it is:
Focusing only on the important details and ignoring the irrelevant ones.

Example in use:

• When creating a network diagram, you don’t show every single cable; instead, you highlight routers, switches, and servers.

• Google Maps doesn’t show every blade of grass — only the roads and landmarks you need to navigate.

Benefit: Simplifies complex problems so solutions are easier to design.
Drawback: Risk of leaving out important information if you abstract too much.

Algorithmic design.

What it is:
Creating a step-by-step set of instructions (an algorithm) to solve a problem.

Example in use:

• Installing a new operating system: the IT team follows a clear sequence of steps (backup → format drive → install OS → restore files).

• Online shops use algorithms to recommend products based on previous purchases.

Benefit: Provides clear, repeatable instructions that can be automated.
Drawback: Algorithms can be rigid; if unexpected input appears, the system may fail.

Situations Where These Components Helped Organisations

• Decomposition: NASA engineers break down rocket launches into small systems (fuel, navigation, communication), making testing manageable.

• Pattern Recognition: Banks identify suspicious activity by noticing unusual spending patterns.

• Abstraction: Social media platforms simplify billions of data points into dashboards for advertisers.

• Algorithmic Design: Delivery companies like UPS optimise routes using algorithms, saving time and fuel.

Situations Where Components Caused Drawbacks

• Decomposition: If a business breaks down a project poorly, teams may work in silos and miss the “bigger picture.”

• Pattern Recognition: Facial recognition software has been criticised for bias because it wrongly assumed patterns across ethnic groups.

• Abstraction: In medical AI, ignoring certain patient data during abstraction has sometimes led to misdiagnoses.

• Algorithmic Design: Uber’s pricing algorithm raised fares unfairly during natural disasters, leading to backlash as discussed earlier.

Task: Analysing the Components of Computational Thinking in Action

Work individually or in groups of 2–3. Spend around 30 minutes investigating and presenting your findings.

Pick one of the four components (decomposition, pattern recognition, abstraction, algorithmic design).

Research a real-world case study where that component was used in an organisation. Examples could include:
Tech companies (Google, Microsoft, Amazon)
Healthcare (NHS, medical AI systems)
Transport (airlines, Uber, logistics)
Cybersecurity (firewalls, intrusion detection systems)

Answer the following questions:
How was the component applied?
What benefit did it bring?
Were there any drawbacks?
If you were part of the team, what would you have done differently?

Present your findings in one of these formats (choose one):
A 1–2 minute verbal summary to the class.
A quick infographic or mind map.
A mini case-study write-up (200 words).

Stretch challenge: Suggest how all four components could be applied to a college-related problem (e.g., planning an esports tournament, scheduling classes, or setting up a college IT network).

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

Computational thinking is built from four components: decomposition, pattern recognition, abstraction, and algorithmic design. Each has strong advantages in problem-solving, but also risks and drawbacks when applied in real-world contexts.

Decomposition

Benefits:

• Clarity of focus: Breaking down large tasks helps teams understand their roles more clearly. For example, in video game development, different teams can separately focus on graphics, storyline, and gameplay mechanics.

• Scalability: Makes it easier to manage very large or long-term projects, as progress can be tracked in smaller chunks.

Drawbacks:

• Loss of context: If teams focus only on their small piece, they may miss how it connects to the wider project. For example, in large government IT projects, lack of communication between sub-teams has sometimes caused mismatched systems.

• Duplication of effort: Different teams may unknowingly solve the same problem in parallel because tasks were broken down poorly.

Pattern Recognition

Benefits:

• Predictive insight: Identifying trends allows organisations to forecast outcomes. For example, supermarkets use loyalty card data to recognise seasonal buying patterns and plan stock levels.

• Efficiency: Once patterns are known, solutions can be reused. For example, cybersecurity firms apply known malware signatures to detect threats faster.

Drawbacks:

• False assumptions: Not all patterns hold true over time. Retailers that rely too heavily on past buying patterns may fail to anticipate new consumer trends.

• Bias: Pattern recognition can reinforce stereotypes or overlook outliers. For instance, in healthcare, pattern-based diagnostic tools may miss rare diseases.

Abstraction

Benefits:

• Simplification of complexity: By ignoring irrelevant details, problems can be tackled more quickly. For example, when planning air traffic, controllers abstract individual aircraft details and focus only on speed, altitude, and direction.

• Communication: Abstraction allows technical and non-technical teams to work together, as simplified models are easier to explain.

Drawbacks:

• Oversimplification: Ignoring too much detail can cause important issues to be missed. For example, financial models that abstract away economic uncertainty may fail to predict market crashes.

• Dependence on assumptions: Abstraction often relies on assumptions that may not always be valid. For example, climate models may abstract some factors, which can make predictions less accurate in certain regions.

Algorithmic Design

Benefits:

• Automation of tasks: Clear step-by-step processes can be turned into automated systems. For example, self-checkout machines in supermarkets follow precise algorithms to process sales quickly.

• Reliability: Algorithms can consistently handle repetitive tasks without human error, such as sorting millions of emails into spam or inbox.

Drawbacks:

• Rigidity: Algorithms struggle when unexpected scenarios arise. For example, navigation apps sometimes direct drivers down unsuitable roads because the algorithm did not consider local conditions.

• Ethical concerns: Automated decision-making may lack human judgement. For example, credit scoring algorithms can unfairly deny loans if they rely on limited data points.

1.1.6 The purpose of decomposition.

What is decomposition?

Decomposition is the process of breaking a big, complex problem into smaller, more manageable parts. Instead of trying to solve everything at once, you deal with one piece at a time.

Think of it like revising for an exam: instead of reading the entire textbook cover to cover, you split it into topics (e.g., networking, security, databases) and tackle each one in turn.

Why is decomposition important?

1. Makes problems easier to understand

   • A huge task can feel overwhelming. By breaking it into smaller steps, it becomes less intimidating.

   • Example: An IT support team investigating a network issue may check the router, then the switch, then the cabling, instead of blaming the whole system.

2. Supports teamwork

   • Smaller tasks can be shared between people, so a big project can move faster.

   • Example: In developing a mobile app, one team might focus on the interface, another on the database, and another on testing.

3. Saves time and reduces mistakes

   • Tackling smaller pieces means errors are easier to spot and fix.

   • Example: In cybersecurity, analysts may decompose an attack into stages (reconnaissance → access → exploit) to pinpoint weaknesses.

4. Builds reusable solutions

   • Once you’ve solved a small piece of a problem, you can often reuse that solution elsewhere.

   • Example: A log-in system for one website can be reused on another project.

Possible drawbacks of decomposition

• If a problem is broken down incorrectly, some steps may be missed.

• Different teams might only focus on their section and forget the “big picture.”

This is why decomposition works best when combined with good planning and communication.

Last Updated
2025-09-30 10:31:56