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IB Computer Science B1.1: Approaches to computational thinking

New IB Computer Science syllabus · first exams May 2027 · Theme B · SL & HL

Guiding question: How can we apply a computational solution to a real-world problem?

B1 Computational thinking is the first unit of Theme B (5 hours at SL and HL), assessed in Paper 2. It's the toolkit you use before writing any code: specify the problem, break it down, spot patterns, focus on what matters, design the steps, and trace them to check they work.

StatementTopicCommand term
B1.1.1A problem specificationConstruct
B1.1.2The fundamental concepts of computational thinkingDescribe
B1.1.3Applying computational thinking to solve problemsExplain
B1.1.4Flowcharts for programming algorithmsTrace
Computational thinking doesn't have to involve programming. It's a problem-solving toolkit used by engineers, scientists and managers too.

Key points

B1.1.1 Problem specification

A complete specification has six components:

  1. Problem statement: what are we solving?
  2. Constraints and limitations: time, budget, hardware, regulations, accessibility.
  3. Objectives and goals: specific and measurable, for example "respond in under 1 s".
  4. Input specifications: the type, format, range and validation rules.
  5. Output specifications: the format, units and precision, plus error messages.
  6. Evaluation criteria: test cases and performance measures that show it works.

A specification describes what success looks like, not how to build it.

B1.1.2 The four concepts

  • Decomposition: break a big problem into smaller sub-problems that can be solved, and tested, separately.
  • Pattern recognition: spot similarities, so one solution can be reused. For example, bus-pass eligibility (≥ 3 km), exam pass (≥ 50%) and driving age (≥ 17) are all "compare to a threshold".
  • Abstraction: hide irrelevant detail and keep what matters. A metro map distorts geography to make the connections clear.
  • Algorithmic design: a clear, step-by-step solution that is correct, unambiguous, finite and deterministic.

B1.1.3 Using computational thinking in computer science

The same toolkit appears across software development, data analysis, machine learning, database design and network security. For example, network security is decomposed into layers of defence, and intrusion detection relies on recognising attack patterns.

B1.1.4 Tracing flowcharts

  • Know the standard symbols: Start/End, Process, Input/Output, Decision, Flowline, Connector.
  • Trace by following the flow step by step and recording each variable in a trace table, to work out the output.

Exam-style question

Describe the four fundamental concepts of computational thinking, with one example each. [8]

Mark scheme

Award [1] for the definition and [1] for an example, for each concept:

  • Decomposition: breaking a problem into smaller sub-problems [1]. For example, splitting an app into modules [1].
  • Pattern recognition: identifying repeating themes or similarities [1]. For example, several "compare to a threshold" apps share one structure [1].
  • Abstraction: hiding irrelevant detail to focus on what matters [1]. For example, a metro map distorts geography to highlight connections [1].
  • Algorithmic design: creating a step-by-step solution [1]. For example, an algorithm to find the maximum value in a list [1].

Common mistakes that cost marks

  • Jumping straight to code when asked to construct a specification. Give all six components.
  • Mixing up pattern recognition and abstraction. Pattern recognition spots what's similar; abstraction decides what's important.
  • Skipping the trace table. Write down every change to every variable, in order, as you follow the flowchart.

Frequently asked questions

Which paper assesses B1?
Theme B, including B1, is assessed in Paper 2.

Is B1 the same at SL and HL?
Yes. B1 is studied for 5 hours at both levels.

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