B1.1 · Book pp. 282–293
Approaches to computational thinking
Before any code, you need to understand the problem. Computational thinking does not mean thinking like a computer. It means thinking like a computer scientist.
What you need to know
- B1.1.1 Construct a problem specification.
- B1.1.2 Describe the fundamental concepts of computational thinking.
- B1.1.3 Explain how these concepts are used to solve problems.
- B1.1.4 Trace flowcharts for a range of algorithms.
This work leads straight into your internal assessment, since its first criterion needs a problem specification.
B1.1.1 Problem specification
A problem specification is a short, clear explanation of an issue, showing who the stakeholders are and why it matters.
| Problem statement | What the problem is, who it affects, and what must be solved |
| Constraints and limitations | Technical, economic, legal, operational and schedule limits |
| Objectives and goals | What the solution should achieve, agreed with stakeholders |
| Inputs and outputs | What goes in, what comes out, and in what form |
| Evaluation criteria | Clear, specific, measurable tests of success |
Inputs and outputs
- Direct entry: barcode, OCR, OMR or MICR readers.
- Manual entry: keyboard, touch screen or mouse. It is cheap but error-prone.
- Automatic entry: sensors. It costs more but is faster and more accurate.
- Output: temporary (screen), permanent (print), or electrical and mechanical (actuators).
Discussion: building for the whole world
A global platform must consider languages, currencies, date formats, laws such as GDPR, right-to-left layouts, time zones and what colours mean in different cultures. Is a local market sometimes the better target?
B1.1.2 Fundamental concepts
- Abstraction: keep essential information and ignore the rest, as in a metro map or a timetable. In code, it hides how a process works, so you can simply call it.
- Algorithmic design: create an ordered, finite set of precise steps with predictable results.
- Decomposition: break a complex problem into smaller parts, solve them, then combine the results. This supports modular teamwork.
- Pattern recognition: identify similarities in problems, so that tested solutions can be reused.
⚠️ Common mistake
Decomposition breaks down the problem, not the program. When it happens, there may be no program yet.
✏️ Book correction
The book says that setting sum to 0 before dividing “would produce an error”. In fact 0 ÷ 3 runs without a problem and prints 0. That is a logic error: a wrong result with no error message, which makes it harder to find.
B1.1.3 Using the concepts
| Area | How the concepts help |
|---|---|
| Software development | Understand the problem, drop unneeded detail, find repeats and design efficient algorithms |
| Libraries | Call a function without knowing how it was written, which is abstraction |
| Data analysis | Spot patterns such as frequent complaints, then automate the repetitive steps |
| Machine learning | Pattern recognition classifies data and predicts behaviour |
| Database design | Abstraction picks the relevant data, and decomposition splits it into tables |
| Normalization | Pattern recognition finds repeated groups of attributes |
| Network security | Patterns identify threats, and algorithms set out step-by-step responses |
✏️ Book note
The book’s game example treats abstraction as players ignoring misleading clues. A clearer example is a game modelling the real world: a racing game keeps speed and grip, and drops most of the physics of a real engine.
B1.1.4 Flowcharts
| Symbol | Meaning |
|---|---|
| Terminator (rounded) | Start or end |
| Parallelogram | Input or output |
| Rectangle | Process, such as a calculation or assignment |
| Diamond | Decision with labelled yes and no branches |
| Arrow | Flowline showing what happens next |
| Circle | Connector to another part of the chart |
✏️ Book note
The book says flowlines show the direction of “data flow”. More exactly, they show the flow of control, or the order in which steps run.
Trace the flowchart
rev ← 0
INPUT number
WHILE number > 0
remainder ← number MOD 10
rev ← rev * 10 + remainder
number ← number DIV 10
END WHILE
OUTPUT revWhat does it do, and what is the flaw? Try 1760.
It reverses the digits of a number. 1760 gives 671, not 0671: the leading zero disappears because rev is a number, not text. Zero itself never enters the loop, and outputs 0.
⚠️ Common mistake
Label both branches of every decision, and make sure that every flowline connects to a shape.
Quick quiz
Exam-style questions
These are written for this site in the style of Paper 2. Answer on paper first, then open the mark points.
-
Define computational thinking and outline its role in problem solving. [3]
Mark points
- A toolkit of techniques for solving problems [1].
- It includes abstraction, decomposition, algorithmic thinking and pattern recognition [1].
- It helps to analyse and simplify a problem and to choose an efficient solution [1].
-
Identify three items in a problem specification and define one. [4]
Mark points
- Problem statement [1].
- Constraints and limitations [1].
- Evaluation criteria [1].
- For example, evaluation criteria are measurable tests of whether the solution succeeds [1].
-
Outline how decomposition and abstraction help software development. [4]
Mark points
- Decomposition splits the problem into smaller, manageable parts [1].
- Teams can then work on the parts at the same time [1].
- Abstraction removes unnecessary detail [1].
- The solution is simpler, and takes less time to build [1].
-
Students report reading time in hours and minutes. Identify the input, process and output for an algorithm that gives minutes only. [3]
Mark points
- Input: hours and minutes [1].
- Process: total ← hours × 60 + minutes [1].
- Output: total minutes [1].
-
A username joins the last two letters of a first name and the first three of a last name. State the username for Malee Tanaka, and explain one limitation. [3]
Mark points
- ee + Tan = eeTan [1].
- Two students may produce the same username [1].
- Adding a unique number would solve this [1].