IGCSE Computer Science (0478): Where Logical Thinkers Still Lose Marks

IGCSE Computer Science is one of the fastest-growing subject choices among Kenyan IGCSE students, and one where strong logical thinking doesn't automatically translate into strong exam marks. The paper rewards a specific kind of precision. Here's what the syllabus covers and where students actually lose marks.

This guide is for students revising now and for parents who want to help but do not code themselves. Most of the marks lost in this subject come from habits that are easy to spot once you know what to look for: loose vocabulary in theory answers, untidy pseudocode, and rushed trace tables. Each section below ends with something practical you can do this week.

Which Code You're Actually Sitting

0478 is the standard Cambridge IGCSE Computer Science syllabus, graded A* to G. A separate code, 0984, covers the same content under the newer 9-1 grading scale. The content and skills tested are identical between the two, only the grade scale differs. Confirm with your school which code your entry is registered under, since it affects how your eventual grade is reported, not what you need to study.

The Two Halves of the Syllabus

The subject splits into two genuinely different skill areas that need separate revision approaches:

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• Theory (computer systems, networks, data representation, algorithms and logic). This half rewards precise, technical vocabulary. "Binary" and "denary" conversions, logic gates, and how data is actually stored and transmitted are tested with exact expected answers, not general understanding.
• Practical programming (algorithm design and problem-solving, usually in pseudocode, plus a programming project component depending on the exam board pathway). This half rewards structured, working logic, not just "an answer that produces the right output." Markers check the actual steps and structure of an algorithm, not only whether a final result seems plausible.

Where Marks Actually Get Lost

Three patterns show up consistently:

• Pseudocode syntax carelessness. Cambridge pseudocode has specific conventions (how loops, conditionals and variable declarations are written). A student who codes correctly in Python but doesn't translate that logic into the exam's expected pseudocode conventions loses marks for presentation even when the underlying logic is sound.
• Confusing "how" with "why." Theory questions often ask students to explain why a particular technical choice is made (why use a particular data structure, why a network uses a specific protocol) rather than just describe how something works. Students who only memorise definitions struggle with the reasoning-based questions.
• Under-practising trace tables. Algorithm-tracing questions, where a student manually steps through pseudocode and records variable values at each stage, are a distinct skill from writing algorithms and need separate, deliberate practice. Many students who can write working code still make careless errors tracing through someone else's.

Worked Practice Examples

The examples below are practice items written for this guide. They are not official Cambridge questions, but they mirror the kinds of skills the syllabus tests. Work through them with a pencil before reading the answers.

Practice 1: Denary to binary and hexadecimal

Task: Convert the denary number 45 into 8-bit binary, then into hexadecimal.

Working: Use place values 128, 64, 32, 16, 8, 4, 2, 1. 45 = 32 + 8 + 4 + 1, so the binary is 00101101. Split into two groups of four bits: 0010 = 2 and 1101 = 13, which is D in hexadecimal. The answer is 2D.

Why it matters: showing the place values and the grouping makes your working visible. If a slip happens, clear working makes the mistake easy to find when you check.

Practice 2: A trace table

Trace this algorithm and state the output.

Total ← 0
FOR Count ← 1 TO 4
    Total ← Total + Count
NEXT Count
OUTPUT Total

Common error: stopping at Count = 3, or writing the output on every row. Fill one row per change in a variable, and write the output only when the OUTPUT line runs.

Practice 3: Explaining "why"

Question: Explain why data is often compressed before it is sent over the internet.

Weak answer: "To make it smaller."

Stronger answer: "Compression reduces the file size, so fewer bits need to be transmitted. This means the file takes less time to send and uses less bandwidth." The stronger answer links the technical fact (smaller file) to the reason it matters (faster transfer, less bandwidth).

Practice 4: Pseudocode, not Python

A student writes for i in range(1, 11): in an exam answer. The logic may be right, but it is Python, not the exam's pseudocode. A pseudocode version uses the conventions in the Cambridge pseudocode guide for your syllabus, such as a FOR ... TO ... NEXT loop and the ← arrow for assignment. Download that guide from your teacher or the official syllabus materials and practise writing every algorithm in its style.

A Practical Revision Split

Given the two-halves structure, revising strictly subject-by-subject through the syllabus is less effective than splitting practice time between theory recall (with exact technical vocabulary drilled specifically), algorithm writing in the exam's actual pseudocode conventions, and dedicated trace-table practice as its own skill. Past papers, marked strictly against the official mark scheme rather than "does this look about right," reveal exactly which of the three areas needs more work.

How parents can help without knowing how to code

• Test definitions aloud from a flashcard deck, and accept only the full wording.
• Time past paper sections so practice feels like the real exam.
• Mark against the mark scheme together. You do not need to understand the code to check whether an answer contains the points the scheme lists.
• Ask "why?" after every theory answer. If your child cannot give a reason, that topic needs another look.

The Oct/Nov 2026 series is running now. For students with Computer Science papers still ahead, short daily sessions on trace tables and conversions keep skills sharp without crowding out other subjects.

Where This Fits

Computer Science sits within the applied sciences that Kenyan students often pair with Mathematics and one of the physical sciences. For general science exam technique that applies across subjects (command words, data-response questions), see our Physics 0625 A* method guide, much of the command-word discipline transfers directly.

Frequently asked questions

Is 0478 harder than 0984?

No. The content and skills are identical; only the grading scale differs. 0478 is graded A* to G and 0984 uses the 9-1 scale.

Can I write my answers in Python?

Write algorithms in the exam's pseudocode conventions unless a question clearly allows otherwise. Logic written in Python syntax risks losing presentation marks even when it works.

How should I practise trace tables?

Separately from writing code. Take algorithms from past papers, trace them by hand row by row, and check against the mark scheme. Speed comes after accuracy.

Are past papers enough on their own?

They are the most useful tool, but only when marked strictly against the official mark scheme. Combine them with vocabulary drills and pseudocode practice for the best results.

For structured IGCSE revision across subjects, see the IGCSE Countdown Bundle for November 2026, the IGCSE all-subjects bundle, or browse the full shop.