Did you know that G3 Computing assesses both written understanding and work completed on a computer? The 2027 SEC syllabus is K349. Its written paper contributes 60%, while its lab-based paper contributes 40%.
Our suggested first step is to ask the student to explain a small solution before running it. What are the inputs, what should the output be and how would we check it? This makes it easier to see whether the next learning need is reading a problem, building an algorithm or correcting code.
Need the format? Open the paper table. Checking readiness? Try the family questions. Ready to practise? Use the original example and the study suggestions, or browse the all-subject directory.
2027 SEC school-candidate syllabus, checked 10 October 2026. Confirm the registered course and examination year with the school. Our examples and study routines are independent suggestions, not official examination questions or marking schemes.
Confirm K349 and the examination year with the school. Computing has distinct G1 and G2 syllabuses; collect resources for the registered course rather than relying on the broad word “Computing”. The school should confirm subject availability and its lesson arrangements.
For the wider comparison, read the related subject guide. Keep the confirmed code beside each resource so that a similar title does not lead to the wrong paper structure.
| Paper | Duration | Marks and weight | Coverage |
|---|---|---|---|
| Paper 1: written | 2 hours | 80 marks · 60% | All five modules |
| Paper 2: lab-based | 2 hours 30 minutes | 70 marks · 40% | Spreadsheets, algorithms and programming |
Both papers have compulsory questions. The five modules cover computing fundamentals, algorithms and programming, spreadsheets, networking, and computing’s impact. Paper 2 uses a computer with spreadsheet software, Python and JupyterLab; candidates submit the required files within the allotted time.
Source: SEAB K349 official syllabus.
Enjoying games or using a computer confidently can start a conversation, but our readiness check focuses on the work. Can the student read a short specification, break it into steps and explain a failed test without giving up? Ask the teacher which recent task best shows these habits.
Bring a recent piece of work to the teacher conversation. Ask which difficulty should be addressed first and what a successful correction would look like. Choose a small task the student can complete and review; increase the demand when the foundation becomes clearer.
Try an original problem: count how many values in a list are at least 10. For [4, 10, 12], the result should be 2. For [10], it should be 1. A solution that tests “greater than 10” instead of “at least 10” fails at the boundary. The important move is to decide the expected answers before trusting the output.
Write the steps in plain language: start a count at zero, examine each value, add one when the value is at least 10, then report the count. Trace the list by hand. A parent can help by asking for the reason behind a step, even without knowing Python. Once the logic is clear, the student can implement and test it in their learning environment.
Keep a short test log with the requirement, input, expected output, actual output and correction. Add a separate theory task, such as explaining a networking concept in a few sentences. In spreadsheet practice, check what a formula references before copying it. Save the work, reopen it and make sure the intended result is still present.
When something fails, reduce the problem. Test one value, one branch or one formula first. Record why the correction works so that the next similar task benefits from it. Our suggestion is to alternate explanation and execution: describing the solution and producing a working solution give different views of understanding.
The Secondary 1 Mathematics learning guide offers a wider learning principle: identify the foundation, sequence practice and use corrections to choose the next step. Apply that approach to the specific difficulty visible here, with the subject teacher’s guidance.