Top 200 Secondary 4 Computing Vocabulary

Secondary 4 Computing vocabulary means understanding how computers encode information, execute Python code, use spreadsheet formulas, communicate over networks and affect people. This complete guide provides 200 clear definitions, worked programming examples, a revision plan and a guide to the first Singapore-Cambridge Secondary Education Certificate examination.

For 2026 GCE O-Level Computing, the subject code is 7155. For the first 2027 SEC G3 Computing, the code is K349. The 2027 syllabus has a written paper and a computer-based practical assessment using spreadsheets, Python and JupyterLab. This independent 200-term glossary is not a prescribed SEAB word list; some additional industry terms are explained for wider understanding.

Quick overview: Learn the meanings of 200 computing terms, see original examples of Python, formulas and networks, understand common programming mistakes, and practise with a four-week plan. Follow the current official syllabus for exact examinable functions and assessment instructions.

All 200 words
2027 SEC G3 examination
Worked examples
Study plan
Frequently asked questions

Top 200 Vocabulary for Secondary 4 Computing: Words and Meanings

1. Computer systems and hardware — words 1–20

Processor, memory and storage have different jobs. A laptop can have a fast processor and still feel slow if other components limit its work.

computer system — A combination of hardware and software that processes information.
hardware — The physical parts of a computer.
software — The instructions and programs used by a computer.
processor — A component that executes machine instructions.
central processing unit — The main unit that controls and executes program operations.
main memory — Storage directly accessible for current program processing.
random-access memory — Volatile memory holding active instructions and data.
secondary storage — Nonvolatile media used to retain files and programs.
solid-state drive — Storage using electronic memory without rotating disks.
magnetic storage — Media that store data using magnetic states.
optical storage — Media read or written using a light beam.
data bus — A pathway transferring data between computer components.
address bus — A pathway carrying memory addresses.
input device — Hardware that supplies information to a system.
output device — Hardware that presents information or performs an action.
peripheral — An attached device that extends computer functions.
USB — A standard for attaching many peripheral devices.
HDMI — A digital interface for carrying audio and video.
PCI Express — An expansion interface used for fast internal connections.
volatile memory — Memory whose contents disappear without power.

2. Digital representation and logic — words 21–40

Numbers, text and logic are encoded into bits. A different base changes a representation, not the underlying mathematical quantity.

bit — A binary digit representing zero or one.
byte — A group of eight bits.
binary — A base-two number system.
denary — A base-ten number system.
hexadecimal — A base-sixteen number system.
place value — The contribution of a digit determined by its position.
two’s complement — A fixed-width method of representing signed binary integers.
overflow — A numerical result outside a format’s representable range.
character encoding — Rules representing text characters as numerical codes.
ASCII — A character coding standard with an original seven-bit set.
Boolean — A logical value of true or false.
logic gate — An element implementing a Boolean operation.
truth table — A table giving output for each possible input combination.
AND — A Boolean operator true only if both inputs are true.
OR — A Boolean operator true if one or both inputs are true.
NOT — A Boolean operator reversing a truth value.
NAND — The negation of the AND operation.
NOR — The negation of the OR operation.
XOR — An exclusive OR true when two inputs differ.
De Morgan’s laws — Logical identities converting negations of AND and OR.

3. Computational thinking — words 41–60

A valid solution starts with understanding the problem. Writing code for the wrong input or output is not a success.

abstraction — Retaining relevant details while ignoring distracting ones.
decomposition — Breaking a large problem into manageable components.
pattern recognition — Finding similarities that support a reusable solution.
algorithm — A finite unambiguous sequence of solution steps.
requirement — A condition a system must satisfy.
input — Information provided to a process.
output — A result produced by a process.
constraint — A limit on the possible solutions.
precondition — A fact required before a procedure begins.
postcondition — A fact expected after a procedure completes.
flowchart — A diagram representing an algorithm’s control flow.
sequence — Actions executed in a chosen order.
selection — A decision between paths based on a condition.
iteration — Repeating a block of instructions.
loop condition — A test controlling whether a loop continues.
termination — Reaching the end of an algorithm or loop.
pseudocode — An informal notation describing program logic.
modular approach — Dividing a solution into smaller separate units.
incremental approach — Extending a working solution in manageable steps.
dry run — Stepping through an algorithm manually with sample data.

4. Python foundations — words 61–80

Python’s indentation is part of its syntax. Know the difference between assignment and comparison and choose types deliberately.

Python — A programming language used to express algorithms.
interpreter — Software used to execute code in a language environment.
source code — A program written in human-readable instructions.
statement — One instruction in a program.
variable — A named reference to a value or object.
assignment — Storing or binding a value to a name.
literal — A value written directly in source code.
identifier — A valid name for a program element.
integer — A whole-number data type.
floating-point number — An approximate real-number computer data type.
string — A sequence of text characters.
type conversion — Changing a value from one data type to another.
arithmetic operator — An operator such as addition or multiplication.
comparison operator — An operator testing relationships between values.
logical operator — An operator working on Boolean expressions.
floor division — Division returning the quotient rounded down.
modulo — An operation returning the remainder after division.
exponentiation — Raising a value to a power.
expression — A combination of values and operators yielding a result.
indentation — Leading whitespace organising Python code blocks.

5. Python structures and reusable code — words 81–100

Collections allow programs to handle more than one value; functions prevent useful logic from being repeated unnecessarily.

list — An ordered and mutable collection of values.
dictionary — A mapping between keys and associated values.
key — A label used to retrieve a dictionary value.
list item — A value stored in a list position.
zero-based indexing — Numbering sequence positions beginning at zero.
slicing — Taking a part of a sequence by positions.
concatenation — Joining two strings or other sequences.
substring — An unbroken part of a text string.
membership test — Checking whether an item belongs to a collection.
for loop — A loop visiting items in a sequence.
while loop — A loop repeating while a condition is true.
function — A reusable named block of program logic.
parameter — A name in a function’s definition for incoming data.
argument — A value supplied when calling a function.
return value — A result supplied back to a function’s caller.
local variable — A variable defined within a local scope.
global variable — A name at the outer module scope.
import — An instruction making another module’s contents available.
file handle — An object used to access a file’s contents.
delimiter — A character separating fields in structured text.

6. Program testing and repair — words 101–120

Successful execution does not guarantee a correct answer. Test both ordinary cases and boundaries against a stated expectation.

syntax error — An error in how programming language rules are written.
logic error — A flaw producing the wrong result without necessarily crashing.
runtime error — An error that arises while the program executes.
debugging — Locating and correcting defects in software.
trace table — A table tracking variable values as instructions run.
test case — A specified test input and expected output.
normal test — A test with typical valid values.
boundary test — A test at or near an allowed limit.
invalid input test — A test of data the program should reject.
validation — Checking whether data satisfies specified rules.
verification — Checking whether entered data agrees with its source.
range check — Testing whether a number falls within permitted limits.
length check — Testing whether text has an allowed number of characters.
format check — Testing whether input follows a required pattern.
presence check — Testing whether required data is supplied.
check digit — A calculated digit intended to detect some input errors.
backtracking — Following execution history to identify a faulty decision.
regression test — Testing a previously working feature after changes.
incremental testing — Checking each small addition before progressing.
error handling — Responding safely to exceptional or invalid situations.

7. Algorithms and performance — words 121–140

An algorithm should work for the whole allowed input range, not only the example used during development.

linear search — Checking successive items until matches are found.
maximum finding — Locating the largest value in a collection.
minimum finding — Locating the smallest value in a collection.
accumulator — A variable used to build a running total.
counter — A variable tracking how many events occur.
sentinel value — A special value marking when an operation should stop.
iteration count — The number of times a loop executes.
nested loop — A loop within another loop.
conditional branch — A path taken when a condition is met.
correctness — Whether a method meets its stated specification.
efficiency — How effectively a solution uses time or resources.
time complexity — How required operations grow with input size.
space complexity — How required storage grows with input size.
edge case — An unusual permitted input needing special attention.
off-by-one error — A counting or boundary mistake involving one position.
invariant — A statement remaining true at a defined stage of a process.
initialisation — Preparing the starting values of a program.
update step — A change to a program’s state during execution.
generic solution — A method capable of handling a class of cases.
code reuse — Applying an existing implementation in another context.

8. Spreadsheets and data — words 141–160

Formula copying changes some references and fixes others. Check whether the spreadsheet continues to calculate the right quantities.

workbook — A spreadsheet file containing one or more sheets.
worksheet — A page of cells within a workbook.
cell — The intersection of a row and a column.
cell range — A group of specified spreadsheet cells.
relative reference — A reference that changes position when copied.
absolute reference — A reference fixed in row and column when copied.
mixed reference — A reference locking only the row or column.
formula — An expression calculated by spreadsheet software.
spreadsheet function — A built-in operation used in a cell formula.
conditional formatting — Appearance changed automatically according to rules.
Goal Seek — A feature finding the input needed for a target result.
lookup — Finding related data from a search value.
exact match — A lookup requiring the precise requested value.
approximate match — A lookup returning a result based on defined range rules.
SUMIF — A function summing items meeting a condition.
COUNTIF — A function counting cells meeting a condition.
VLOOKUP — A function finding a value through a table’s first column.
INDEX — A function returning a value at a specified table position.
MATCH — A function finding the relative location of a value.
data type — The form of data such as number text or date.

9. Networks and connectivity — words 161–180

Network delays, capacity and addressing describe different properties. A fast connection does not guarantee trustworthy websites.

computer network — A group of connected devices sharing data.
local area network — A network covering a limited place.
wide area network — A network spanning distant locations.
Internet — The global network of interconnected networks.
router — A device forwarding packets between networks.
switch — A device forwarding network data within a local network.
network interface — A component that lets a device communicate on a network.
IP address — An address identifying a network interface for communication.
MAC address — A hardware-related link-layer network identifier.
packet — A unit of data transmitted through a network.
protocol — Rules defining how systems exchange information.
TCP — A transport protocol supporting reliable ordered delivery.
UDP — A transport protocol sending datagrams without delivery guarantees.
DNS — A service relating domain names to network information.
HTTP — A protocol for requesting and transferring web resources.
HTTPS — HTTP traffic protected by a TLS connection.
bandwidth — The quantity of data a link can carry per unit time.
latency — The delay before requested information arrives.
packet loss — Packets that are sent but fail to arrive successfully.
firewall — A control filtering network traffic according to rules.

10. Ethics and impact of computing — words 181–200

Technology choices affect privacy, inclusion, resource use and security. Technical feasibility alone does not make a system responsible.

personal data — Information relating to an identifiable person.
privacy — Control over personal information and unwanted intrusion.
data security — Protecting data against unauthorised access or modification.
consent — Permission given with adequate understanding.
encryption — Transforming data using a cryptographic process.
authentication — Checking a claimed user or system identity.
authorisation — Controlling which resources an identity may access.
phishing — A deceptive attempt to obtain secrets or provoke harmful actions.
malware — Software designed to harm or exploit devices and data.
social engineering — Manipulating people to evade security measures.
digital footprint — Records left by online actions and system use.
algorithmic bias — Systematic unfair differences arising from computing processes.
fairness — Appropriate equitable treatment by systems and decisions.
accessibility — Usability for people with varied abilities.
intellectual property — Legal rights associated with creative works and inventions.
copyright — Rights granted to original works of expression.
plagiarism — Passing another person’s work or ideas off as one’s own.
electronic waste — Discarded electrical and electronic devices.
environmental impact — Effects of activities and products on natural systems.
responsible computing — Building and using digital systems with care for people.

What Is Assessed in 2027 SEC G3 Computing K349?

The published 2027 K349 syllabus identifies five modules: Computing Fundamentals, Algorithms and Programming, Spreadsheets, Networking and Impact of Computing. Paper 1 is a written examination lasting two hours, carrying 60% of the overall marks. It includes a mix of multiple-choice, short-answer, matching, cloze and structured questions.

Paper 2 is a two-hour-thirty-minute laboratory-based examination carrying 40%. Students use spreadsheets, Python and JupyterLab, complete one spreadsheet question and four to five programming questions, and submit the required digital files. Correct working, careful saving and appropriate data handling matter alongside understanding terminology.

G1 Computing K127 and G2 Computing K237 are different routes. Their syllabuses and software expectations should not be mixed with the G3 K349 requirements. The year-specific document is authoritative for question content and what software or Python functions may be used.

Worked Examples: From a Word to an Actual Computing Decision

1. Binary and hexadecimal represent the same number

The decimal value 26 can be expressed as binary 11010₂ or hexadecimal 1A₁₆. In binary, the relevant place values are 16 + 8 + 2. In hexadecimal, A denotes ten and the value is 1 × 16 + 10. A student who confuses the number system with the quantity may produce the wrong conversion, even while doing ordinary addition correctly.

Data size also requires attention to units. A byte normally contains eight bits. A kibibyte refers to 1024 bytes; a kilobyte ordinarily refers to 1000 bytes in SI naming. If a question specifies the unit, use that convention rather than treating every storage suffix as interchangeable.

2. A logic gate changes a system’s behaviour

Imagine a classroom alert that should activate only when a door sensor is triggered and the monitoring mode is on. Let D and M represent the two Boolean inputs. The required expression is D AND M, which is true only when both are true. Replacing AND with OR makes the system activate in more situations. The original example is educational, not a safety-certified alarm design.

A truth table is a compact way to test the behaviour for all combinations. For two inputs there are four possibilities. Write the expected output before drawing a circuit. An incorrect choice of logic gate is an error in the design’s requirement interpretation, not just in its diagram.

3. A Python loop must match the exact comparison

To count the number of readings strictly greater than five, use an accumulator and a condition. The sample code below prints 2 because 8 and 10 qualify but 4, 5 and 1 do not.

readings = [4, 5, 8, 10, 1]
count = 0
for reading in readings:
    if reading > 5:
        count += 1
print(count)  # 2

The for statement provides iteration. The if provides selection. The count variable stores a changing total. If the question said ‘at least five’, the comparison should be >= instead. One short English phrase changes which values satisfy the rule.

4. Boundary tests find mistakes that everyday examples miss

Suppose a permitted age is from twelve to sixteen, inclusive. A programmer writes 12 < age < 16, incorrectly rejecting both valid endpoints. The correct test is 12 <= age <= 16. Try inputs 11, 12, 13, 16 and 17. Those examples reveal the difference between exclusion, inclusion and invalid-input handling.

A syntax error means the language’s written rules were violated. A runtime error happens during execution, perhaps when a program attempts to divide by zero. A logic error may leave the program running but calculate the wrong result. Distinguishing these types suggests the first useful debugging action.

5. A list and a dictionary solve different tasks

An ordered list such as [3, 6, 9] allows the program to get the first value at position 0. A dictionary such as {'apple': 3, 'pear': 6} associates keys with values. The programmer asks whether the problem needs positional access, a key-based lookup or both. Choosing a structure based on an unexplained habit can make later code harder to reason about.

The difference becomes visible when new data is added. Appending a list item usually changes its length and creates another position. Adding a dictionary entry creates or updates a key association. The system’s requirements, rather than which option uses fewer lines of code, should drive the choice.

6. Relative and absolute spreadsheet references

Imagine a table where B2 stores a quantity and C2 stores a unit price. A formula =B2*C2 gives the line total; when copied down one row, its relative references become B3 and C3. If a fixed rate is held in F1, using =D2*(1+$F$1) keeps that rate fixed while D2 moves to D3. Do not assume F1 holds a specific real-world tax rate; it is an illustrative input.

When a long spreadsheet produces plausible totals, check the copied formulas rather than assuming the first correct cell proves the entire column is correct. The distinction between relative, absolute and mixed references can determine whether a student’s model works after the first row.

7. Algorithmic correctness needs more than one test

Suppose a function finds the maximum element of a list and starts by setting the answer to zero. That works for [4, 7, 2] but fails for [-8, -3, -5], where the maximum should be -3. A generic method can initialise from the first list element after checking that the input is not empty. A successful result on a friendly example does not prove that an algorithm is correct.

A useful dry run records the variable’s value after each item is processed. This makes the point of failure observable. Fix the problem, then retest the original input and other cases. The development process should be iterative rather than treating the first program to run as complete.

8. Bandwidth is not latency

Bandwidth indicates how much data a network connection can carry per second. Latency concerns delay before information arrives. A large download can benefit from increased bandwidth; an interactive request can still feel sluggish when latency is high. Packet loss, congestion or server processing can create further delays. Use these words accurately when discussing network performance.

DNS helps translate a familiar domain name into network information. Routers forward traffic between networks; switches connect devices within a network; HTTPS helps protect data while it travels. None of these mechanisms guarantees that content is trustworthy or that a user will never fall for a fraudulent message.

The Error-Repair Routine Students Can Reuse

Read the requirement exactly: underline conditions such as ‘strictly greater than’, ‘all’ or ‘first occurrence’.
Predict one expected result: work it out manually before running code.
Test boundary and invalid inputs: include empty strings, empty lists and extreme values where permitted.
Inspect variable states: use print statements or a trace table.
Fix one issue at a time: avoid introducing unrelated changes while debugging.
Retest existing behaviour: confirm the fix has not broken something that worked.
Save the required file: the correct program must also be submitted in the form requested.

Record mistakes in a short notebook with four columns: requirement, failing test, reason for failure and successful revised test. A student’s repeated ‘off-by-one’ mistake is different from a recurring problem with Python types or spreadsheet references. Diagnosing the pattern helps the next study session target what matters.

Four-Week Vocabulary and Practical Revision Plan

Week 1 — Hardware and logic: revise words 1–50, practise number conversions and explain truth tables with two and three inputs.
Week 2 — Python constructs: revise words 51–100 and write short programs using conditions, loops, lists and functions.
Week 3 — Testing and spreadsheets: revise words 101–150, test a small algorithm with normal and boundary inputs, and practise copied spreadsheet formulas.
Week 4 — Networks and responsibility: revise words 151–200, draw a simple network, check security and privacy distinctions, and finish with a supervised practical exercise.

A daily routine can be as simple as ten unfamiliar terms, one short programming task and a review of yesterday’s errors. At the end of the week, choose twenty words at random. Award one point for the definition and another for an appropriate example. The maximum of forty points is a personal diagnostic, not a predicted examination grade.

Computing Beyond the Exam: What These Words Help Readers Do

The same vocabulary makes everyday digital choices more sensible. When comparing external storage and online backups, understand memory, redundancy, capacity and security. When receiving a suspicious message, distinguish authentication from authorisation and know why encryption alone does not prevent phishing. When judging an algorithmic recommendation, ask whether the training or input data systematically excludes some people.

A computing system can be technically efficient while still creating harm. For example, an application might collect far more personal information than it needs. Consent, privacy and appropriate retention matter even if the software has strong encryption. Technology can help people with disabilities when interfaces are accessible, but poorly considered systems can exclude them. These are practical design concerns, not just examination definitions.

Coding also trains disciplined explanation. A reader who can say what a function takes as input, what it returns, what conditions it assumes and how it behaves when something goes wrong is better able to collaborate with engineers, teachers and other users. That confidence is valuable in business, healthcare, data journalism and public services.

Frequently Asked Questions

Is this the official SEAB list of 200 Computing words?

No. These are independently curated terms, including extension vocabulary. The official syllabus determines the examinable scope and software requirements.

What is the 2027 G3 Computing paper format?

The K349 syllabus specifies Paper 1 as a two-hour written exam worth 60%, and Paper 2 as a two-hour-thirty-minute lab-based exam worth 40%. Both are compulsory.

Must students practise Python and spreadsheets?

Yes. The practical examination assesses functioning work and requires students to use software, not merely recognise terminology.

Is K349 the same as G2 K237 or G1 K127?

No. These are distinct SEC level routes with different syllabuses and software expectations. Always use the subject code in the student’s registration.

Does the glossary contain every examinable Python built-in?

It does not claim to. Check the official K349 quick reference and syllabus for permitted or expected operations, functions and language constructs.

How do I know when a student understands an algorithm?

Ask them to state the input, predict the result on two cases, explain the steps, identify one boundary risk and then write or trace the algorithm without copying a known solution.

Official References and Connected Guides

Updated 13 November 2026. This independent learning guide is not an official SEAB vocabulary list. Follow the actual examination-year syllabus and school instructions.

Top 200 Vocabulary for Secondary 4 Computing | O-Level and SEC 2027 Python, Algorithms, Spreadsheets and Networking