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Algorithms & computational thinking

What counts as computational thinking

OCR splits this into decomposition, abstraction and algorithmic thinking.

  • Decomposition: breaking a big problem into smaller sub-problems that are easier to solve.
  • Abstraction: removing unnecessary detail and keeping only what matters for the problem.
  • Algorithmic thinking: designing the ordered steps (the algorithm) that solve the problem.

An algorithm is a precise, finite sequence of steps that solves a problem or completes a task. It must be unambiguous, have a clear start and end, and terminate.

Representing algorithms

OCR expects you to read and write algorithms in three forms:

  • Flowcharts: use standard symbols. Oval = start/stop, rectangle = process, parallelogram = input/output, diamond = decision (always has two exits, usually yes/no).
  • Pseudocode: structured English-like code using sequence, selection (IF/ELSE) and iteration (FOR/WHILE), similar in style to the OCR Exam Reference Language.
  • Structured English: numbered plain-language steps.

All three must show the same three constructs: sequence (steps in order), selection (decisions/branches) and iteration (loops).

Searching algorithms

  • Linear search: checks each item in turn from the start. Works on unsorted or sorted lists. Worst case checks every item, so it is O(n).
  • Binary search: only works on a sorted list. Repeatedly checks the middle item and discards the half that cannot contain the target. Much faster on large lists (O(log n)) but the data must be sorted first.

Sorting algorithms

  • Bubble sort: repeatedly compares adjacent pairs and swaps them if out of order; the largest values 'bubble' to the end each pass. Simple but slow on large lists.
  • Insertion sort: builds a sorted section one item at a time, inserting each new item into its correct place.
  • Merge sort: splits the list into halves repeatedly until single items, then merges them back in order. Efficient on large lists (O(n log n)) but uses more memory.

Common mistakes

  • Confusing algorithm efficiency with correctness — an algorithm can be correct but still inefficient.
  • Forgetting binary search needs sorted data first.
  • Mixing up decomposition (breaking down) with abstraction (removing detail) — they are different skills.
  • In flowcharts, forgetting decision diamonds need two labelled paths.
  • Writing pseudocode that never terminates (missing a loop exit condition).
  • Trace tables: forgetting to update every variable on every pass, not just the one that changed.

Trace tables

Used to test/dry-run an algorithm by hand. Draw a column for each variable and a row for each iteration or step, updating values in order and noting any output.

  • An algorithm must be a finite, unambiguous sequence of steps that always terminates.
  • Decomposition breaks a problem into smaller parts; abstraction removes unnecessary detail; together with algorithmic thinking these form computational thinking.
  • The three basic programming constructs are sequence, selection and iteration.
  • Flowchart diamonds represent decisions and always have exactly two exit paths.
  • Linear search checks items one by one from the start and works on unsorted data.
  • Binary search only works on sorted data and repeatedly halves the search space.
  • Binary search is much faster than linear search on large lists (O(log n) vs O(n)).
  • Bubble sort repeatedly swaps adjacent out-of-order pairs until the list is sorted.
  • Insertion sort builds up a sorted section by inserting each new item into its correct position.
  • Merge sort splits a list into halves repeatedly then merges them back in order, and is efficient for large lists.
  • A trace table has one column per variable and one row per step or iteration, used to dry-run an algorithm.
  • Pseudocode, flowcharts and structured English are three equivalent ways to represent the same algorithm.
What are the three components of computational thinking?
Decomposition, abstraction, and algorithmic thinking.
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Define an algorithm.
A finite, unambiguous sequence of steps that solves a problem and always terminates.
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What is decomposition?
Breaking a large problem down into smaller, easier-to-solve sub-problems.
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What is abstraction?
Removing unnecessary detail so only the information relevant to solving the problem remains.
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Name the three basic programming constructs.
Sequence, selection, and iteration.
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What shape represents a decision in a flowchart, and how many exits does it have?
A diamond, with exactly two exits (usually labelled yes/no).
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How does linear search work?
It checks each item in a list one by one from the start until it finds the target or reaches the end.
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What condition must be true before you can use binary search?
The data must already be sorted.
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How does binary search find an item?
It repeatedly checks the middle item of the remaining list and discards the half that cannot contain the target.
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How does bubble sort work?
It repeatedly compares adjacent pairs and swaps them if out of order, so the largest values move to the end each pass.
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How does insertion sort work?
It builds a sorted section of the list one item at a time, inserting each new item into its correct position.
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How does merge sort work?
It repeatedly splits the list in half until each part has one item, then merges the parts back together in order.
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Why is binary search faster than linear search on large sorted lists?
Because it halves the search space each step (O(log n)) instead of checking every item (O(n)).
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What is a trace table used for?
To dry-run an algorithm by hand, recording how each variable changes at every step or iteration.
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What are the three equivalent ways to represent an algorithm expected by OCR?
Flowcharts, pseudocode, and structured English.
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Programming fundamentals

What are programming fundamentals?

This is the core toolkit every programmer needs: variables, constants, sequence, selection, iteration, operators and data types. OCR J277 expects you to read, trace and write pseudocode or code (Python-style) using these building blocks.

Data types

  • Integer: whole numbers, e.g. 7 or -3.
  • Real (float): numbers with a decimal point, e.g. 3.14.
  • Boolean: only True or False.
  • Character: a single letter, digit or symbol.
  • String: a sequence of characters, e.g. 'hello'.
  • Casting converts one type to another, e.g. int('5') turns the string '5' into the integer 5. Common mistake: forgetting that input() in Python always returns a string, so numbers typed in must be cast before doing maths on them.

Variables and constants

A variable stores a value that can change while the program runs; a constant stores a value that stays fixed. Use meaningful identifier names. Assignment uses = (e.g. score = 0), while == tests equality in a condition. Mixing these up is one of the most common exam and coding errors.

The three constructs

  • Sequence: instructions run in order, top to bottom.
  • Selection: IF, ELSE IF (ELIF), ELSE branch the logic based on a condition; nested IFs place one selection inside another.
  • Iteration: count-controlled loops (FOR) repeat a set number of times; condition-controlled loops (WHILE, or REPEAT...UNTIL) repeat until a condition is met. A WHILE loop checks the condition BEFORE running the body, so it might run zero times; REPEAT...UNTIL checks AFTER, so it always runs at least once.

Operators

  • Arithmetic: + - * / (division), // (integer/floor division, drops remainder), MOD or % (remainder), ^ or ** (exponent/power).
  • Relational (comparison): > < >= <= == != — these always produce a Boolean result.
  • Boolean/logical: AND, OR, NOT combine or invert conditions, e.g. IF age >= 18 AND hasID == True.

String handling and arrays

Know string operations: length (LEN), concatenation (+), substring/slicing, converting case, and finding a character's position. Arrays (or lists) store multiple values under one identifier, indexed from 0 in most exam pseudocode and in Python — a classic off-by-one mistake is thinking the first item is index 1.

Nested and combined structures

Expect exam questions that nest loops inside selections, or selections inside loops, then ask you to trace the output by hand — write out variable values step by step in a trace table rather than guessing.

  • Assignment uses a single = ; equality testing in a condition uses == — never confuse the two.
  • Python's input() always returns a string, so numbers must be cast with int() or float() before arithmetic.
  • Arrays and Python lists are zero-indexed: the first element is at position 0, not 1.
  • // performs integer (floor) division, discarding any remainder; MOD (or %) returns just the remainder.
  • A WHILE loop tests its condition before the loop body runs, so it can execute zero times.
  • A REPEAT...UNTIL loop tests its condition after the body runs, so it always executes at least once.
  • FOR loops are count-controlled — used when the exact number of repetitions is known in advance.
  • The four core data types in OCR J277 are integer, real, Boolean and character/string.
  • Boolean operators AND, OR and NOT combine or invert conditions and always evaluate to True or False.
  • Relational operators (> < >= <= == !=) always produce a Boolean result, used to control selection and iteration.
  • Casting converts a value from one data type to another, e.g. str(5) turns the integer 5 into the string '5'.
  • Nested selection means placing one IF statement entirely inside the body of another IF statement.
What is the difference between = and == in code?
= is assignment (stores a value in a variable); == is a comparison operator that tests if two values are equal.
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Why must you cast the result of input() before doing maths with it?
input() always returns a string, so it must be cast with int() or float() before it can be used in arithmetic.
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What index does the first element of an array have?
0 — arrays and lists are zero-indexed.
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What does the // operator do?
Integer (floor) division — divides and discards the remainder, e.g. 7 // 2 = 3.
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What does MOD (or %) return?
The remainder after division, e.g. 7 MOD 2 = 1.
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When does a WHILE loop check its condition?
Before running the loop body, so the body may run zero times.
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When does a REPEAT...UNTIL loop check its condition?
After running the loop body, so the body always runs at least once.
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What type of loop is a FOR loop?
Count-controlled — it repeats a known, fixed number of times.
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Name the four core data types tested in OCR Programming Fundamentals.
Integer, real (float), Boolean, and character/string.
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What do the Boolean operators AND, OR and NOT do?
They combine or invert conditions and always produce a True or False result.
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What is casting?
Converting a value from one data type to another, e.g. int('5') to 5, or str(5) to '5'.
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What is the difference between a variable and a constant?
A variable's stored value can change while the program runs; a constant's value stays fixed.
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What are the three basic programming constructs?
Sequence (instructions in order), selection (IF/ELIF/ELSE branching), and iteration (loops).
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What does nested selection mean?
Placing one IF statement entirely inside the body of another IF statement.
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Why is a trace table useful for loop and selection questions?
It records each variable's value step by step as the code runs, avoiding guesswork when working out the output.
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Data representation & binary

Why binary?

Computers are built from transistors that are either on or off, so they only understand two states: 1 and 0. Every piece of data, numbers, text, images, sound, must be converted into binary (base 2) to be stored or processed.

Bits, nibbles, bytes

  • A bit is a single binary digit, 0 or 1.
  • A nibble is 4 bits.
  • A byte is 8 bits, the standard unit computers use to store one character.
  • With n bits you can represent 2^n different values. An 8-bit byte gives 2^8 = 256 values, so unsigned range is 0 to 255.

Denary to binary and back

To convert denary to binary, use the place values 128 64 32 16 8 4 2 1 (for 8 bits) and subtract the biggest value that fits, marking a 1, then 0 for values you skip. To convert binary to denary, add up the place values where there is a 1. Common mistake: writing place values left to right without remembering they halve each time, or miscounting the number of bits.

Binary addition and overflow

Add binary like denary but carry when a column reaches 2 (10 in binary). If the result needs more bits than the register holds, this is overflow, and the extra bit is lost, giving a wrong answer.

Hexadecimal

Hex (base 16) uses digits 0-9 then A-F (A=10 up to F=15). Each hex digit represents exactly 4 bits (one nibble), so a byte is written as two hex digits, e.g. 11110000 = F0. Hex is used to shorten binary for things like MAC addresses and colour codes because it is far easier for humans to read.

Binary shifts

A logical left shift multiplies the value by 2 for each place shifted; a right shift divides by 2 (rounding down), for example 00000011 shifted left once becomes 00000110 (3 becomes 6).

Units of storage

1 kilobyte (KB) = 1000 bytes, 1 megabyte (MB) = 1000 KB, 1 gigabyte (GB) = 1000 MB, 1 terabyte (TB) = 1000 GB (OCR uses the decimal, 1000-based, convention, not 1024).

Common mistakes

  • Forgetting leading zeros when writing an 8-bit binary number.
  • Mixing up KB (1000 bytes) with the old kibibyte (1024 bytes).
  • Confusing hex conversion by not splitting into nibbles first.
  • Forgetting overflow can occur in binary addition when the register is full.
  • 1 byte = 8 bits, and 8 bits can represent 256 different values (0 to 255).
  • With n bits you can represent 2^n different values.
  • Binary place values for 8 bits are 128, 64, 32, 16, 8, 4, 2, 1.
  • Hexadecimal uses base 16 with digits 0-9 and A-F, where A=10 and F=15.
  • One hex digit always represents exactly 4 bits (a nibble).
  • A byte is written as exactly two hex digits, for example F0 is 11110000.
  • Overflow happens when a binary addition produces more bits than the register can hold, losing data.
  • A left binary shift multiplies the value by 2 per shift; a right shift divides by 2 per shift.
  • OCR uses decimal units: 1 KB = 1000 bytes, 1 MB = 1000 KB, 1 GB = 1000 MB, 1 TB = 1000 GB.
  • A nibble is 4 bits, half of a byte.
  • Computers use binary because transistors only have two stable states, on and off.
How many bits are in a byte?
8 bits.
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How many bits are in a nibble?
4 bits.
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How many different values can 8 bits represent?
256 values (0 to 255), because 2^8 = 256.
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What is the general formula for how many values n bits can represent?
2^n
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What are the 8-bit binary place values?
128, 64, 32, 16, 8, 4, 2, 1.
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What base is hexadecimal?
Base 16.
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What digits does hexadecimal use and what do A to F equal?
0-9 then A-F, where A=10, B=11, C=12, D=13, E=14, F=15.
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How many bits does one hex digit represent?
4 bits (one nibble).
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What is 11110000 in hexadecimal?
F0
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What is binary overflow?
When an addition produces a result with more bits than the register can hold, so the extra bit is lost and the answer is wrong.
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What effect does a logical left shift by one place have on a binary value?
It multiplies the value by 2.
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What effect does a logical right shift by one place have on a binary value?
It divides the value by 2, rounding down.
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Under OCR's convention, how many bytes are in 1 kilobyte?
1000 bytes.
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Why do computers use binary rather than denary?
Because transistors only have two reliable states, on and off, matching 1 and 0.
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Computer systems & architecture

The CPU and the fetch-execute cycle

The CPU (Central Processing Unit) carries out instructions from programs. It runs the fetch-decode-execute cycle constantly: fetch an instruction from memory, decode what it means, execute it, then move on.

  • PC (Program Counter): holds the address of the NEXT instruction to fetch.
  • MAR (Memory Address Register): holds the address currently being read/written.
  • MDR (Memory Data Register): holds the data/instruction just fetched.
  • Accumulator: stores results of calculations during execution.

CPU performance factors

Three things affect how fast a CPU runs:

  • Clock speed: measured in GHz (billions of cycles per second). Higher = more fetch-execute cycles per second.
  • Number of cores: each core can process instructions independently, so more cores can mean more done in parallel (but software must support multi-core use).
  • Cache size: small, very fast memory on the CPU that stores frequently used data, so the CPU spends less time waiting on slower RAM.

Common mistake: saying 'more cores always means faster' — it only helps if the software is written to use multiple cores.

Von Neumann architecture

Most computers use the Von Neumann model: one memory store holds both data AND instructions, and they share the same bus system. Key idea: since data and instructions look the same in memory, the CPU must know which is which from context.

Embedded systems

An embedded system is a computer built into a larger device to perform a specific, dedicated task (e.g. a washing machine controller, a car engine management system, a microwave). Common mistake: forgetting that embedded systems usually run ONE fixed program, unlike a general-purpose computer.

Primary storage: RAM and ROM

  • RAM (Random Access Memory): volatile (loses contents when powered off), holds currently running programs and data, read AND write.
  • ROM (Read Only Memory): non-volatile, holds start-up instructions (e.g. BIOS), cannot normally be written to.

Common mistake: confusing 'volatile' (loses data when off) with 'non-volatile' (keeps data when off) — RAM is volatile, ROM and storage like SSDs are non-volatile.

Buses

Data travels around the computer along buses:

  • Address bus: carries the memory address (one-directional, CPU to memory).
  • Data bus: carries the actual data or instruction (bi-directional).
  • Control bus: carries control signals like read/write commands.
  • The fetch-decode-execute cycle repeats continuously while a CPU runs a program.
  • The Program Counter (PC) holds the address of the next instruction to be fetched.
  • Clock speed is measured in GHz and shows how many fetch-execute cycles happen per second.
  • Cache is small, very fast memory on the CPU used to store frequently accessed data.
  • RAM is volatile memory: it loses all its contents when the power is turned off.
  • ROM is non-volatile memory: it keeps its contents (like startup instructions) without power.
  • Von Neumann architecture stores both data and instructions in the same memory using the same bus.
  • The address bus carries memory addresses and is one-directional (CPU to memory).
  • The data bus carries the actual data or instructions and is bi-directional.
  • An embedded system is a computer built into a device to perform one specific dedicated function.
  • More CPU cores only speed up processing if the software is designed to use multiple cores.
  • The MAR (Memory Address Register) holds the address currently being accessed in memory.

Networks & security

Networks: types and hardware

A LAN (Local Area Network) covers one site, like a school or office.

A WAN (Wide Area Network) covers a large geographical area and links multiple LANs, often using third-party infrastructure like phone lines or satellites - the internet is the largest WAN.

Key hardware: a switch connects devices within a LAN and directs data only to the correct device using MAC addresses.

A router connects different networks together (e.g. a LAN to the internet) and directs data using IP addresses.

A network interface card (NIC) allows a device to connect to a network, and has a unique MAC address burned in at manufacture.

Wi-Fi uses radio waves and needs a wireless access point; Ethernet uses cables and is generally faster and more reliable.

Network topologies

In a star topology, every device connects to a central switch or hub - if one cable fails only that device drops off, but if the switch fails the whole network goes down.

In a mesh topology, devices connect directly to many other devices, giving high reliability with no single point of failure, but it is expensive and complex to set up.

The internet and protocols

DNS (Domain Name System) translates human-readable domain names into IP addresses.

An IP address identifies a device on a network; IPv4 uses 32 bits (e.g. 192.168.1.1) giving about 4.3 billion addresses, while IPv6 uses 128 bits to solve address exhaustion.

TCP/IP is the protocol stack that governs how data is split into packets, addressed, sent and reassembled.

HTTP transfers web pages in plain text; HTTPS does the same but encrypts the data using TLS/SSL, shown by the padlock icon.

Common mistake: students confuse HTTP and HTTPS - only HTTPS is encrypted, HTTP is not secure for sensitive data.

Other key protocols: FTP transfers files, SMTP sends email, IMAP/POP3 receive email.

Network security threats

Malware includes viruses (attach to files and self-replicate), worms (self-replicate without a host file, spreading across networks), trojans (disguised as legitimate software), spyware and ransomware (encrypts files and demands payment).

Social engineering exploits people rather than technology - phishing sends fake emails/messages to trick users into giving up data.

Other threats: brute-force attacks (trying many passwords), denial-of-service (DoS) attacks flood a server with traffic to take it offline, SQL injection inserts malicious code into a database via an input box, and man-in-the-middle attacks intercept communication between two parties.

Preventing vulnerabilities

Penetration testing simulates an attack to find weaknesses before criminals do.

Anti-malware software detects and removes malicious programs.

Firewalls monitor and control incoming/outgoing traffic based on security rules, blocking unauthorised access.

User access levels restrict what different users can see or do on a system.

Passwords should be strong (mixing cases, numbers, symbols) and systems should use encryption to protect data in transit and at rest.

Common mistake: encryption prevents data being read if intercepted, but does NOT prevent an attack happening in the first place - it is not the same as a firewall.

Physical security (locks, biometrics) also matters alongside software defences.

  • A LAN covers one site; a WAN covers a large geographical area and links multiple LANs.
  • A switch uses MAC addresses to direct data within a LAN; a router uses IP addresses to connect different networks.
  • IPv4 addresses are 32 bits (about 4.3 billion addresses); IPv6 addresses are 128 bits.
  • DNS translates domain names into IP addresses.
  • HTTPS encrypts data with TLS/SSL; HTTP does not encrypt data.
  • A star topology has a single point of failure at the central switch; a mesh topology has no single point of failure.
  • A virus needs a host file and self-replicates; a worm self-replicates without needing a host file.
  • Phishing is a social engineering attack that tricks users via fake emails or messages.
  • A denial-of-service (DoS) attack floods a server with traffic to make it unavailable.
  • SQL injection inserts malicious code through an input box to attack a database.
  • A firewall monitors and controls network traffic based on rules; it is not the same as encryption.
  • Penetration testing simulates real attacks to find security weaknesses before criminals exploit them.
What does LAN stand for and what does it cover?
Local Area Network - covers one site, such as a school or office.
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What does WAN stand for and what does it cover?
Wide Area Network - covers a large geographical area, linking multiple LANs.
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What addresses does a switch use to direct data?
MAC addresses.
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What addresses does a router use to direct data?
IP addresses, to connect different networks together.
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How many bits does an IPv4 address use, and how many addresses does that give?
32 bits, giving about 4.3 billion addresses.
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Why was IPv6 introduced?
To solve IPv4 address exhaustion, using 128-bit addresses instead of 32-bit.
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What does DNS do?
Translates human-readable domain names into IP addresses.
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What is the key difference between HTTP and HTTPS?
HTTPS encrypts data using TLS/SSL; HTTP sends data in plain, unencrypted text.
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What is the main weakness of a star topology?
If the central switch fails, the whole network goes down.
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What is the main advantage of a mesh topology?
No single point of failure, since devices connect directly to many others.
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What is the difference between a virus and a worm?
A virus attaches to a host file and self-replicates; a worm self-replicates across networks without needing a host file.
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What is phishing?
A social engineering attack using fake emails or messages to trick users into giving up data.
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What is a denial-of-service (DoS) attack?
An attack that floods a server with traffic to take it offline.
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What is SQL injection?
Inserting malicious code into a database via an input box on a form or website.
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What is the purpose of penetration testing?
To simulate an attack and find security weaknesses before criminals can exploit them.
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Ethical, legal & environmental issues

Why this topic matters

OCR GCSE Computer Science expects you to discuss the wider impact of computing on individuals and society. Questions are usually extended-response (6-mark) 'discuss' or 'evaluate' style, so you need balanced points, not just a list.

Ethical issues

  • Ethics = right and wrong behaviour, not always covered by law.
  • Examples: AI decision-making bias, autonomous vehicles making life-or-death choices, surveillance and facial recognition, deepfakes, use of personal data for targeted advertising, digital divide (not everyone can afford tech).
  • Common exam angle: 'is it right to do X even though it is legal?'

Legal issues (know the actual laws)

  • Data Protection Act 2018 / UK GDPR: controls how personal data is collected, stored and used; organisations must keep data accurate, secure and only as long as necessary.
  • Computer Misuse Act 1990: makes it illegal to gain unauthorised access to computer material, unauthorised access with intent to commit further offences, and unauthorised modification of data (including malware and DoS attacks).
  • Copyright, Designs and Patents Act 1988: protects software, music, images and other digital works from being copied or distributed without permission.
  • Regulation of Investigatory Powers Act 2000 (RIPA): governs surveillance and interception of communications by public bodies.
  • Common mistake: mixing up the Data Protection Act (personal data handling) with the Computer Misuse Act (hacking/unauthorised access) — examiners specifically test this confusion.

Environmental issues

  • E-waste: old devices contain toxic materials (lead, mercury) and are often shipped abroad, causing pollution.
  • Energy use: data centres and server farms consume huge amounts of electricity and water for cooling.
  • Manufacturing: mining rare earth metals for chips and batteries damages ecosystems.
  • Positive side: computers can reduce environmental impact too, e.g. video conferencing cuts travel, smart meters reduce energy waste, cloud computing can be more efficient than many small local servers.

Cultural issues

  • Impact on employment (automation replacing jobs vs creating new tech jobs).
  • Changes to how people communicate, work and access information.
  • Always give both a benefit and a drawback in a 6-mark answer, then a justified conclusion — that's how the marks are actually awarded.

Exam technique tip

When asked to 'discuss', structure as: point, explanation, example, counterpoint, example, conclusion. Naming the correct Act by name (not just describing it vaguely) earns extra marks.

  • The Data Protection Act 2018 (UK GDPR) controls how personal data must be collected, stored and used.
  • The Computer Misuse Act 1990 covers three offences: unauthorised access, unauthorised access with intent to commit further crime, and unauthorised modification of data.
  • The Copyright, Designs and Patents Act 1988 protects software, music and images from unauthorised copying or distribution.
  • RIPA (Regulation of Investigatory Powers Act 2000) governs lawful interception and surveillance by public bodies.
  • Ethics covers what is morally right or wrong, which is not always the same as what is legal.
  • Data centres consume very large amounts of electricity and water, contributing to environmental impact.
  • E-waste often contains toxic materials such as lead and mercury and is frequently shipped to other countries for disposal.
  • Automation can remove some jobs but also creates new roles in tech, a key cultural/ethical trade-off.
  • Facial recognition and AI decision-making raise ethical concerns around bias and privacy even where their use is legal.
  • 6-mark 'discuss' questions require a balanced answer with points for and against plus a justified conclusion.
  • Video conferencing and smart meters are examples of technology reducing environmental impact.
  • Mining rare earth metals for chips and batteries causes ecological damage, an environmental issue linked to manufacturing.
What does the Data Protection Act 2018 regulate?
How personal data is collected, stored, used and kept secure by organisations.
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Name the three offences under the Computer Misuse Act 1990.
Unauthorised access; unauthorised access with intent to commit further offences; unauthorised modification of data.
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What does the Copyright, Designs and Patents Act 1988 protect?
Software, music, images and other digital works from being copied or distributed without permission.
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What is RIPA 2000 about?
It governs surveillance and interception of communications by public bodies.
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What is the difference between ethical and legal issues?
Legal issues are governed by law; ethical issues are about right and wrong, which may not be covered by law at all.
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Give two environmental impacts of computing.
High energy/water use in data centres, and toxic e-waste from discarded devices.
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Give one way technology can help the environment.
Video conferencing reduces travel; smart meters reduce energy waste; cloud computing can be more efficient.
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What ethical issue arises from AI decision-making?
AI systems can show bias, and it is unclear who is responsible when an AI makes a harmful decision.
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What structure should you use for a 6-mark 'discuss' exam answer?
Point, explanation, example, counterpoint, example, then a justified conclusion.
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Why are rare earth metals an environmental concern in computing?
Mining them for chips and batteries causes ecological and habitat damage.
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What is a cultural issue linked to automation?
It can remove existing jobs while creating new technology-based roles.
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Which Act would apply to someone hacking into a school's computer system?
The Computer Misuse Act 1990.
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Which Act would apply to a company misusing customers' personal details?
The Data Protection Act 2018 (UK GDPR).
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