GCSE Computer Science
Explore the different types of memory and storage used in computer systems.
Learn about RAM, ROM, and the differences between primary and secondary storage. Understand storage devices including hard disk drives, solid state drives, optical media, and how data is stored and retrieved.
This unit also covers virtual memory, the purpose of storage, and how to choose appropriate storage solutions for different needs.
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Your phone has 128GB of storage. Your computer has 16GB of RAM. Why are these different numbers—and why do you need both?
Show students a phone screenshot: 'Storage Almost Full - 127.5GB used of 128GB'. Then show a computer: '16GB RAM'. Ask: 'Why is your phone storage measured in hundreds of gigabytes, but RAM is only 16GB? What's the difference? Why can't we just have 128GB of RAM and be done with it?'
Resources:
Screenshot showing typical phone storage breakdown (Photos, Apps, System, etc.)
Windows/Mac screenshot showing RAM usage with multiple apps
Teacher Notes:
Let students speculate freely. Common misconceptions to note: 'RAM and storage are the same thing', 'More storage = faster computer'. Don't correct yet—we'll discover together.
Introduce the kitchen analogy: RAM is your worktop (where you actively work), secondary storage is your cupboards (where you keep everything). Explore: Why can't chefs work with everything in cupboards? Why don't we have massive worktops? Build towards: speed, volatility, cost, capacity trade-offs.
Resources:
Visual showing cluttered worktop (RAM) vs organised cupboards (storage)
Teacher Notes:
Extend the analogy: 'What happens when you turn the lights off?' (worktop gets cleared = RAM loses data). 'What if your worktop is too small for the recipe?' (leads to virtual memory in lesson 2).
Students receive cards with scenarios: 'Running Spotify', 'Your photo library', 'The app you're currently using', 'Files you downloaded last year', 'The operating system code being executed', 'Your saved game progress'. They sort into 'Needs to be in RAM right now' vs 'Lives in secondary storage'. Discuss edge cases and surprises.
Resources:
Set of 20+ scenario cards covering various storage situations
Two-column mat for RAM vs Storage sorting
Teacher Notes:
Key insight to draw out: some things MOVE between storage and RAM (e.g., opening an app loads it into RAM). This previews how the two work together.
Quick exploration: What storage is in different devices? Phones, laptops, games consoles, smart TVs, washing machines with digital displays. Students identify: Does this device have RAM? Does it have secondary storage? Why or why not?
Resources:
Photos of various devices including embedded systems
Teacher Notes:
Interesting discussion point: your washing machine probably has a tiny amount of ROM for its program, maybe a bit of RAM, but no secondary storage. Why?
In pairs, students prepare and deliver a 2-minute explanation to 'a confused parent': Why does a computer need BOTH RAM and storage? Can't it just have one? Peer assessment using success criteria.
Resources:
Criteria: mentions speed, volatility, capacity, gives an example, uses analogy
Teacher Notes:
This consolidates learning through explanation. Listen for misconceptions to address next lesson.
Explore why we don't just have one type of super-fast, unlimited storage. Introduce the concept of the memory hierarchy—registers, cache, RAM, SSD, HDD, tape—and how each level trades off speed for cost and capacity.
Connection: Understanding why multiple storage types exist helps students appreciate the design trade-offs in real computer systems, making RAM vs secondary storage feel like a natural solution rather than arbitrary complexity.
Further Reading:
Support:
Stretch:
Why does your laptop slow down when you open 50 Chrome tabs—and why does restarting it magically fix everything?
Show a task manager with 50 Chrome tabs eating 12GB of RAM. The system is crawling. Ask: What's actually happening here? Why doesn't the computer just... cope? What would happen if we opened 10 more tabs?
Resources:
Screenshot showing high RAM usage from browser tabs
Teacher Notes:
Students often have direct experience with this. Let them share their frustrations—it's genuine motivation to understand what's happening.
Interactive explanation of RAM: volatile (data lost when power off), fast access, used for currently running programs and data. Show physical RAM stick. Discuss: Why is RAM fast? (physically close to CPU, designed for rapid read/write). Why volatile? (cost of non-volatile fast memory).
Resources:
Actual RAM module to pass around or high-quality image
Diagram showing RAM's position between CPU and storage
Teacher Notes:
Key point: RAM is like a whiteboard—fast to write and erase, but wiped clean when you leave the room.
Contrast with ROM: non-volatile, read-only (or rarely written), stores essential startup instructions. Demo: what happens in the first 2 seconds when you press power? Before Windows/Mac loads, something has to tell the computer HOW to load Windows/Mac. That's ROM's job.
Resources:
Short clip showing BIOS/POST screen before OS loads
Teacher Notes:
Students might ask 'But I can update my BIOS?' - explain that modern 'ROM' is actually flash memory that CAN be written to, but is treated as read-only for daily operation.
Students categorise characteristics: Volatile, Non-volatile, Fast, Stores running programs, Stores boot instructions, Data lost when power off, Written during manufacturing, Written during use. Create a comparison table.
Resources:
Cards with RAM/ROM characteristics to sort
Two-column table for recording
Teacher Notes:
Quick activity to consolidate the key differences. Some characteristics might spark discussion—encourage this.
Introduce cache as even faster memory between CPU and RAM. Analogy: RAM is your desk, cache is the papers you're actively reading right now in your hands. Show cache sizes on a real CPU spec (tiny compared to RAM, but much faster).
Resources:
Real CPU specs showing cache sizes (L1, L2, L3)
Teacher Notes:
Students don't need to know L1/L2/L3 details for the exam, but seeing real specs makes it concrete.
Return to our Chrome problem: what if RAM fills up? Introduce virtual memory: the computer pretends it has more RAM by using secondary storage temporarily. Demonstrate: show the page file/swap on a real system. Why does this slow things down? (secondary storage is MUCH slower than RAM).
Resources:
Visual showing data swapping between RAM and storage
Windows/Mac virtual memory settings
Teacher Notes:
Key insight: virtual memory is a clever workaround, not a solution. It keeps the computer running when RAM is full, but at a severe speed cost.
Team quiz: rapid-fire questions about RAM, ROM, cache, virtual memory. Include scenario questions: 'Your computer is running slowly with many apps. Is this more likely a RAM or ROM problem?'
Resources:
20 quick-fire questions with answers
Teacher Notes:
Competitive element increases engagement. Note which topics need reinforcement.
Investigate why Chrome is famous for using so much RAM. Each tab runs as a separate process for security and stability, but this has memory costs. This connects to real conversations students have seen online.
Connection: Makes the abstract concept of RAM tangible through a real application students interact with daily, and shows why RAM capacity matters.
Further Reading:
When you press the power button, your computer has no idea how to be a computer—until ROM tells it. Brief exploration of BIOS/UEFI and the boot process.
Connection: Provides a concrete, visible example of ROM's purpose that students can observe on their own computers.
Further Reading:
Support:
Stretch:
Prerequisites: 1
A hard drive has moving parts spinning at 7,200 RPM. An SSD has no moving parts at all. Why would anyone still buy a hard drive in 2024?
Present the puzzle: 'I can buy a 2TB hard drive for £50, or a 2TB SSD for £150. Both store the same amount. Why would anyone pay 3x more?' Let students speculate, then reveal we'll investigate ALL the factors that make storage devices different.
Resources:
Current prices for equivalent HDD vs SSD from retailer
Teacher Notes:
Students often assume 'more expensive = better'. Challenge this—sometimes the cheaper option IS the right choice.
Interactive exploration of each type. Magnetic (HDD): show video of spinning platters and read head. Optical (CD/DVD/Blu-ray): demo with a disc and laser pointer. Solid State (SSD/Flash): explain no moving parts, stores in electrical circuits. Pass around actual examples of each.
Resources:
Slow-motion video showing HDD internals
Actual HDD, SSD, USB stick, SD card, CD/DVD, Blu-ray disc
Teacher Notes:
Note: spec says students don't need to understand component parts in detail, but seeing how different they are helps understanding.
Each group researches one storage type and 'competes' across six categories: capacity (how much?), speed (how fast?), portability (how easily moved?), durability (how tough?), reliability (how likely to fail?), cost (how expensive per GB?). Groups present findings, class creates comparison table.
Resources:
One per group with prompts for finding information about their storage type
Large class table to compile all findings
Teacher Notes:
This is the core exam content. The competitive framing makes dry comparison feel more engaging. Ensure each category is clearly understood before research begins.
Present real-world scenarios. Students must choose appropriate storage and justify: 'A photographer travelling through remote locations', 'A hospital storing patient records for 10 years', 'A gamer who wants fast game loading', 'A school archiving old yearbooks', 'Netflix streaming movies to millions'. Discuss why there's often no single 'right' answer.
Resources:
8-10 scenarios covering various use cases
Sentence starters: 'I chose ___ because...' for each characteristic
Teacher Notes:
Emphasise justification over 'right' answers. Some scenarios have multiple valid solutions. This mirrors exam questions which often require applied reasoning.
Individual exit ticket: 'A small business needs to back up 10TB of data weekly. They have limited budget but need data to last 5+ years. Recommend a storage solution and explain your choice using at least three of the six characteristics we studied.'
Resources:
Structured response template with space for characteristics
Teacher Notes:
This is exam-style question practice. Review responses to identify who needs additional support.
When you save to 'the cloud', it goes to a massive warehouse filled with hard drives. Explore why data centres still predominantly use HDDs (cost per terabyte), how they handle the inevitable failures, and the environmental impact of global data storage.
Connection: Shows why comparing storage technologies matters at scale—different choices for different contexts.
Further Reading:
Some nuclear missile systems still use floppy disks. Banks store backups on magnetic tape. Why do 'obsolete' technologies persist? Explores the trade-offs of reliability, cost, and 'if it ain't broke'.
Connection: Reinforces that 'best' storage depends entirely on the use case—there's no universal winner.
Further Reading:
Support:
Stretch:
Prerequisites: 1
Everything on your computer—every photo, song, game, and message—is stored as just two symbols: 0 and 1. Why can't computers just use normal numbers and letters like us?
Present the challenge: 'I want to store the number 42 using only light switches. Each switch can only be ON or OFF. How few switches do I need?' Let students experiment and struggle. Then reveal: this is exactly what computers face—and binary is their solution.
Resources:
Row of toggle switches image or physical demonstration
Teacher Notes:
Don't reveal the answer immediately. The struggle to solve this creates genuine curiosity about why binary works.
Explain: electricity is either flowing or not flowing. Transistors are either on or off. A magnet is pointing north or south. EVERYTHING in computers works in two states. Show: it would be incredibly hard to reliably distinguish between 10 different voltage levels—but two states (high/low) is easy and reliable.
Resources:
Simple visual showing transistor as a switch
Teacher Notes:
Key insight: binary isn't arbitrary—it's the natural language of electronics. Computers speak binary because that's what electronic circuits naturally 'understand'.
Build vocabulary: 1 bit = one binary digit (0 or 1). How many values can 1 bit represent? (2). 2 bits? (4 patterns: 00, 01, 10, 11). 3 bits? (8). Pattern emerges: n bits = 2^n values. Introduce nibble (4 bits, 16 values) and byte (8 bits, 256 values). Students calculate and verify.
Resources:
Table showing all combinations for 1, 2, 3, 4 bits
Teacher Notes:
Stress the pattern: each additional bit DOUBLES the number of possible values. This is crucial for understanding storage capacity and representation limits.
Physical activity: 8 students stand in a row, each representing 1 bit. Hands up = 1, hands down = 0. Class calls out numbers 0-255, and the 'human byte' tries to represent them. Then reverse: 'byte' makes a pattern, class calculates the value.
Resources:
Cards with numbers for the class to try
Teacher Notes:
Kinesthetic activity helps cement the concept. If class is large, multiple 'bytes' can compete. This previews binary conversion in the next lesson.
Brief discussion: Why did we settle on 8 bits as a 'byte'? Historical context: 8 bits was enough for all English letters, numbers, punctuation (128 combinations), with room to spare. It's also a power of 2, which makes computing more efficient.
Teacher Notes:
This is nice context but not exam-essential. Keep it brief and interesting.
Rapid-fire practice: 'How many bits in 3 bytes?', 'How many values can 4 bits represent?', 'If I have 5 bits, what's the maximum number of different things I can identify?' Students write answers on mini-whiteboards.
Resources:
One per student for showing answers
15-20 quick calculation questions
Teacher Notes:
Focus on the 2^n pattern. This foundational understanding is crucial for later lessons on character sets and image representation.
Regular computers use bits (0 or 1). Quantum computers use qubits that can be 0, 1, or both at once. Brief, accessible introduction to why this matters and why we might still need classical binary computers.
Connection: Shows students that the binary system, while foundational, isn't the only possible computing paradigm—inspiring curiosity about the future.
Further Reading:
Brief exploration of how transistors work as tiny switches—on/off = 1/0. Modern chips have billions of these switches. Shows the physical reality behind the abstract concept.
Connection: Connects abstract binary to physical electronics, helping students understand why binary isn't arbitrary—it matches what electronics naturally do.
Further Reading:
Support:
Stretch:
Prerequisites: 1
In binary, 1 + 1 = 10. That's not a mistake—it's how computers count. Can you crack the code?
Challenge: Who can count the highest using only one hand? In denary, max is 5. But using binary finger-counting (each finger is a bit), you can reach 31 with one hand! Demonstrate, let students try. 'How high could you count with two hands?' (1,023)
Resources:
Visual showing which fingers represent which values
Teacher Notes:
This is an excellent physical mnemonic. Students often remember binary through finger counting long after the lesson.
Connect to denary place values: 1s, 10s, 100s, 1000s (each column is x10). Binary works the same but each column is x2: 1, 2, 4, 8, 16, 32, 64, 128. Build a table together. Convert simple numbers by finding which column combinations sum to the target.
Resources:
8-column grid showing 128, 64, 32, 16, 8, 4, 2, 1
Step-by-step conversions for 13, 45, 100
Teacher Notes:
Key method: work left to right, asking 'Can I include this column without going over?' Always emphasise showing working—this is what examiners want to see.
Introduce terminology: Most Significant Bit (leftmost, biggest value) and Least Significant Bit (rightmost, just 1). In 10110100: MSB is the leftmost 1 (worth 128), LSB is the rightmost 0 (worth 0). Why does this matter? Changing the MSB has the biggest impact on the value.
Resources:
Visual highlighting MSB and LSB in example numbers
Teacher Notes:
These terms come up in exam questions. Ensure students can identify both in any binary number.
Structured practice in pairs. Round 1: Denary to binary (start easy: 5, 12, 20, build to 200, 255). Round 2: Binary to denary (00001010, 01010101, 11111111). Round 3: Mixed, against the clock. Pairs mark each other's work.
Resources:
Graded practice problems with answer key
Cards for timed practice round
Teacher Notes:
Repetition is key here—this is a skill that needs practice to become automatic. Circulate and identify struggling students for support.
Clarify: 11010 = 011010 = 00011010 (all equal 26 in denary). Leading zeros don't change the value. Why include them? Sometimes we work with fixed-width numbers (e.g., always 8 bits). Like writing '007' instead of '7'.
Resources:
Visual showing equivalent representations
Teacher Notes:
Important for exam questions which often give numbers with different numbers of bits.
Individual challenge: Convert 173 to binary (showing working). Convert 10111001 to denary (showing working). Identify the MSB and LSB in your binary answer. Self-mark using provided answers.
Resources:
Challenge questions with marking scheme
Teacher Notes:
Review common errors: forgetting to show place values, miscounting bits, confusing MSB/LSB.
Some video game glitches exploit overflow errors or binary representation quirks. The famous 'kill screen' in Pac-Man happens at level 256 because the level counter is stored in 8 bits.
Connection: Shows how understanding binary representation has real-world applications, even in entertainment.
Further Reading:
Support:
Stretch:
Prerequisites: 4
What happens when a computer tries to calculate 200 + 100, but it can only store numbers up to 255? Welcome to overflow errors—where maths breaks in spectacular ways.
Present a scenario: An 8-bit calculator stores values 0-255. Calculate 200 + 100. What should the answer be? (300). But 300 doesn't fit in 8 bits! Demonstrate on a binary table what happens—the '1' that should represent 256 gets lost. Answer becomes 44. This is overflow.
Resources:
Step-by-step binary addition showing the lost carry bit
Teacher Notes:
This dramatic failure captures attention. Let students feel the frustration of the 'wrong' answer before explaining why.
Teach binary addition rules: 0+0=0, 0+1=1, 1+0=1, 1+1=10 (write 0, carry 1), 1+1+1=11 (write 1, carry 1). Work through examples together: start with simple (0101 + 0011), build to complex (10110101 + 01101010). Emphasise showing working—columns, carries.
Resources:
Visual reminder of the four rules
Step-by-step addition with column layout and carries shown
Teacher Notes:
This is like denary addition but simpler—fewer rules. Most errors come from forgetting to write carries. Stress the importance of neat column alignment.
Structured practice: start with 4-bit additions (no overflow), progress to 8-bit additions (some causing overflow). For each overflow, identify: What should the answer be? What does the computer get? What's lost?
Resources:
Graded problems from simple to overflow-inducing
Teacher Notes:
Make students predict whether overflow will occur before calculating. This builds understanding of when overflow happens.
Introduce binary shifts. LEFT shift: move all bits left, add 0 on right. What happens to 00001100 (12) shifted left once? 00011000 (24)—it DOUBLED. Right shift: opposite—halves the number (losing remainder). Demonstrate multiple shifts: shifting left twice = x4, shifting right twice = ÷4.
Resources:
Visual showing bits moving left and right
Table showing numbers before and after various shifts
Teacher Notes:
Key insight: left shift = multiply by 2, right shift = divide by 2 (integer division). This is why computers use shifts for fast multiplication—they're simpler than actual multiplication circuits.
Given a number, predict the result of shifting before calculating. Then verify. Example: 00010110 (22) shifted right twice. Predict: 22 ÷ 4 = 5.5, so 5 (integer). Verify: 00000101 = 5. ✓
Resources:
Problems requiring prediction then verification
Teacher Notes:
The predict-then-verify approach builds deeper understanding than just following a procedure.
Mini-quiz with exam-style questions: 'Add these two binary numbers', 'Explain why overflow might occur', 'Perform a left shift by 2 places', 'What is the effect of shifting 00111100 right by 3 places?'
Resources:
5-6 questions mimicking exam format
Teacher Notes:
Exposure to exam wording is important. Note which question types cause difficulty.
In 1996, the Ariane 5 rocket exploded because a 64-bit number was converted to a 16-bit number, causing an overflow. Real consequences of the binary limitations we're studying.
Connection: Demonstrates that overflow errors aren't just exam questions—they have catastrophic real-world consequences in poorly designed systems.
Further Reading:
In the original Civilization game, Gandhi was programmed to be peaceful (aggression = 1). But if his aggression dropped below 0, it overflowed to 255, making him extremely aggressive—and nuke-happy.
Connection: Shows overflow errors in a context students find entertaining, making the concept memorable.
Further Reading:
Support:
Stretch:
Prerequisites: 5
Why do web designers write colours as #FF5733 instead of '255, 87, 51' or '11111111, 01010111, 00110011'? The answer reveals programmers' favourite number system.
Show a web colour picker. Point to red: #FF0000. Green: #00FF00. Blue: #0000FF. White: #FFFFFF. Black: #000000. Ask: What do you notice? What does 'FF' represent? Why these strange letters? Students predict before we explain.
Resources:
Online colour picker showing hex codes
Teacher Notes:
Let students puzzle over the patterns. Many will notice FF = maximum, 00 = minimum. Build on their observations.
The problem: binary is hard to read (10110101 vs 01101101—spot the difference?). Solution: group binary into nibbles (4 bits), each nibble = one hex digit. 16 possible values = 0-9 plus A-F. Show: 1111 = F = 15, so FF = 11111111 = 255. It's just a compact way to write binary!
Resources:
Table showing 0-F with binary and denary equivalents
Teacher Notes:
Key insight: hexadecimal exists because binary is hard to read. One hex digit = exactly 4 bits. This makes conversion trivial.
Method 1: Divide by 16, the remainder is the right digit, the quotient is the left digit. Example: 200 ÷ 16 = 12 remainder 8. So 200 = C8 (C=12). Method 2: Use binary as a stepping stone (easier for exam). Work through several examples.
Resources:
Both methods displayed step-by-step
Teacher Notes:
Most students find the binary stepping-stone method easier: denary → binary → split into nibbles → hex.
Students practice converting between all three systems. Given a number in any base, convert to the other two. Start with simple examples, build complexity. Use the colour code context: 'What colour is #3C (just the red channel)?'
Resources:
Practice problems requiring denary ↔ binary ↔ hex
Questions in colour context
Teacher Notes:
The colour context makes hex feel relevant. Students enjoy predicting what colour a hex code will produce.
Show examples of hex in real contexts: colour codes, error messages, MAC addresses, memory dumps, programming. Students identify which numbers are hex (hint: look for letters, 0x prefix, or # symbol).
Resources:
Screenshots of hex in various contexts
Teacher Notes:
This contextualises the skill—students see they're learning something actually used in industry.
Timed challenge: convert between bases as quickly (and accurately) as possible. Mix denary, binary, and hex. Peer marking. Discuss common errors and quick methods.
Resources:
30 quick conversions
Teacher Notes:
Speed practice builds fluency. Some students will develop personal shortcuts—encourage sharing these.
When your computer crashes and shows an error code like 0x0000007E, that's hexadecimal. When programmers debug memory at address 0x7FFF4A2C, that's hexadecimal. Why did the computing world adopt this strange system?
Connection: Shows students that hex isn't just an exam topic—it's the standard notation in professional computing.
Further Reading:
Every network device has a MAC address like 00:1A:2B:3C:4D:5E—six pairs of hexadecimal digits. Brief exploration of why hex is perfect for representing binary data compactly.
Connection: Connects to networking topics and provides another real-world hexadecimal example.
Support:
Stretch:
https://www.rapidtables.com/convert/number/hex-to-binary.html
Prerequisites: 5
How does your phone turn the letter 'A' into electricity? And how can it display 😀 when emoji didn't exist when computers were invented?
When you text 'Hi!' to someone, your phone doesn't send the letters H, i, !. It sends binary numbers. Show: 01001000 01101001 00100001. How does the receiver know these mean 'Hi!'? There must be a shared codebook...
Resources:
Graphic showing text → binary → transmission → text
Teacher Notes:
Build mystery: How do sender and receiver agree on what 01001000 means? This motivates the need for standardised character sets.
Introduce the concept: a character set is an agreed mapping between numbers and characters. Like a dictionary: 65 = 'A', 66 = 'B'. Explore: Why does order matter? (Makes sorting and comparison work). Show ASCII table—note patterns: consecutive letters have consecutive codes.
Resources:
Partial ASCII table showing letters, numbers, common symbols
Questions about ASCII patterns (B = A + 1, etc.)
Teacher Notes:
Students don't need to memorise codes—they need to understand the concept and recognise logical ordering.
ASCII uses 7 bits (extended ASCII uses 8). 7 bits = 2^7 = 128 characters. Enough for English letters (upper and lower), numbers, punctuation, control characters. BUT: What about other languages? Chinese? Arabic? 😀?
Resources:
Examples of characters ASCII cannot represent
Teacher Notes:
Key limitation: ASCII is English-centric. This creates a genuine problem that Unicode solves.
Unicode uses up to 32 bits. 2^32 = over 4 billion possible characters! Includes every writing system, ancient scripts, mathematical symbols, and emoji. But: more bits = more storage per character. Text files are bigger in Unicode. Trade-off discussion.
Resources:
Showing diverse scripts: Arabic, Chinese, Greek, Emoji
Same text stored in ASCII vs Unicode—size difference
Teacher Notes:
Unicode isn't just 'better'—there's a storage trade-off. ASCII files are smaller. This is why both still exist.
Give students encoded messages in ASCII (as binary/hex/denary). They decode using an ASCII table. Then: 'decode' an emoji—students discover it's not in ASCII! Discuss: Why does your phone need Unicode to show emoji?
Resources:
Encoded messages to decode
Reference for decoding
Teacher Notes:
Make the emoji fail a dramatic moment—students are invested in making it work, then discover ASCII's limitation firsthand.
Exam focus: Given n bits per character, how many characters can be represented? (2^n). Given a character set with 50,000 characters, how many bits minimum? (2^15 = 32,768 too small, 2^16 = 65,536 ✓, so 16 bits). Practice problems.
Resources:
Calculation problems
Teacher Notes:
This mathematical relationship appears frequently in exams. Ensure students can work both directions.
A real committee meets to decide which new emoji get added to Unicode each year. Explore the surprisingly political process of emoji creation and why representation matters.
Connection: Shows character sets as living, evolving standards—not just exam content. Connects computing to social and cultural considerations.
Further Reading:
Ever seen a website display ????????? or café showing as 'café'? That's 'mojibake'—what happens when text is decoded with the wrong character set. Real examples of encoding failures.
Connection: Shows the real-world consequences of character set choices and why standards matter.
Further Reading:
Support:
Stretch:
Prerequisites: 4, 5
Why do photos from 2005 look so blurry compared to photos today—even though the 'camera' was the same phone app?
Display a beautiful photograph. Now zoom in... further... further... until individual coloured squares appear. 'Your photo isn't smooth—it's made of tiny coloured squares called pixels. Let's understand how your phone turns a sunset into numbers.'
Resources:
Beautiful image that pixelates when zoomed
Teacher Notes:
The reveal that all images are 'just squares' surprises many students. Let them process this before moving on.
Each pixel = one colour. Each colour = a number. Simple example: black-and-white image with 1-bit colour depth: 0 = black, 1 = white. Build a simple image on a grid. Now add grey: need 2 bits (00=black, 01=dark grey, 10=light grey, 11=white). More bits = more colours.
Resources:
Blank grid for creating simple 1-bit images
Same image at 1-bit, 4-bit, 8-bit, 24-bit
Teacher Notes:
Build from simple to complex. Let students physically colour grid squares to experience image creation at the pixel level.
Colour depth = bits per pixel. 1-bit = 2 colours. 8-bit = 256 colours. 24-bit = 16.7 million colours ('true colour'). Show the same photo at different colour depths. More colours = smoother gradients but bigger file. Calculate: 100x100 image at 24-bit = how many bits?
Resources:
Gradient images at various colour depths
Worked examples of storage calculations
Teacher Notes:
The visual comparison is key—students SEE the difference colour depth makes. Link to hex colours: 24-bit = 8 bits each for R, G, B = #RRGGBB.
Resolution = width × height in pixels. More pixels = sharper image but bigger file. Show: 640×480 vs 1920×1080 vs 4K (3840×2160). Calculate the difference in total pixels. Why does your phone offer different photo quality settings?
Resources:
Same image at different resolutions
Teacher Notes:
Connect to real choices: students decide between 'High Quality' (big files) and 'Standard' (smaller files) when taking photos.
Image files contain more than pixel colours. Metadata includes: dimensions, date taken, camera settings, GPS location, software used. Demo: show metadata from a real photo. Discuss: Why might this matter for privacy?
Resources:
Tool to show metadata from sample image
Teacher Notes:
The privacy angle engages students—they may not realise photos contain location data. Good opportunity for discussion about digital footprints.
Scenario: Design image settings for three devices: a basic e-reader, a smartphone camera, and a professional cinema camera. For each, recommend resolution and colour depth. Justify choices considering quality needs and storage constraints.
Resources:
Three scenarios with space for recommendations
Teacher Notes:
This applied task prepares students for exam scenarios. Accept justified answers even if they differ from 'expected' solutions.
Rapid-fire: 'An image is 800×600 pixels with 8-bit colour depth. Calculate the file size in bytes (ignoring metadata and compression).' Self-check answers, identify any confusion.
Resources:
3-4 file size calculations
Teacher Notes:
This prepares for the calculations lesson coming up. Note any formula confusion to address.
Brief exploration of how LCD and OLED screens physically create colours using sub-pixels. Each 'pixel' is actually three tiny lights (red, green, blue) that our eyes blend together.
Connection: Connects the abstract concept of pixels to the physical technology students interact with constantly.
Further Reading:
Professional cameras offer RAW format with more colour depth than your eye can perceive. Why bother? Editing flexibility. Brief look at why more bits matters for image editing.
Connection: Shows real-world application of colour depth choices beyond exam scenarios.
Further Reading:
Support:
Stretch:
Prerequisites: 4
How does Spotify fit 100 million songs on its servers when sound is literally invisible vibrations in the air?
Play a sound. 'Sound is vibrations in the air—you can't see them, touch them, or hold them. Yet here they are stored on my computer as a file I can copy, email, and play years later. How does something invisible become something permanent?'
Resources:
Software showing live waveform of sound
Teacher Notes:
The physical mystery of sound captures attention. Show a waveform visualisation if possible.
Sound is a continuous wave (analogue). Computers need discrete numbers (digital). Solution: SAMPLING—take measurements of the wave at regular intervals. Show: same wave sampled at low frequency (jagged, sounds bad) vs high frequency (smooth, sounds good).
Resources:
Visual showing waveform being sampled at different rates
Same sound at 8kHz vs 44.1kHz
Teacher Notes:
The visual of dots along a wave makes sampling intuitive. Play audio samples—students HEAR the quality difference.
Sample rate = measurements per second (Hz). CD quality = 44,100 Hz (44.1kHz). Phone calls = 8,000 Hz (sounds tinny—why?). Higher rate = more accurate wave capture but bigger file. Demo: downsample a song—students hear quality degrade.
Resources:
Same clip at various sample rates
Teacher Notes:
The phone call example is relatable—students recognise that 'phone voice' quality and now understand why.
Bit depth = bits per sample = precision of each measurement. 8-bit = 256 levels. 16-bit = 65,536 levels. Higher bit depth = smoother dynamics, quieter noise, but bigger file. Demo: same sound at 8-bit (crunchy, noisy) vs 16-bit (clean).
Resources:
Same clip at 8-bit vs 16-bit
Teacher Notes:
Low bit depth sounds 'crunchy' or 'lo-fi'—some artists use this deliberately for effect.
Scenario-based design: Choose sample rate and bit depth for: podcast speech, CD-quality music, voice note app, movie soundtrack. Justify each choice. Then calculate file sizes: if sample rate = 44100 Hz, bit depth = 16 bits, duration = 3 minutes, what's the file size?
Resources:
Scenarios with space for choices and calculations
Teacher Notes:
The formula: file size = sample rate × duration × bit depth. Ensure students can apply it before moving on.
Compare: Images have pixels, sound has samples. Images have colour depth, sound has bit depth. Images have resolution, sound has sample rate. Create a comparison table. Quiz: which audio concept matches which image concept?
Resources:
Table comparing image and sound representation
Teacher Notes:
Drawing these parallels helps students see the underlying pattern: digital representation always involves sampling and quantising.
Some music fans claim vinyl records sound better than digital. Is this true? Explore the 'analogue vs digital' debate, including what CD quality (44.1kHz, 16-bit) actually captures and why some argue higher sample rates are pointless.
Connection: Shows real-world debates about sample rate and bit depth—these aren't just exam topics but active discussions in the music industry.
Further Reading:
Humans can hear up to about 20kHz. The Nyquist theorem says you need to sample at TWICE the maximum frequency. 2 × 20kHz = 40kHz, rounded up to 44.1kHz. Brief look at how biology determined our digital audio standards.
Connection: Explains why we use specific sample rates, connecting maths to physics and biology.
Further Reading:
Support:
Stretch:
Prerequisites: 4, 9
Netflix streams 15% of all internet traffic worldwide. YouTube uploads 500 hours of video every minute. How do we even measure that much data?
Show scale: a text message = ~1 KB, a photo = ~3 MB, a song = ~4 MB, a movie = ~4 GB, your phone storage = 128 GB, a data centre = many PB. Challenge: put these in order and estimate how many text messages equal one photo.
Resources:
Infographic showing relative sizes
Teacher Notes:
The scale from text message to data centre makes the progression concrete.
Build the ladder: Bit → Nibble (4 bits) → Byte (8 bits) → KB (1000 bytes) → MB (1000 KB) → GB (1000 MB) → TB (1000 GB) → PB (1000 TB). Practice conversions: How many bits in 2 KB? How many MB in 0.5 GB?
Resources:
Visual showing all units and conversion factors
Teacher Notes:
Note: OCR accepts both 1000 and 1024 for conversions. Teach 1000 as primary (SI standard) but mention 1024 exists.
Three key formulas: Text = bits per character × number of characters. Image = colour depth × width × height. Sound = sample rate × duration × bit depth. Work through examples of each, including unit conversions to KB/MB.
Resources:
One card per formula with worked example
Step-by-step calculations for each type
Teacher Notes:
Common error: forgetting to convert to appropriate units at the end. Always ask 'Is this answer sensible?'—a 3-minute song shouldn't be 500 TB!
Mixed practice problems: Calculate file sizes for various scenarios. Include multi-step problems: 'A website has 20 images (800×600, 24-bit) and 50 pages of text (2000 characters each, ASCII). What's the total size?' Estimate first, then calculate.
Resources:
Graded calculation problems
Teacher Notes:
Encourage estimation before calculation—this catches major errors and builds intuition.
Real-world application: 'A school wants to store: 500 student photos, 10,000 text documents, and 100 audio recordings of 5 minutes each. Recommend an appropriate storage device and calculate whether it will fit.' Students present solutions.
Resources:
3-4 storage planning scenarios
Teacher Notes:
This combines calculation skills with the storage comparison knowledge from lesson 3. Great exam preparation.
Speed round: rapid unit conversions. Students show answers on whiteboards. Focus on common exam conversion types. Self-assess and identify weak areas.
Resources:
20 quick-fire conversion questions
Teacher Notes:
Speed builds fluency. Note common errors for future revision.
Humanity creates 2.5 quintillion bytes of data daily. What does that even mean? Explore the scale of global data creation and storage, from individual phones to Google's data centres.
Connection: Makes the large units (TB, PB) feel real—students see they're not just exam content but measures of actual things.
Further Reading:
Why does your '1 TB' hard drive only show 931 GB in Windows? The difference between SI units (1000) and binary units (1024). This isn't a scam—it's two valid measurement systems.
Connection: Addresses a common real-world confusion students may have encountered, and explains why both systems are acceptable in exams.
Further Reading:
Support:
Stretch:
Prerequisites: 4, 8, 9, 10
How does a 4GB movie become a 700MB download? And why do some compressed files look perfect while others look like they're made of blocks?
Scenario: You want to email a 50MB photo, but the limit is 25MB. You want to watch a 4K movie, but your internet is too slow for the full size. You want to store 1000 songs on your phone, but you only have 5GB free. What's the solution?
Resources:
Visual slides showing each impossible situation
Teacher Notes:
Students will likely suggest 'compress it' or 'make it smaller'. Build from their intuition.
Three main reasons for compression: 1) Storage (fit more on a drive), 2) Transmission (faster upload/download), 3) Cost (smaller files = cheaper to store and transfer). Real examples of each: phone storage, Netflix streaming, cloud storage costs.
Resources:
Visual showing storage, speed, and cost benefits
Teacher Notes:
These are the 'scenarios' mentioned in the spec. Ensure students can identify when compression is appropriate.
Lossless compression: file gets smaller, but NOTHING is lost. When you decompress, you get the exact original back. Examples: ZIP files, PNG images, FLAC audio. How? By finding patterns and redundancy (briefly explain). Used when perfect accuracy matters: documents, code, medical images.
Resources:
Demonstrate ZIP compression and decompression
Teacher Notes:
Key point: lossless is reversible—you can always get the exact original back.
Lossy compression: file gets MUCH smaller, but some data is permanently removed. You can't get the exact original back. Examples: JPEG images, MP3 audio, streaming video. How? By removing data humans don't notice much (barely audible sounds, subtle colour differences).
Resources:
Same image/audio at different quality levels
Zoomed image showing compression blocks
Teacher Notes:
The visible/audible quality loss makes this concrete. Show artifacts in heavily compressed images.
Students create a decision flowchart: Given a scenario, which compression type? Scenarios include: legal document, holiday photo for Instagram, music master recording, email attachment, game save file, streaming video. Justify each choice.
Resources:
Blank flowchart structure
12 scenarios to categorise
Teacher Notes:
Key insight: the choice depends on what matters—file size, perfect quality, or a balance.
Build a comparison table together: Lossless (✓ perfect quality, ✗ larger files, ✓ reversible) vs Lossy (✓ very small files, ✗ quality loss, ✗ irreversible). Discuss trade-offs: When is quality loss acceptable? When is it not?
Resources:
Two-column table for class completion
Teacher Notes:
This summary is core exam content. Ensure all students have a complete comparison table.
Exam question practice: 'A photographer needs to send images to a client for approval. They also need to send final high-resolution versions for printing. Recommend compression approaches for each purpose and explain your choices.' Individual written response, then peer discussion.
Resources:
4-6 mark scenario question with mark scheme
Teacher Notes:
This tests ability to apply knowledge in context—a common exam format. Review strong answers with the class.
The TV show 'Silicon Valley' centers on a revolutionary compression algorithm. How realistic is this? Brief exploration of why compression research is still a competitive field, and how new codecs like AV1 are fighting for dominance.
Connection: Shows that compression isn't a 'solved' problem—it's an active area of development affecting streaming, gaming, and more.
Further Reading:
Brief look at common compression formats students encounter. Why do different formats exist? Why can't you compress a compressed file much further?
Connection: Connects the abstract concept to tools students actually use when downloading and sharing files.
Further Reading:
Support:
Stretch:
Prerequisites: 9, 10, 11