🎯 Learning Goal

Children understand that tasks have a correct order — and the wrong order gives the wrong result.

📦 Materials

• 6–8 laminated picture cards showing roti-making steps
• A small board or floor mat for sequencing

🔢 Steps

1. Show all cards face-up in random order
2. Ask: "What does Dadi do first when making roti?"
3. Children place cards in sequence, explaining their choices
4. Deliberately "make a mistake" (e.g., rolling before kneading) and ask "Does this work?"
5. Introduce the word "sequence" naturally: "This is the right sequence!"

🎯 Learning Goal

Children understand that the same objects can be grouped differently depending on which attribute you choose — introducing abstraction (choosing what matters).

🔢 Steps

1. Sort by colour (easy, visual)
2. Sort again by size (different groups emerge)
3. Ask: "Can they belong to two groups at once?"
4. Introduce rule-based sorting: "Everything green AND round goes here"

🎯 Learning Goal

Computers (and robots) need precise, unambiguous instructions. Vague language causes bugs.

🔢 Steps

1. Teacher plays "robot" — deliberately misinterprets vague instructions (hilarity ensues)
2. Children figure out they need to say exactly "walk 2 steps forward, turn right, stop"
3. Switch: one child is the robot, another gives instructions to navigate a simple path
4. Introduce obstacles on the path for more challenge

🎯 Learning Goal

Students understand that even one wrong step in an algorithm breaks the whole thing — and that finding the bug requires systematic testing.

📦 Materials

• Chalk or masking tape to draw a simple maze on the floor
• A card with 8 movement instructions, 2 of which are wrong
• A "sugar cube" (block) at the destination

🔢 Steps

1. One student is the "ant" and follows instructions exactly (no improvising!)
2. When they get stuck, class identifies the "bug" (wrong instruction)
3. Students rewrite the correct instruction ("fix the bug")
4. New ant tests the fixed algorithm

🎯 Learning Goal

Not all algorithms are equally efficient. Students discover that the way you organise sorting matters enormously.

📦 Materials

• 50 "letter" cards with 6-digit fake PIN codes
• 5 labelled district boxes (first digit of PIN determines district)

🔢 Steps

1. Round 1: Sort all letters one by one — time it
2. Students propose a better approach (group by first digit first)
3. Round 2: Use the student-designed algorithm — time it again
4. Compare times: which is faster? Why?

🎯 Learning Goal

Data is more useful when organised. The choice of how to organise it depends on what questions you want to answer — a core idea in both computer science and statistics.

🔢 Steps

1. Questions given before students see the data — they must plan organisation in advance
2. Round 1: Unorganised pile — answer 5 questions, time it
3. Students design their own organisation system
4. Round 2: Answer same questions — time it, compare
5. Discussion: What did you sacrifice by organising? (Some questions became harder)

🎯 Learning Goal

Real decisions are governed by combinations of conditions. AND, OR, NOT are not just programming constructs — they describe how rules work in law, government, and daily life.

🔢 Steps

1. Rule 1: "Applicant must be from an OBC/SC/ST household AND household income < ₹2 lakh"
2. Rule 2: "Applicant must have marks > 80% OR have won a district competition"
3. Rule 3: "NOT already received a state scholarship"
4. Apply all three rules to 15 profiles — who qualifies?
5. Debate: Is this a fair algorithm? What would you change?

🎯 Learning Goal

The shortest path problem is one of the most common in computing. Students discover that "always go to the nearest unvisited node" produces the optimal solution — they rediscover Dijkstra without knowing it.

🔢 Steps

1. Draw a simple 7-node city map with 10 roads, each labelled with travel time (minutes)
2. Challenge: Find the fastest route from restaurant to Customer 6
3. Most students try trial-and-error — compare all their times
4. Teacher introduces the greedy rule: "At each step, go to the closest unvisited place"
5. Apply the rule systematically — does it always give the best answer?

🔢 Steps

1. Students receive a short encrypted message (5–6 sentences)
2. Count frequency of each symbol
3. Most common symbol is likely the most common letter in Hindi/Urdu ("ka/की")
4. Use this to crack the shift key and decode the message
5. Discussion: Why is frequency analysis a pattern recognition technique?

🎯 Learning Goal

Real-world optimisation requires abstracting a messy problem into a mathematical model, then using an algorithm to find the best solution within constraints.

🔢 Steps

1. Problem framing: 20 beds, 3 categories, each with different occupancy duration
2. Define objective: maximise total patients served per week
3. Write constraints as inequalities
4. Graph feasible region (or use spreadsheet solver)
5. Find optimal point — discuss what the model ignores (ethical dimensions)

🎯 Learning Goal

Algorithms reflect the data they are trained on. Biased data creates biased algorithms. Understanding this is essential citizenship in the age of AI.

🔢 Steps

1. Read: short case study of a wrongful arrest based on facial recognition
2. Identify: what data was the system trained on? What was underrepresented?
3. Model: How would you test a facial recognition system for fairness?
4. Debate: Should public facial recognition be banned, regulated, or allowed?
5. Write: a 1-page "algorithmic impact assessment" for one proposed use case

🔢 Steps

1. Lay out 3 colours of beads in separate bowls (red = A, yellow = B, blue = C)
2. Teacher threads 6 beads: ABABAB and asks "what comes next?"
3. Children predict — then check by threading
4. Introduce AABB: "This is a different pattern rule"
5. Challenge: "Can you invent your own pattern rule?"
6. Extension: Connect to saree borders — show photos of border patterns and identify the loop unit

🎯 Learning Goal

Students discover that everyday arithmetic tasks follow an algorithm — and that the algorithm must handle different cases (exact change, more change than expected, no small coins available).

🔢 Steps

1. Set up a "kirana shop" with toy currency (₹1, ₹2, ₹5, ₹10, ₹20 coins/notes)
2. Class writes the change-giving algorithm together (Step 1: Subtract cost from payment...)
3. Test the algorithm with 5 different purchase scenarios
4. Introduce a bug: "What if we don't have ₹2 coins?" — redesign the algorithm

🔢 Steps

1. Brainstorm everything needed for a mela (unstructured)
2. Group related tasks into 5 "modules" (stalls, food, decoration, entry, programme)
3. For each module, identify: What must happen first? What can happen in parallel?
4. Draw a simple timeline showing dependencies (on a large paper/chalkboard)
5. Debrief: "Which task, if delayed, would delay everything else?" (Critical path)

🔢 Steps

1. Groups receive the same raw data but different user personas (business traveller, family with kids, senior citizen)
2. Each group creates a timetable optimised for their user
3. Compare: Different users need different abstractions of the same data
4. Discuss: "What happens when the abstraction is wrong?" (train is cancelled but shows as on time)

🔢 Steps

1. Students sort 20 parent-offspring cards into groups by trait outcome
2. They count ratios and notice "something keeps appearing"
3. The 3:1 ratio emerges from the data — students formulate a rule themselves
4. Test the rule with 10 new cards — does it hold?
5. Bridge to Mendel: "This is exactly how Mendel discovered heredity laws — pattern recognition from data, not from a textbook."

🔢 Steps

1. Each student reads the same article independently
2. Write a 7-word headline — compare with class: are they different? Why?
3. Write a 30-word summary — what must be included? what can be dropped?
4. Compare two student headlines: which is more accurate? more clickable? is there a trade-off?
5. Bridge: "This is exactly what a database does when it stores a summary vs. full text."

🎯 Learning Goal

Real open government data is messy, incomplete, and requires CT to make sense of. Students experience authentic data analysis — not made-up classroom numbers.

🔢 Steps

1. Download a simple CSV of MGNREGA person-days data from data.gov.in
2. Identify obvious patterns: which years show spikes? (COVID years will show anomalies)
3. Create 3 different visualisations of the same data — which tells the "true" story?
4. Write a 100-word evidence-based claim about the data
5. Peer-review: Is the claim supported by the data, or is the student overgeneralising?

🔢 Steps

1. Model the Mumbai local network as a graph (5–6 key nodes, 8–10 edges)
2. Remove one edge (station closure) and find all passengers who can no longer reach their destination
3. Design a "contingency algorithm" — what alternate routes does the system offer?
4. Identify "critical nodes" — stations whose removal disconnects the network completely
5. Bridge: This is how engineers design resilient internet infrastructure, power grids, and supply chains.

🎯 Learning Goal

Large-scale sociotechnical systems can be analysed using CT. Understanding the algorithm behind a policy enables better civic participation and more informed critique.

🔢 Steps

1. Map the Aadhaar authentication algorithm as a flowchart from publicly available sources
2. Identify failure points: What happens if biometric fails for elderly users? No network connectivity? Server down?
3. Analyse: Who benefits from the abstraction choices made in the algorithm design?
4. Conduct a structured ethical impact assessment: Accuracy, Privacy, Accessibility, Exclusion risks
5. Write a 200-word policy recommendation: How would you improve the algorithm?

🔢 Steps

1. Set up 3 columns: Susceptible (S), Infected (I), Recovered (R) with day numbers
2. Enter the SIR equations as spreadsheet formulas (provided as template)
3. Run the model for 100 days — observe the bell curve of infections
4. Change β (transmission rate): simulate wearing masks → lower β
5. Change vaccination coverage: start 20% of population as already "Recovered"
6. Find: what vaccination percentage causes the epidemic to never take off? (Herd immunity)

🔢 Steps

1. Show 10 animal cards one by one — children name them in their home language
2. Sort by "where they live" (jungle / water / both)
3. Re-sort by "what they eat"
4. Ask: "Can the same animal be in different groups?" (A crocodile is in both water and land groups)
5. Final: sort by Panchtantra story role — "who was wise?" "who was tricked?"

🔢 Steps

1. Without warning, ask students to draw their route from home to school — 5 minutes, from memory
2. Share in pairs: What's different? What's the same?
3. Discuss: whose map is "more correct"? (Both are correct — they abstract differently)
4. Compare with Google Maps view of the same area
5. Ask: "What does Google Maps hide that your map shows? What does your map hide that Google shows?"