Activity 01
Maze Challenge: Obstacle Avoidance
Students program robots with ultrasonic sensors to detect walls and turn right using if-distance-less-than-10cm-then-turn. Test on a printed maze, record successful runs, and swap programs with peers for evaluation. Adjust thresholds based on trial data.
Explain how a robot uses 'if-then' logic to react to its environment.
Facilitation TipDuring Maze Challenge, place obstacles at different angles so students see how sensor alignment affects detection reliability.
What to look forPresent students with a flowchart snippet showing an 'if-then' condition for a robot. Ask: 'If the distance sensor reads less than 10cm, what action should the robot take? Write down the next step in the robot's algorithm.'
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Activity 02
Line Follower: Decision Points
Use line-following sensors with conditionals: if-left-black-then-turn-left, else-straight. Add junctions where if-both-white-then-stop-and-reverse. Groups build custom tracks and compete for fastest completion.
Design a program for a robot to avoid obstacles using a sensor.
Facilitation TipIn Line Follower, mark decision points with tape and ask students to predict the robot’s next move before running code.
What to look forGive students a scenario: 'A robot is programmed to turn left if it detects a red object, otherwise it moves forward.' Ask them to write one sentence explaining what type of sensor is needed for this task and one sentence describing the conditional logic used.
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Activity 03
Light Maze: Adaptive Speed
Program robots to check light sensors: if-bright-then-fast, if-dark-then-slow. Create a classroom maze with shaded areas. Students predict paths, run trials, and graph speed data from multiple tests.
Evaluate the effectiveness of a robot's decision-making process in a given scenario.
Facilitation TipFor Light Maze, vary brightness levels across the room so students experience how thresholds change behavior.
What to look forShow a short video of a robot navigating a simple maze. Ask students: 'What sensor input might the robot be using? How does the robot decide when to turn? What might happen if the sensor reading is inaccurate?'
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Activity 04
Whole Class Demo: Sound Response
Demonstrate with sound sensors: if-loud-then-dance-moves-else-patrol. Class votes on scenarios, programs variations, and discusses why conditionals prevent constant actions.
Explain how a robot uses 'if-then' logic to react to its environment.
Facilitation TipIn Whole Class Demo, use a sound sensor where responses are immediate but inconsistent, highlighting the need for multiple trials.
What to look forPresent students with a flowchart snippet showing an 'if-then' condition for a robot. Ask: 'If the distance sensor reads less than 10cm, what action should the robot take? Write down the next step in the robot's algorithm.'
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Generate Complete Lesson→A few notes on teaching this unit
Start with concrete examples: show a robot that turns only when it senses an object closer than 10 centimeters. Have students draw flowcharts before coding to make the conditional structure visible. Avoid rushing to abstraction; let students iterate with real hardware to refine their understanding of thresholds and sensor limits. Research shows this hands-on cycle builds stronger mental models than abstract exercises alone.
Successful students will write conditionals that correctly map sensor readings to robot actions, test programs repeatedly across varied environments, and explain why certain logic choices improve reliability. They will also distinguish between mechanical responses and human-like decision making.
Watch Out for These Misconceptions
During Maze Challenge, watch for students who say the robot 'decides' to avoid the wall like a person would.
Pause the activity and ask students to trace the robot’s flowchart step-by-step, labeling each sensor reading and corresponding action to reinforce that decisions are mechanical and data-driven.
During Line Follower, watch for students who assume the line sensor detects perfectly from any angle or distance.
Have students test the sensor at the edge of its detection range and adjust the angle of the robot to show how readings change; then prompt them to add nested conditionals for reliability.
During Light Maze, watch for students who believe one test run proves their program works.
Ask groups to run trials under different lighting conditions and share videos of failures; use these to evaluate logic gaps and revise conditionals iteratively.
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