Computing · Year 6

Active learning ideas

Algorithms for Navigation

Active learning works for algorithms in navigation because students need to experience how real-world variability—like sensor errors or changing surfaces—affects a robot’s path. Hands-on activities turn abstract logic into tangible outcomes, making the need for precise instructions clear to learners.

• National Curriculum Attainment Targets
• KS2: Computing - Programming and Algorithms
• KS2: Computing - Computational Thinking

• 25–50 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Collaborative Problem-Solving
• 30 min · Pairs

Pairs Challenge: Line Following Path

Pairs program a robot to follow a taped line using colour sensors and forward, slight turn instructions. They test on a 2m course, log deviations, and add loop conditions for corrections. Pairs then swap robots to evaluate each other's code.

Analyze how a robot uses sensors and algorithms to navigate a maze.

Facilitation Tip

During the Line Following Path challenge, circulate to listen for pairs debating whether to use a loop for straightaways or a conditional for turns, then ask guiding questions about efficiency.

What to look for

Provide students with a simple grid map and a starting point. Ask them to write down the sequence of commands (e.g., FORWARD, TURN LEFT, IF OBSTACLE THEN TURN RIGHT) a robot would need to reach a target destination, explaining one conditional step.

• Apply
• Analyze
• Evaluate
• Create
• Relationship Skills
• Decision-Making
• Self-Management

Activity 02

• Collaborative Problem-Solving
• 45 min · Small Groups

Small Groups: Obstacle Dodge Circuit

Set up a circuit with foam blocks as obstacles. Groups write algorithms with if-sensor-then-turn logic, run trials, and measure completion time. They iterate twice, sharing videos of improvements with the class.

Differentiate between different strategies a robot could use to avoid an obstacle.

Facilitation Tip

For the Obstacle Dodge Circuit, place obstacles in unexpected positions during trials to push students to adjust their sensor logic dynamically.

What to look for

Observe students as they program a physical robot. Ask: 'What sensor is the robot using here?' and 'What will happen if the robot encounters a different colored line than you programmed it to follow?'

• Apply
• Analyze
• Evaluate
• Create
• Relationship Skills
• Decision-Making
• Self-Management

Activity 03

• Collaborative Problem-Solving
• 50 min · Whole Class

Whole Class: Maze Algorithm Build

Project a simple maze on the floor. Class brainstorms a shared algorithm step-by-step, coding sections in turns. Test as a group, vote on fixes, and celebrate a full run.

Construct a sequence of instructions for a robot to follow a simple path.

Facilitation Tip

In the Maze Algorithm Build, pause the whole class when two groups solve the same section differently, then facilitate a vote on which approach is more robust for blind corners.

What to look for

Present two different algorithms for obstacle avoidance (e.g., one that stops, one that turns). Ask students: 'Which strategy is better if the robot needs to keep moving? Why?' and 'When might the other strategy be more useful?'

• Apply
• Analyze
• Evaluate
• Create
• Relationship Skills
• Decision-Making
• Self-Management

Activity 04

• Collaborative Problem-Solving
• 25 min · Individual

Individual: Unplugged Navigation Map

Each student draws a path and writes an algorithm on paper for a partner robot 'human'. Partners follow instructions blindfolded, feedback errors, and students revise for clarity before coding on devices.

Analyze how a robot uses sensors and algorithms to navigate a maze.

Facilitation Tip

During the Unplugged Navigation Map activity, insist students label each step with a clear sensor trigger before they test their route on paper, modeling precision early.

What to look for

Provide students with a simple grid map and a starting point. Ask them to write down the sequence of commands (e.g., FORWARD, TURN LEFT, IF OBSTACLE THEN TURN RIGHT) a robot would need to reach a target destination, explaining one conditional step.

• Apply
• Analyze
• Evaluate
• Create
• Relationship Skills
• Decision-Making
• Self-Management

A few notes on teaching this unit

Teachers should model debugging as a normal part of the process, narrating their own mistakes aloud when sensors fail or loops run too long. Avoid rushing to correct errors immediately—instead, let the evidence from trials guide student adjustments. Research shows that students grasp conditionals better when they physically experience the consequences of vague instructions, so start unplugged to expose ambiguities before moving to robots.

Successful learning looks like students refining their algorithms through repeated testing, adjusting sensor thresholds or condition steps when obstacles are missed. By the end, they explain why their final sequence works and how they improved it after noticing flaws.

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Watch Out for These Misconceptions

• During the Line Following Path challenge, watch for students assuming their robot will follow the line perfectly on the first try without checking sensor data or adjusting thresholds.

  Have them run the robot three times, record where it deviates, and circle the sensor readings at the failure points. Then ask, ‘What changed between runs?’ to link variation to algorithm tweaks.

• During the Obstacle Dodge Circuit, watch for students using vague commands like ‘go around the obstacle’ without specifying direction or sensor triggers.

  Pause their group and ask them to redraw their planned path on paper with exact steps. Require them to label each action with a sensor condition or simple instruction before testing again.

• During the Maze Algorithm Build, watch for students assuming their robot’s sensors will detect all obstacles equally in dim or bright lighting.

  Shift the maze lights or add a colored obstacle. Ask the class to observe the robot’s behavior, then discuss how lighting changes sensor reliability and how to adjust the algorithm to compensate.

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Methods used in this brief

• Collaborative Problem-Solving