Computing · Year 1

Active learning ideas

Using Sensors (Simple Inputs)

Active learning works well here because Year 1 students grasp abstract concepts like inputs and reactions best through hands-on, observable experiences. When children manipulate real robots and see immediate consequences of sensor activation, the abstract becomes concrete and memorable.

• National Curriculum Attainment Targets
• KS1: Computing - Programming
• KS1: Computing - Controlling Devices

• 20–40 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Experiential Learning
• 30 min · Pairs

Pair Testing: Bump and Reverse Challenge

Pairs place obstacles on a grid mat and program the robot to move forward until its bumper sensor triggers a reverse. They test three paths, noting what happens on each bump. Pairs swap programs to predict outcomes for the other duo.

What does the robot do when it bumps into something?

Facilitation Tip

During Pair Testing, position yourself nearby to listen for pairs naming specific actions like ‘bump means reverse’ as they adjust programs together.

What to look for

Place a simple obstacle in the robot's path. Ask students: 'What do you think will happen when the robot reaches the obstacle?' Observe if they can predict the robot's reaction based on its touch sensor.

• Apply
• Analyze
• Evaluate
• Self-Awareness
• Self-Management
• Social Awareness

Activity 02

• Experiential Learning
• 40 min · Small Groups

Small Group: Light Sensor Maze

Groups build a shaded maze with cardboard tunnels and program the robot to pause in dark areas via light sensor. They time runs and adjust for faster paths. Share tweaks with the class.

Can you make the robot stop when it senses something in front of it?

Facilitation Tip

For the Light Sensor Maze, ensure students take turns both as program coders and maze designers to reinforce role clarity and shared understanding.

What to look for

Cover the robot's light sensor with your hand. Ask: 'What is happening to the sensor? How might this change what the robot does?' Guide students to connect the sensor being blocked to a change in the robot's behavior.

• Apply
• Analyze
• Evaluate
• Self-Awareness
• Self-Management
• Social Awareness

Activity 03

• Experiential Learning
• 25 min · Whole Class

Whole Class: Sensor Prediction Demo

Teacher demonstrates sensor blocks with covering and uncovering. Class predicts and votes on robot actions before each run. Record predictions on a shared chart for review.

What do you think would happen if we covered the robot's sensor?

Facilitation Tip

In the Sensor Prediction Demo, pause between steps to ask students to whisper their predictions to a partner before revealing the outcome.

What to look for

Give each student a picture of a robot with a sensor. Ask them to draw or write one thing the sensor could detect and one way the robot might react to it.

• Apply
• Analyze
• Evaluate
• Self-Awareness
• Self-Management
• Social Awareness

Activity 04

• Experiential Learning
• 20 min · Individual

Individual: Sensor Journal Logs

Each child draws a robot path, labels sensor inputs, and notes expected behaviours. Test solo on mats, then add real observations to journals for teacher check.

What does the robot do when it bumps into something?

Facilitation Tip

While students complete Sensor Journal Logs, circulate with guiding questions like ‘What did the robot do when the sensor was covered?’ to prompt detailed observations.

What to look for

Place a simple obstacle in the robot's path. Ask students: 'What do you think will happen when the robot reaches the obstacle?' Observe if they can predict the robot's reaction based on its touch sensor.

• Apply
• Analyze
• Evaluate
• Self-Awareness
• Self-Management
• Social Awareness

A few notes on teaching this unit

Teachers should model curiosity and iterative testing, showing students how to adjust one variable at a time and record changes carefully. Avoid rushing to ‘correct’ incorrect predictions; instead, let the robot’s behavior guide reflection. Research suggests that young learners benefit from scaffolded discussions where they articulate their hypotheses before seeing results, reinforcing cause-and-effect thinking.

Successful learning looks like students confidently predicting robot behavior based on sensor inputs, testing their ideas through trial and error, and clearly explaining connections between what they observe and how the robot reacts.

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Watch Out for These Misconceptions

• During Pair Testing, watch for students assuming the robot can ‘see’ the obstacle without touching it.

  During Pair Testing, have students cover the touch sensor with tape and observe the robot’s movement. Ask them to compare this to runs where the sensor is uncovered, highlighting that contact is required for detection.

• During Sensor Prediction Demo, listen for students saying the robot ‘decides’ what to do on its own.

  During Sensor Prediction Demo, after showing the robot’s programmed reaction, replay the code step-by-step as a class and ask students to point to the exact instruction that triggered the robot’s action.

• During Light Sensor Maze rotations, notice if students treat all sensors as identical in function.

  During Light Sensor Maze, set up a station where students cover the touch sensor instead to directly contrast it with the light sensor’s behavior, prompting them to describe the different conditions each sensor requires.

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Methods used in this brief

• Experiential Learning