Prediction Prompt Interface
This interactive MicroSim demonstrates best practices for designing prediction prompt interfaces in educational simulations. The 5-panel workflow shows how to effectively capture learner predictions before observations, maximizing engagement and learning.
About This MicroSim
The simulation presents an annotated mockup of an ideal prediction prompt sequence, walking through each stage of the prediction-observation-reflection cycle that makes learning stick.
The 5-Panel Workflow
- Setup - Present the scenario clearly with initial conditions visible
- Prediction Input - Capture predictions using multiple input methods
- Reasoning Capture - Optionally capture why learners made their prediction
- Observation - Run the simulation with prediction visible alongside results
- Reflection - Guide explicit acknowledgment and connect to concepts
Key Design Principles
1. Commitment Before Observation
The most critical principle: learners must commit to a prediction before seeing the outcome. This:
- Creates cognitive investment in the result
- Activates prior knowledge and mental models
- Makes the observation personally meaningful
- Enables genuine surprise when expectations differ
2. Multiple Input Methods
Effective prediction interfaces offer various ways to express predictions:
- Multiple choice - Quick, easy to analyze, can include common misconceptions
- Drawing/sketching - For trajectories, graphs, or spatial predictions
- Sliders/values - For quantitative predictions
- Free-form text - For complex reasoning
3. Confidence Indicators
Asking "How sure are you?" (1-5 scale) provides valuable data:
- Identifies areas of uncertainty for targeted instruction
- Helps learners calibrate their metacognition
- Enables adaptive difficulty adjustments
4. No Skip Option
The interface should not allow skipping the prediction step. Commitment is crucial for learning. Design the experience so prediction feels natural and valuable, not like a barrier.
5. Visible Comparison
During and after observation:
- Keep the prediction visible alongside the actual result
- Provide clear visual comparison
- Celebrate engagement, not just correctness
6. Structured Reflection
After observation, guide reflection with:
- Explicit acknowledgment ("Was your prediction correct?")
- Explanation for unexpected results
- Connection to underlying concepts
- Option to retry with new understanding
Color Scheme
The mockup uses a consistent color scheme to signal different types of content:
| Color | Purpose |
|---|---|
| Blue | Prompts and questions |
| Yellow | Learner input areas |
| Green | Observation and results |
| Orange | Reflection and warnings |
Implementation Tips
When implementing prediction prompts in your MicroSims:
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Research Foundation
The prediction-observation-reflection cycle is grounded in:
- Constructivism - Learners build knowledge by testing mental models
- Cognitive conflict - Surprise at unexpected results drives schema change
- Active learning - Engagement improves retention and transfer
- Metacognition - Reflecting on predictions builds self-awareness
Studies show that students who make predictions before observations learn significantly more than those who simply observe, even when their predictions are wrong.
p5.js Editor Template
You can experiment with this code in the p5.js web editor.
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References
- Prediction-Observation-Explanation (POE) - Science Education Research
- Making Predictions to Enhance Learning - The Learning Scientists
- PhET Interactive Simulations - Examples of prediction-based physics simulations
- Cognitive Conflict in Learning - Educational Psychology