Skip to main content

​
Overview

The Cognitive Learning Engine uses advanced predictive analytics to forecast student performance, estimate goal achievement timelines, and provide data-driven study recommendations.

​
Score Predictions

​
Basic Usage

from cognition_engine import CognitionEngine

engine = CognitionEngine(supabase_url, supabase_key)

# Get predictions
predictions = await engine.get_predictions("student_123")

print(f"Current SAT Math: {predictions['current_math']}")
print(f"Predicted SAT Math in 30 days: {predictions['predicted_math_in_30_days']}")
print(f"Goal progress: {predictions['goal_progress_percent']}%")

​
Prediction Response

{
    "current_math": 620,
    "current_rw": 580,
    "current_total": 1200,
    "predicted_math_in_30_days": 680,
    "predicted_rw_in_30_days": 640,
    "predicted_total_in_30_days": 1320,
    "confidence_intervals": {
        "math": {"low": 660, "high": 700},
        "rw": {"low": 620, "high": 660},
        "total": {"low": 1290, "high": 1350}
    },
    "goal_tracking": {
        "target_score": 1400,
        "days_to_goal": 45,
        "progress_percent": 75,
        "on_track": True
    }
}

​
How Predictions Work

​
Linear Regression Model

Predictions use linear regression based on historical performance trends:
1

Data Collection

Collect all practice session scores over the last 60 days
2

Trend Analysis

Calculate linear trend using least squares regression
3

Projection

Extend trend line to target date
4

Confidence Intervals

Calculate prediction intervals based on historical variance

​
Factors Influencing Predictions

Current velocity directly impacts predicted improvement rates.
More consistent practice leads to more reliable predictions and faster projected gains.
Students with higher efficiency metrics progress faster than those with lower efficiency.

​
Goal Tracking

​
Setting Goals

Track progress toward specific score targets: 
# Student wants 1400 total SAT score
goal_tracking = predictions['goal_tracking']

if goal_tracking['on_track']:
    print(f"On track! {goal_tracking['days_to_goal']} days to goal")
else:
    print(f"Need to accelerate by {goal_tracking['acceleration_needed']}")

​
Goal Metrics

​
Confidence Intervals

​
Understanding Confidence Ranges

"confidence_intervals": {
    "math": {"low": 660, "high": 700},
    "rw": {"low": 620, "high": 660},
    "total": {"low": 1290, "high": 1350}
}
Confidence intervals represent the range where we expect the actual score to fall with 95% probability. Narrower intervals indicate more reliable predictions.

​
Factors Affecting Confidence

  • Sample size: More data = narrower intervals
  • Consistency: Steady progress = more confidence
  • Recency: Recent data weighted more heavily
  • Variability: Lower variance = tighter estimates
Predictions become less reliable beyond 60 days. Always use recent velocity data for long-term projections.

​
Recommendations

The engine provides personalized study recommendations based on predictions: 
recommendations = predictions['recommendations']

for rec in recommendations:
    print(f"{rec['priority']}: {rec['action']}")
    print(f"Expected impact: {rec['impact']}")
Example recommendations:
High Priority: Focus on Reading/Writing fundamentals - largest growth opportunity
Expected Impact: +25 points in 30 days
Medium Priority: Increase math practice frequency by 20%
Expected Impact: Accelerate trajectory by 5 days

​
Scenario Planning

​
Optimistic Scenario

scenarios = predictions['scenarios']

print(f"Optimistic: {scenarios['optimistic']}")
# Assumes: Increased practice, improved efficiency

​
Conservative Scenario

print(f"Conservative: {scenarios['conservative']}")
# Assumes: Current pace continues

​
Realistic Scenario

print(f"Realistic: {scenarios['realistic']}")
# Most likely outcome based on current trajectory

​
Best Practices

Review predictions weekly to track progress and adjust goals as needed.
Don’t make significant decisions based on predictions beyond 60 days. Short-term projections are more reliable.
Combine predictions with velocity analysis for the most complete picture of future performance.

​
Limitations

​
What Predictions Can’t Account For

  • Life events: Unexpected disruptions to study routine
  • Curriculum changes: New topics not yet practiced
  • Motivation shifts: Changes in student engagement
  • Test anxiety: Performance under pressure vs. practice

​
Improving Prediction Accuracy

  1. More practice data: Aim for 50+ sessions per subject
  2. Consistent frequency: Regular practice improves reliability
  3. Recent updates: Keep velocity current with fresh data
  4. Diverse problems: Mix difficulty levels and topics

​
Integration Example

# Dashboard integration
async def get_student_dashboard(user_id):
    predictions = await engine.get_predictions(user_id)
    velocity = await engine.get_learning_velocity(user_id)
    
    return {
        "current_score": predictions['current_total'],
        "predicted_score": predictions['predicted_total_in_30_days'],
        "goal_progress": predictions['goal_tracking']['progress_percent'],
        "momentum": velocity['momentum_score'],
        "recommendations": predictions['recommendations']
    }

​
Next Steps

Learning Velocity

Understand how velocity powers predictions

API Reference

Explore the complete API documentation