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:
Data Collection
Collect all practice session scores over the last 60 days
Trend Analysis
Calculate linear trend using least squares regression
Projection
Extend trend line to target date
Confidence Intervals
Calculate prediction intervals based on historical variance
Factors Influencing Predictions
Recent performance trajectory heavily weights predictions. Accelerating students see upward projections.
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 intervalsConsistency : Steady progress = more confidenceRecency : Recent data weighted more heavilyVariability : 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 opportunityExpected 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 routineCurriculum changes : New topics not yet practicedMotivation shifts : Changes in student engagementTest anxiety : Performance under pressure vs. practice
Improving Prediction Accuracy
More practice data : Aim for 50+ sessions per subjectConsistent frequency : Regular practice improves reliabilityRecent updates : Keep velocity current with fresh dataDiverse 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 
