Thinking...
Six specialized algorithms operating in parallel through the Synchronization Engine (370). Each algorithm addresses a distinct dimension of the learning process.
Claims 1, 25-30 | Latency Budget: 200ms
The Sync Engine orchestrates all six algorithms in parallel, detects conflicts between recommendations, applies priority weighting, and synthesizes a unified learning recommendation within the 200ms latency budget.
DDE
(310)
PFF
(320)
NBS
(330)
MDF
(340)
CALO
(350)
ASR
(360)
Unified Recommendation
Priority: DDE > ASR > PFF > NBS > CALO > MDF
Maintains learners in the optimal difficulty zone (flow zone) using Bayesian success probability estimation and adaptive question selection.
Flow Zone Min
0.60
Flow Zone Max
0.85
Target Probability
0.72
Max Difficulty Change
0.15
Models memory retention using modified Ebbinghaus curves with interference factors, scheduling optimal review times to prevent knowledge decay.
Gamma Interference
0.15
Similarity Threshold
0.7
Half-Life Range
0.5-365 days
Models concept relationships as a Bayesian network, determining optimal learning sequences based on prerequisite mastery and Hebbian connection strength.
Hebbian Learning Rate
0.01
Connection Decay
0.001
PID-controlled scaffolding system that dynamically adjusts support level, gradually reducing assistance as learner competence increases.
PID Kp
0.3
PID Ki
0.05
PID Kd
0.1
Decay Exponent
0.15
Estimates cognitive load in real-time and selects optimal content modality to prevent overload while maximizing learning efficiency.
Capacity Estimation
Dual-Process
Modality Selection
Adaptive
Derives learner emotional state from behavioral signals (timing, accuracy, patterns) without requiring webcams or sensors. Triggers tiered interventions on crisis.
Anxiety Weight
0.4
Frustration Weight
0.6
Crisis Threshold
0.8