Interactive Learning

Explain how Bayesian Knowledge Tracing (BKT) estimates a learner's mastery probability from a sequence of responses.

Implement a basic BKT model in pseudocode to compute updated mastery estimates.

Describe how Deep Knowledge Tracing (DKT) uses recurrent neural networks to model learner knowledge.

Compare the trade-offs between BKT and DKT in terms of accuracy, interpretability, and data requirements for practical applications.

Write pseudocode for a content sequencing algorithm that selects the next learning activity based on a learner's knowledge state and prerequisite graph.

Design the high-level architecture of an adaptive learning platform, illustrating the data flow between the core components.

Define the essential API endpoints and data structures for communication between the frontend, backend, and adaptation engine.