01 - Adaptive Learning Algorithms: From Theory to Production

Implementing adaptive learning algorithms: IRT, knowledge tracing, personalized paths and A/B testing in production.

What you'll learn

• IRT provides a solid mathematical foundation for item difficulty calibration and Computerized Adaptive Testing
• BKT is interpretable and fast; DKT is more accurate for complex multi-KC interactions
• Separate your online serving path (Redis + FastAPI) from offline training (batch jobs)
• A/B test with student-level hashing to prevent contamination between variants
• Monitor for concept drift; curricula and student populations change over academic cycles

This article is part of the EdTech series on federicocalo.dev.

Read the full article

The complete article (28 min read) with code examples, diagrams, and practical exercises is available here:

➡️ 01 - Adaptive Learning Algorithms: From Theory to Production

https://federicocalo.dev/en/blog/adaptive-learning-algorithms-theory-to-production

By Federico Calò — Software Developer & Technical Writer