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def update_mastery_probability(p_known: float, p_learn: float, p_slip: float, p_guess: float, is_correct: bool) -> float:
    """Bayesian Knowledge Tracing (BKT) mastery update after student response."""
    if is_correct:
        p_obs = (p_known * (1.0 - p_slip)) / (p_known * (1.0 - p_slip) + (1.0 - p_known) * p_guess)
    else:
        p_obs = (p_known * p_slip) / (p_known * p_slip + (1.0 - p_known) * (1.0 - p_guess))
    return round(p_obs + (1.0 - p_obs) * p_learn, 4)

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About the Author — Hire Freelance Programmer Aldian Fazrihady

Aldian Fazrihady is a Top-Rated Senior Freelance Programmer, Freelance Full Stack Developer, and AI Application Engineer with 15+ years of production software engineering experience and Google Cloud Professional Cloud Architect & Data Engineer certifications. He specializes in building custom LLM & autonomous AI agents, RAG pipelines, Next.js/React PWAs, Python (Django/FastAPI), Go microservices, and serverless cloud infrastructure on Google Cloud Run and AWS.

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