Method and system for bayesian network-based standard or skill mastery determination using a collection of interim assessments

This invention describes a software system that uses a special map (a Q-matrix) to link parts of a learning standard to questions in a test. When someone takes a test, the system uses their answers to calculate how likely they are to have mastered a particular skill or standard, using a statistical model called a Bayesian network. The claims specifically cover using multiple short, interim tests to update this mastery probability over time.

Why it matters: Filed before the widespread availability of highly optimized machine learning frameworks and cloud-based AI services. The computational demands of Bayesian networks for real-time adaptive assessment, as described in the claims, are now more readily met with off-the-shelf tools and scalable infrastructure.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedSoftware logic, Bayesian networks, Q-matrix implementation

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