Flexible, personalized student success modeling for institutions with complex …

This invention describes a way to predict whether students in non-traditional learning programs will succeed. It works by tracking their progress through different stages, like flexible learning sessions, and using a mathematical model called a Markov model to understand how students move between these stages. This allows the system to identify patterns and build personalized prediction models for different groups of students, specifically for programs with flexible academic sessions.

Why it matters: Filed before the widespread adoption of advanced cloud-based machine learning platforms and the significant growth of flexible, non-traditional educational models. Today, the computational resources and specialized data science tools needed to implement and scale such a system are far more accessible and powerful, and the market for flexible learning has expanded significantly.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, data science, machine learning expertise

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