adaptive-course-learner

Adaptive learning contexts from Canvas course modules are built by fetching module data via Canvas APIs and applying reinforcement learning with perceptrons to iteratively refine contextual knowledge sequences for personalized study guides.

Reinforcement learning improves course content personalization by using an agent to build and refine contextual sequences from module items, progressively enhancing context quality and relevance based on learned usefulness and learner interactions.

Yes, you can use a perceptron network to extract features from Canvas course module items, which then informs the reinforcement learning agent to assemble and refine the quality of adaptive knowledge contexts.

The best way to assemble adaptive study guides from Canvas modules is to iteratively add modules based on learned usefulness, allowing the reinforcement learning agent to progressively improve context quality and relevance for study outcomes.

Yes, creating adaptive course contexts requires setting up a project folder, installing necessary packages, and running the learner to fetch your Canvas course and start building the adaptive context from module data.