Mastery Pathing

Adaptive-learning engine. From a curriculum's prerequisite map (topics + prerequisites) and learners' answers (learner, topic, question, correct, time) it returns: graph checks (loops with their path, undefined prerequisites, redundant links, levels, a valid teaching order); each learner's mastery of each topic (Bayesian Knowledge Tracing fitted per topic, standard settings at cold start; Elo rating for chance of a right answer and question difficulty); the knowledge frontier (topics ready to learn: not mastered, all prerequisites mastered, Knowledge Space Theory) with the next best topic and why; review-first when stuck; gaps blocking a goal and the path to it with time; shaky foundations; curriculum checks from data (topics that behave like two skills, practice with no learning gain, prerequisite links the data contradicts or suggests). Learners are never ranked. Decision cards first. Up to 5,000 answers; larger: mastery_pathing_get_engine.

For answer logs over 5,000 rows or curricula over 2,000 topics, or any such file already in your code sandbox. Returns a complete numpy-only Python script with the same engine as mastery_pathing; it reads topics, prerequisite links, answers and stored topic settings, prints the decision cards and each learner's next topic, and writes everything to mastery_plan.json. No learner data reaches this server on this path.