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← All glossary termsDelivery & Pedagogy

Adaptive Learning

Also known as: personalised learning, adaptive pathways

Adaptive learning is an instructional approach in which content, pacing, and assessment difficulty adjust to each learner's observed performance — typically through Bayesian or item-response-theory models running over learner interaction data.

Adaptive learning moves away from a single linear path through a course. The platform observes how a learner answers diagnostic questions, how long they spend on each concept, and which prerequisite gaps surface — then picks the next item from a curated library. Two well-studied model families dominate:

  • Item Response Theory (IRT) — estimates learner ability and item difficulty as latent variables; widely used for high-stakes adaptive testing (GRE, GMAT).
  • Bayesian Knowledge Tracing (BKT) and its neural extensions (DKT, SAKT) — model the probability that a learner has mastered a knowledge component, updated after every interaction.

Related terms

Learning Management System (LMS)

A learning management system (LMS) is software that delivers, tracks, and reports on learning activities — courses, assessments, certificates, and learner progress — for a defined audience such as a university, training institute, or enterprise.

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