Bayesian Knowledge Tracing Driven Adaptive Learning for Personalized GCE Examination Preparation

Abstract

Examination preparation for the General Certificate of Education in Cameroon relies heavily on static past question booklets that treat every learner identically regardless of what that learner has already mastered. This paper reports the design, implementation, and simulation-based validation of a Bayesian Knowledge Tracing-driven adaptive quiz engine built for GCE Ordinary Level Chemistry within the ToriLearn prototype platform. The engine estimates a learner's mastery of eleven syllabus-defined knowledge components after every response, using the canonical four-parameter Bayesian Knowledge Tracing formulation together with penalty adjustments for hint use, skipped questions, and viewed answers, and it feeds the resulting mastery estimate into a real-time decision engine that selects the next question's difficulty. We describe the mathematical formulation as implemented, the sequence of operations executed on every answer submission, and three synthetic learner scenarios constructed to test whether the engine's behaviour matches the behaviour the underlying model predicts. Results show that a consistently correct simulated learner reaches high mastery within a small number of attempts and is advanced to harder material, that a consistently incorrect learner is retained on remedial content without mastery collapsing to zero, and that a learner with alternating performance produces a fluctuating difficulty trajectory that tracks the fluctuating mastery estimate. The endpoint that performs this computation responded within 45 to 80 milliseconds under local development testing. No real student data were used to fit the model parameters and no classroom trial was conducted, so the contribution of this paper is a validated implementation of the Bayesian mechanism rather than measured evidence of examination performance improvement.