Learner-Context Modelling: A Bayesian Approach

Abstract

The following paper is a proof-of-concept demonstration of a novel Bayesian model for making inferences about individual learners and the context in which they are learning. This model has implications for both efforts to create rich open leaner models, develop automated personalization and increase the breadth of adaptive responses that machines are capable of. The purpose of the following work is to demonstrate, using both simulated data and a benchmark dataset, that the model can perform comparably to commonly used models. Since the model has fewer parameters and a flexible interpretation, comparable performance opens the possibility of utilizing it to extend automation greater variety of learning environments and use cases.