A Cluster-based Predictive Modeling to Improve Pedagogic Reasoning

This paper discusses a cluster knowledge-based predictive modeling framework actualized in a learning agent that leverages on the capability of a clustering algorithm to discover in logged tutorial interactions unknown structures that may exhibit predictive characteristics. The learned cluster models are described along learner-system interaction attributes, i.e., in terms of the learner’s knowledge state and behaviour and system’s tutoring actions. The agent utilizes the knowledge of its various clusters to learn predictive models of high-level student information that can be utilized to support fine-grained individualized adaptation. We investigated on utilizing the Self-Organizing Map as clustering algorithm, and the naïve Bayesian classifier and perceptron as weighting algorithms to learn the predictive models. Though the agent faced the difficulty imposed by the experimentation dataset, empirical results show that utilizing cluster knowledge has the potential to improve coarse-grained prediction for a more informed and improved pedagogic decision-making.