Cluster-based predictive modeling to improve pedagogic reasoning

This paper discusses a predictive modeling framework actualized in a learning agent that uses logged tutorial interactions to discover predictive characteristics of students. The agent automatically forms cluster models that are described in terms of student–system interaction attributes, i.e., in terms of the student’s knowledge state and behaviour and system’s tutoring actions. The agent utilizes the knowledge of its various clusters together with a weighting scheme to learn predictive models of high-level student information, specifically, the time it will take the student to respond to a problem and whether the response is correct, that can be utilized to support individualized adaptation. We investigated utilizing the Self-Organizing Map and AutoClass as clustering algorithms and the naïve Bayesian classifier and single layer neural network as weighting algorithms. Empirical results show that by utilizing cluster knowledge the agent’s predictions are acceptably strong for response time and accurate at the average for response correctness. Further investigation is needed to validate the scalability of the framework given other datasets and possibly migrate to other approaches that can obtain more meaningful cluster models, detect richer attribute relations, and provide better approximations to further improve prediction of response behaviour for a more informed pedagogical decision-making by the system.