Modeling Narrative-Centered Tutorial Decision Making in Guided Discovery Learning

Interactive narrative-centered learning environments offer significant potential for scaffolding guided discovery learning in rich virtual storyworlds while creating engaging and pedagogically effective experiences. Within these environments students actively participate in problem-solving activities. A significant challenge posed by narrative-centered learning environments is devising accurate models of narrative-centered tutorial decision making to craft customized story-based learning experiences for students. A promising approach is developing empirically driven models of narrative-centered tutorial decision-making. In this work, a dynamic Bayesian network has been designed to make narrative-centered tutorial decisions. The network parameters were learned from a corpus collected in a Wizard-of-Oz study in which narrative and tutorial planning activities were performed by humans. The performance of the resulting model was evaluated with respect to predictive accuracy and yields encouraging results.