Artificial Student Agents and Course Mastery Tracking

In an effort to meet the changing landscape of education many departments and universities are offering more online courses – a move that is likely to impact every department in some way (Rover et al., 2013). This will require more instructors create online courses, and we describe here how agents and dynamic Bayesian networks can be used to inform this process. Other innovations in instructional strategies are also widely impacting educators (Cutler et al., 2012) including peer instruction, flipped classrooms, problem-based learning, just-in-time teaching, and a variety of active learning strategies. Implementing any of these strategies requires changes to existing courses. We propose ENABLE, a graph-based methodology, to transform a standard linear in-class delivery approach to an on-line, active course delivery system (DuHadway and Henderson, 2015). The overall objectives are: (1) to create a set of methods to analyze the content and structure of existing learning materials that have been used in a synchronous, linearly structured course and provide insight into the nature and relations of the course material and provide alternative ways to organize them, (2) to provide a Bayesian framework to assist in the discovery of causal relations between course learning items and student performance, and (3) to develop some simple artificial student agents and corresponding behavior models to probe the methods’ efficacy and accuracy. In this paper, we focus on our efforts on the third point.