Integrating Perceptual Learning with External World Knowledge in a Simulated Student

Systems for smart authoring of automated tutors, like SimStudent, have been mostly applied in well-defined problem-solving domains where little real-world background knowledge is needed, like math. Here we explore the generality of these methods by considering a very different task, article selection in English, where little problem-solving is done, but where complex prior perceptual skills and large amounts of background knowledge are needed. This background knowledge includes the ability to parse text and the extensive understanding of semantics of English words and phrases. We show that good performance can be obtained by coupling SimStudent with appropriate broad-coverage linguistic tools. Performance can be improved further on this task by extending one of the learning mechanisms used by SimStudent so that it will accept less-accurate production rule conditions, and prioritize learned production rules by accuracy. Experimental results show that the extended SimStudent successfully learns the tutored article selection grammar rules, and can be used to discover a student model that predicts human student behavior as well as the human-generated model.