Ryan Carlson, Victoria Keiser, Noboru Matsuda, Kenneth R. Koedinger, Carolyn Penstein Rosé
SimStudent, an intelligent-agent architecture that generates a cognitive model from worked-out examples, currently interacts with human subjects only in a limited capacity. In our application, SimStudent attempts to solve algebra equations, querying the user about the correctness of each step as it solves, and the user explains the step in natural language. Based on that input, SimStudent can choose to ask further questions that prompt the user to think harder about the problem in an attempt to elicit deeper responses. We show how text classification techniques can be used to train models that can distinguish between different categories of student feedback to SimStudent, and how this enables interaction with SimStudent in a pilot study.
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_73.