Gender differences and the value of choice in intelligent tutoring systems
Derek T. Green, Thomas J. Walsh, Paul R. Cohen, Carole R. Beal, Yu-Han Chang · 2011
Abstract. Students interacted with an intelligent tutoring system to learn grammatical rules for an artificial language. Six tutoring policies were explored. One, based on a Dynamic Bayes ’ Network model of skills, was learned from the performance of previous students. Overall, this policy and other intelligent policies outperformed random policies. Some policies allowed students to choose one of three problems to work on, while others presented a single problem at each iteration. The benefit of choice was not apparent in group statistics; however, there was a strong interaction with gender. Overall, women learned less than men, but they learned different amounts in the choice and no choice conditions, whereas men seemed unaffected by choice. We explore reasons for these interactions between gender, choice and learning. Problem-oriented Intelligent Tutoring Systems (ITS) teach a subject by presenting problems to students to solve (e.g., [10,4]). One form of intelligence in these systems is the policies that decide which problem(s) to present. Generally speaking,