A bayesian model of individual differences and flexibility in inductive reasoning for categorization of examples

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. Intelligent tutoring systems can potentially facilitate this work for teachers by supporting the automatic adaptation of examples based on a student model of the induction process. However, current models of inductive reasoning usually lack two main characteristics helpful to adaptive learning environments, individual differences of students and tracing of students' learning as they receive feedback. In this paper, we describe a model to predict and simulate inductive reasoning of students for a categorization task. Our approach uses a Bayesian model for describing the reasoning processes of students. This model allows us to predict students' choices in categorization questions by accounting for their feature biases. Using data gathered from 222 students categorizing three topics, we find that our model has a 75% accuracy, which is 10% greater than a baseline model. Our model is a contribution to learning analytics by enabling us to assign different bias profiles to individual students and tracking these profile changes over time through which we can gain a better understanding of students' learning processes. This model may be relevant for systematically analysing students' differences and evolution in inductive reasoning strategies while supporting the design of adaptive inductive learning environments.