6533b7dcfe1ef96bd127278e

RESEARCH PRODUCT

Using System Dynamics to Model Student Performance in an Intelligent Tutoring System

subject

description

One basic adaptation function of an Intelligent Tutoring System (ITS) consists of selecting the most appropriate next task to be offered to the learner. This decision can be based on estimates, such as the expected performance of the student, or the probability that the student successfully solves each particular task. However, the computation of these values is intrinsically difficult, as they may depend on other complex latent variables that also need to be estimated from observable quantities, e.g. the current student's ability. In this work, we have used system dynamics to model learning and predict the student's performance in a given exercise, in an existing ITS that was developed to teach students solve arithmetic-algebraic word problems. The high correlation between the predicted and real scores outlines the potential of this type of modeling as a prediction tool to support the decision about the next task that should be offered to the learner.