Dual-model: An Architecture for Utilizing Temporal Information in Student Modeling

A modeling system may be required to predict an agent’s future actions even when confronted by inadequate or contradictory relevant evidence from observations of past actions. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. This raises two issues. First, when maximizing prediction rate is preferable, what mechanisms can be employed such that a system can make more predictions without severely degrading prediction accuracy? Second, for contexts in which accuracy is of primary importance, how can we further improve prediction accuracy? A recently proposed Dual-model approach, which takes models’ temporal characteristics into account, suggests a solution to the first problem, but leaves room for further improvement. This paper presents two classes of Dual-model variant. Each aims to achieve one of the above objectives. With the performance of the original system as a baseline, which does not utilize the temporal information, empirical evaluations in the domain of elementary subtraction show that one class of variant outperforms the baseline in prediction rate while the other does so in prediction accuracy, without significantly affecting other overall measures of the original performance.