Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these designers use A/B experiments to evaluate each intervention; however, it is costly and time consuming to run such studies. To address this issue, we explore how computational models of learning might support designers in reasoning causally about alternative interventions within a fractions tutor. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Next, we conduct simulations to generate counterfactual predictions of performance and learning for two students (high and low performing) in different versions of the fractions tutor. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments.

A YOUNG MAN, let's call him Roger, arrives at the emergency department complaining of belly pain and nausea. A physical exam reveals that the pain is focused in the lower right portion of his abdomen. The doctor worries that it could be appendicitis. But by the time the imaging results come back, Roger is feeling better, and the scan shows that his appendix appears normal. The doctor turns to the computer to prescribe two medications, one for nausea and Tylenol for pain, before discharging him. This is one of the fictitious scenarios presented to 55 physicians around the country as part of a study to look at the usability of electronic health records (EHRs).

A young man, let's call him Roger, arrives at the emergency department complaining of belly pain and nausea. A physical exam reveals that the pain is focused in the lower right portion of his abdomen. The doctor worries that it could be appendicitis. But by the time the imaging results come back, Roger is feeling better, and the scan shows that his appendix appears normal. The doctor turns to the computer to prescribe two medications, one for nausea and Tylenol for pain, before discharging him. This is one of the fictitious scenarios presented to 55 physicians around the country as part of a study to look at the usability of electronic health records (EHRs).