Human performance optimization (HPO) is centered around designing tools and interventions that increase human performance. Typically, A/B experiments are used to evaluate alternative HPO interventions to determine which yield the best performance. Unfortunately, A/B experiments are time consuming and costly to run. Further, each new intervention requires an additional A/B experiment to evaluate it, which means that it is often feasible to only evaluate a few intervetions. To overcome these issues, we exploring the use of computational models of humans—or computer models that receive the same stimuli as human A/B experiment participants and then make the same decisions they do—to simulate A/B experiments and support reasoning about about hypothetical and counterfactual HPO interventions. Additionally, we are also exploring how data about specific individuals can be leveraged to create tuned computational models that better approximate and predict the behavior of specific individuals they were tuned to.
Here is an example of a prediction form our model for a fractions tutoring system HPO intervention (see [MacLellan, Stowers, and Brady, 2020][ACS 2020] for more information):
MacLellan, C.J., Stowers, K., Brady, L. (2020). Optimizing Human Performance using Individualized Computational Models of Learning. Proceedings of the Eighth Annual Conference on Advances in Cognitive Systems.