Evaluation of a Personalized Method for Proactive Mind Wandering Reduction

We report on a project with the goal of creating a proactive system that attempts to reduce the propensity to mind wander (MW) by optimizing learning conditions (e.g., text difficulty and value) for individual learners. Our previous work had shown that supervised classification based on individual attributes could be used to detect the learning condition with the lowest MW rates. Here we test the model by comparing MW rates for the predicted optimal conditions to MW rates from a random control condition or in the condition with the overall best MW rate across all learners. Our results suggest that our method is better than these non-adaptive alternatives in certain contexts.