Interactive Teaching with Groups of Unknown Bayesian Learners

Carla Guerra, Francisco S. Melo, Manuel Lopes

Abstract: In this work we empirically explore the extension of an interactive approach for machine teaching from single learners to groups of learners. We use interactivity to overcome the common mismatch between the knowledge the teacher has about the students and the students themselves. With a multi-learner setting we also investigated the best way to consider the class—as a whole or divided in partitions accordingly to the students priors. The results of an user study where we teach a Bayesian estimation task have shown that, regardless of considering partitions or not, the interactive approaches significantly increase the learning performance of the class when compared to non-interactive alternatives.