@article { guerra20, abstract = {In this work we empirically explore an interactive approach for machine teaching with classes of students. We use interactivity to overcome the common mismatch between the knowledge the teacher has about the students and the students themselves. We analyze a specific situation where the students learning algorithm is known but the corresponding parameters are not. We focus on the case of Bayesian Gaussian learners, where the lack of knowledge regarding the students parameters significantly deteriorates the performance of machine teaching. 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 have shown that, regardless of considering partitions or not, the interactive approach increases the learning performance of the class (reducing the teaching dimension) when compared to the non-interactive approach.}, journal = {2nd AIMA4EDU Workshop, IJCAI}, keywords = {Miscellaneous;}, title = {Interactive Teaching with Unknown Classes of Bayesian Learners}, year = {2020}, author = {Carla Guerra and Francisco S. Melo and Manuel Lopes} }

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