Iterative Classroom Teaching
This addresses the problem of efficient teaching in AI education for diverse learners, but it is incremental as it builds on existing machine teaching frameworks.
The paper tackles the machine teaching problem in a classroom setting with diverse students, proving that a teacher with full knowledge can teach a target concept using O(min{d,N} log(1/eps)) examples, and shows robustness with limited knowledge and trade-offs in workload and cost.
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O(min{d,N} log(1/eps)) examples, where d is the ambient dimension of the problem, N is the number of learners, and eps is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners' internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners' workload and the teacher's cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.