  Iterative machine teaching is a method for selecting an optimal teaching
example that enables a student to efficiently learn a target concept at each
iteration. Existing studies on iterative machine teaching are based on
supervised machine learning and assume that there are teachers who know the
true answers of all teaching examples. In this study, we consider an
unsupervised case where such teachers do not exist; that is, we cannot access
the true answer of any teaching example. Students are given a teaching example
at each iteration, but there is no guarantee if the corresponding label is
correct. Recent studies on crowdsourcing have developed methods for estimating
the true answers from crowdsourcing responses. In this study, we apply these to
iterative machine teaching for estimating the true labels of teaching examples
along with student models that are used for teaching. Our method supports the
collaborative learning of students without teachers. The experimental results
show that the teaching performance of our method is particularly effective for
low-level students in particular.
