Iterative Machine Teaching without Teachers

TL;DR

This paper introduces an unsupervised iterative machine teaching approach that estimates true labels from crowdsourced responses, enabling collaborative learning without teachers, especially benefiting low-level students.

Contribution

It proposes a novel method combining label estimation and student modeling for unsupervised iterative teaching, extending machine teaching to crowdsourced, teacher-free scenarios.

Findings

Effective teaching performance for low-level students

Successful estimation of true labels from crowdsourcing responses

Supports collaborative learning without teachers

Abstract

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.