  With the increasing demand for large amount of labeled data, crowdsourcing
has been used in many large-scale data mining applications. However, most
existing works in crowdsourcing mainly focus on label inference and incentive
design. In this paper, we address a different problem of adaptive crowd
teaching, which is a sub-area of machine teaching in the context of
crowdsourcing. Compared with machines, human beings are extremely good at
learning a specific target concept (e.g., classifying the images into given
categories) and they can also easily transfer the learned concepts into similar
learning tasks. Therefore, a more effective way of utilizing crowdsourcing is
by supervising the crowd to label in the form of teaching. In order to perform
the teaching and expertise estimation simultaneously, we propose an adaptive
teaching framework named JEDI to construct the personalized optimal teaching
set for the crowdsourcing workers. In JEDI teaching, the teacher assumes that
each learner has an exponentially decayed memory. Furthermore, it ensures
comprehensiveness in the learning process by carefully balancing teaching
diversity and learner's accurate learning in terms of teaching usefulness.
Finally, we validate the effectiveness and efficacy of JEDI teaching in
comparison with the state-of-the-art techniques on multiple data sets with both
synthetic learners and real crowdsourcing workers.
