  In conversation, uptake happens when a speaker builds on the contribution of
their interlocutor by, for example, acknowledging, repeating or reformulating
what they have said. In education, teachers' uptake of student contributions
has been linked to higher student achievement. Yet measuring and improving
teachers' uptake at scale is challenging, as existing methods require expensive
annotation by experts. We propose a framework for computationally measuring
uptake, by (1) releasing a dataset of student-teacher exchanges extracted from
US math classroom transcripts annotated for uptake by experts; (2) formalizing
uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next
utterance classification; (3) conducting a linguistically-motivated comparison
of different unsupervised measures and (4) correlating these measures with
educational outcomes. We find that although repetition captures a significant
part of uptake, pJSD outperforms repetition-based baselines, as it is capable
of identifying a wider range of uptake phenomena like question answering and
reformulation. We apply our uptake measure to three different educational
datasets with outcome indicators. Unlike baseline measures, pJSD correlates
significantly with instruction quality in all three, providing evidence for its
generalizability and for its potential to serve as an automated professional
development tool for teachers.
