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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.
We present results from a meta-analysis of 37 experimental and quasi-experimental studies of summer programs in mathematics for children in grades pre-K-12, examining what resources and characteristics relate to stronger student achievement, attainment, and social-emotional and behavioral outcomes. Compared to control group children, children who participated in summer programs that included mathematics lessons and activities enjoyed significant improvements in mathematics learning as well as social-behavioral outcomes. We find an average weighted impact estimate of +0.09 standard deviations on mathematics achievement outcomes. In a parallel meta-analysis, we found similar positive impacts of summer programs on socialemotional and behavioral outcomes, Programs conducted in both high- and lower-poverty settings saw similar positive impacts. The results highlight the potential for summer programs to strengthen children’s mathematical ability and improve learning outcomes in both mixed-poverty and high-poverty settings.
Despite calls for more evidence regarding the effectiveness of teacher education practices, causal research in the field remains rare. One reason is that we lack designs and measurement approaches that appropriately meet the challenges of causal inference in the context of teacher education programs. This article provides a framework for how to fill this gap. We first outline the difficulties of doing causal research in teacher education. We then describe a set of replicable practices for developing measures of key teaching outcomes, and propose causal research designs suited to the needs of the field. Finally, we identify community-wide initiatives that are necessary to advance effectiveness research in teacher education at scale.