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 linguisticallymotivated 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 repetitionbased 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 contemporary experimental and quasi-experimental studies of summer programs in mathematics for children in grades pre-K–12, examining what resources and characteristics predict stronger student achievement. Children who participated in summer programs that included mathematics activities experienced significantly better mathematics achievement outcomes compared to their control group counterparts. We find an average weighted impact estimate of +0.10 standard deviations on mathematics achievement outcomes. We find similar effects for programs conducted in higher- and lower-poverty settings. We undertook a secondary analysis exploring the effect of summer programs on noncognitive outcomes and found positive mean impacts. The results indicate that summer programs are a promising tool to strengthen children’s mathematical proficiency outside of school time.

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In this article, Zid Mancenido examines how high-achieving students are socialized to believe that they should not become K–12 classroom teachers. Research has well established that academically successful students are often disinterested in teaching as a career, yet there has been little attention to how this disinterest is developed through the process of career exploration. To address this gap in the literature, Mancenido conducts a narrative inquiry based on interviews with high-achieving recent college graduates and graduating seniors. He presents six representative vignettes to demonstrate how high achievers learn through explicit and implicit signals that teaching is not appropriate for someone like them. This process is social, with parents and peers playing a significant role in shaping beliefs. These findings suggest that policy efforts to recruit more high achievers into teaching may benefit from more focus earlier in the career exploration pipeline.