Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect…

Students reasoning with data in an authentic science environment had the opportunity to learn about the process of science and the world around them while developing skills to analyze and interpret self-collected and secondhand data. Our results show that nearly 50% of the treatment group responses were accurate when describing the reason for…

Qualitative interviews and focus groups are commonly used methods to elicit participants' voices in program evaluations. However, the use of these data-gathering methods can fall short of the goal; even with open-ended questions, the protocols guiding and shaping interviews and focus groups heavily reflect the evaluators' understanding and…

We present a historical case study of "data-driven" general education policy reform at the City University of New York, where within-system transfer issues prompted the need for curricular reform that was debated and eventually implemented from 2011 to 2017. Through an empirical examination of artifacts such as meeting minutes, internal…

Background: Data missingness can bias interpretation and outcomes resulting from data use. We describe data missingness in the longest-standing US-based youth fitness surveillance system (2006/07-2019/20). Methods: This observational study uses the New York City FITNESSGRAM (NYCFG) database from 1,983,629 unique 4th-12th grade students (9,147,873…

One of the biggest challenges that states and local intermediaries face in setting up and scaling high-quality youth apprenticeships is gathering relevant, accurate and actionable data. High-quality data is an essential ingredient for a strong youth apprenticeship program because it equips state and local leaders to evaluate impact, monitor…

A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While…

This article describes five elementary statistics projects involving the COVID-19 data made available to the public in csv files by the Centers for Disease Control and Prevention. The first project examined data available at the beginning of the COVID surge in New York City in spring, 2020, and used the correlation coefficient to estimate the…

"Creating Purposeful Partnerships" offers insights into business-led regional talent ecosystems that facilitate access, alignment, and development of a prepared workforce with the skills necessary for companies' long-term success. The findings of this report serve as a playbook for CEOs and their executive teams for establishing…

Qualitative training rarely acknowledges the role of emotions in both data collection and analysis. While bracketing emotions is an important part of reflexivity, emotions are both a source of data and a source of 'work' (Hochschild, 1983). Accordingly, mentoring junior qualitative scholars also requires emotion work. Issues of race, gender, and…

One way to provide intensive intervention for students with severe and persistent reading difficulties is to use a systematic data-based decision-making process called data-based individualisation (DBI). DBI is a process for identifying needs and aligning them with specialist support. Meta-analyses of DBI studies by university-based researchers…

This study examines the implementation of a course module that integrated computing and data analysis. The module aimed to engage prospective mathematics teachers in the practice of working with datasets and analyzing data to investigate questions related to local Brooklyn schools, as well as to gain deeper insight into the students and…

In this first issue of "Research Library Issues" ("RLI") in 2019, the authors explore privacy from a legal, digital, and applied perspective, with a focus on the implications and opportunities for research libraries. The current privacy landscape highlights the need for a nuanced understanding of the complicated nature of…

The school discipline literature has expanded rapidly in recent decades, yet the conceptualization and measurement of school discipline patterns remains overlooked. In this paper, we present a comprehensive analytic framework to examine school discipline patterns that encompasses school-level metrics that capture the prevalence and disparity in…

In this article, Rachel Abenavoli, Natalia Rojas, Rebecca Unterman, Elise Cappella, Josh Wallack, and Pamela Morris argue that research-practice partnerships make it possible to rigorously study relevant policy questions in ways that would otherwise be infeasible. Randomized controlled trials of small-scale programs have shown us that early…