Strategic Data Project Convening 2026

Session Description: Districts often struggle to build trust with community stakeholders that their input has been captured, accurately represented, and equitably reflected in decision making. Atlanta Public Schools will share how it explored the use of artificial intelligence to support large scale community feedback, including data sources, validation strategies, and efficiency gains. Participants interested in engagement, data, and equity are invited to join a closing discussion on persistent challenges, including how to better surface and amplify voices that have been historically underrepresented.

Intended Audience: Districts, charters or other local education agencies; State education agencies; Higher Education

Session Description: Many districts struggle to ensure that their investments in educational programs improve student outcomes and are implemented with integrity. Participants will explore one district’s successful Outcomes Based Contracting approach—which produced a 0.39 SD effect in middle school math—and gain practical tools including contract templates, a framework for evaluating evidence, and strategies for building data partnerships that drive continuous improvement. Data leaders are especially encouraged to attend to strengthen evidence-based resource allocation practices.

Intended Audience: Districts, charters or other local education agencies; State education agencies; Managers of data or technology teams

Session Description: Instructional coherence is the emerging practice of aligning all aspects of learning to reduce “instructional code-switching” between varying procedures, approaches, and curriculum. In this session, we will share learnings from a district pilot and randomized control trail on implementing instructional coherence for early literacy. Those interested in supporting student literacy, innovative models for supporting student learning interventions, and advancing how to leverage formal and informal student data to meet student needs should attend this session.

Intended Audience: Districts, charters or other local education agencies; State education agencies; Service providers

Session Description: Schools and districts are always searching for ways to streamline data collection and help generate proof points. And while the vast majority of organizations are using the Google Suite, most are using it for its basic functionality and not tapping into its full potential. This session will highlight various tools built in the Google Suite and inspire school/district leaders to think about how to leverage the Google Suite to improve their own orgs.

Intended Audience: Districts, charters or other local education agencies; Nonprofit organizations; Data analysts or independent researchers; Managers of data or technology teams; Senior leaders (including cabinet-level leaders and executive directors)

Session Description: With lingering learning losses from the pandemic and the end of federal aid, asking “Does it work?” is no longer sufficient. Policymakers and school leaders now must ask, “Is it worth it?” when deciding which interventions to fund. This session introduces Accelerate’s Cost Tool (Kohlmoos & Steinberg, 2024), designed to advance the use of cost analysis in education program evaluations. Participants will engage with the tool, explore its features, review real-world use cases, and discuss how standardized cost analysis can improve the transparency and rigor of educational decision-making. Please bring a device to engage with the tool (linked in Sched).

Intended Audience: Districts, charters or other local education agencies; State education agencies; Service providers

*Slides and activity can be found below the presenters*

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Helpful Links & Resources:

• https://accelerate.us/cost-tool/
• Downloadable as: Excel version & Google Sheet
• Summary infographic
• Definition of tutoring dosage (scheduled vs. actual)
• Report on tutoring efficiency
• Cost analysis - report

         Tutorial video

Session Description: SEA staff across the country face shared challenges in data reporting, assessment, and accountability but often lack opportunities to connect with peers in similar roles. This networking session will bring SEA professionals together for structured small-group discussions to share experiences, compare approaches, and learn from one another. SEA staff working in reporting, assessment, accountability, research, or related roles are encouraged to attend.

Intended Audience: State education agencies; Data analysts or independent researchers; Managers of data or technology teams

Session Description: Data initiatives often fail at the adoption stage due to user resistance and data grief. This structured exchange will convene leaders, analysts, and directors to crowd-source solutions for managing data change. Participants will rotate through themed round tables on easing emotional resistance, aligning system design to user workloads, and building trust between data builders and decision-makers.

Intended Audience: Districts, charters or other local education agencies; Managers of data or technology teams; Senior leaders (including cabinet-level leaders and executive directors)"

Session Description: Knowing whether educational improvement efforts are yielding improved student outcomes often depends on information outside state longitudinal systems. From Logs to Learning shows how a data standard for tutoring creates shared definitions that enable transparency, comparability, faster feedback loops, and high-quality continuous improvement. We will share an open tutoring data standard and toolkit, then present a case study with Florida Tutoring Advantage, Littera, and Accelerate demonstrating earlier-cycle monitoring, cross-district benchmarking, and implementation refinement.

Intended Audience: State education agencies; Districts, charters or other local education agencies; Service providers

Session Description: Education data systems are abundant yet fragmented, unstable, and difficult to reuse for
strategic decision-making. This session offers two solutions to address these challenges. First, we present a FAIR-aligned framework and practical tools for mapping, stabilizing and reusing education data from a cross-state study of 3,822 public K-12 datasets. Then we present findings from an empirical study of AI-produced mappings across higher education data sources to show which AI models provide the best balance between performance and cost. Together the session provides an interoperable way to work with K-12 datasets and a means to scale the mapping of education data.

Intended Audience: Districts, charters or other local education agencies; Managers of data or technology teams; Senior leaders (including cabinet-level leaders and executive directors)