Frameworks

Three frameworks guide Datadaptive’s work: one for organizational culture, one for the work itself, and one for the data literacy that work requires.

What it means to be datadaptive

Datadaptive education organizations

Use the framework for datadaptive culture to build an organizational culture with a data-literate educator workforce at its foundation, using data to inform evidence-based practices that solve complex problems-of-practice.

  • Build a culture of adaptive transformation, with datadaptive leaders willing and able to lead and coach datadaptive workers, who are willing and able to manage and do datadaptive work.
  • Build a culture of organizational learning that promotes and nurtures people’s innate desire and capacity to learn.
  • Build a culture of continuous improvement that uses data to inform evidence-based practices.

Datadaptive educational leaders

Use the Id.E.A.L. cycle of inquiry to:

  • Lead and coach datadaptive work to solve complex problems-of-practice.
  • Make evidence-based decisions to adopt, adapt, or abandon datadaptive solutions.
  • Lead and coach datadaptive work for data literacy development.

Datadaptive educators

Use the Id.E.A.L. cycle of inquiry to:

  • Manage and do datadaptive work to solve complex problems-of-practice.
  • Collect data to inform and advise datadaptive leaders making evidence-based decisions to adopt, adapt, or abandon datadaptive solutions.
  • Manage, do, articulate, and observe datadaptive work for data literacy development.

The framework for datadaptive culture

A datadaptive culture joins three cultures (continuous improvement, organizational learning, and adaptive transformation) on a foundation of an equity-focused, data-literate workforce.

Diagram: The Framework for Datadaptive Culture. Three overlapping petals (culture of continuous improvement, culture of organizational learning, and culture of adaptive transformation) sit on a circle labeled equity-focused data-literate workforce. Where all three meet is datadaptive culture. Sources: adaptive leadership, organizational learning, improvement science, and data literacy research.

The Id.E.A.L. cycle of inquiry

The framework for datadaptive organizational improvement moves through four phases (Identify, Explore, Analyze, and Lead) in ten steps, then reiterates.

Diagram: The Framework for Datadaptive Organizational Improvement (Id.E.A.L.) for education organizations. A cycle of four phases surrounds the student, teacher, and content triangle with student engagement at its center. Identify: identify a complex problem-of-practice, generate questions, construct a theory-of-action, and apply data governance policies. Explore: discover, collect, and manage data. Analyze: analyze data and investigate findings. Lead: decide to adopt, adapt, or abandon the datadaptive solution, then reiterate.

The framework for data literacy development

Six levels of data literacy, from observing datadaptive work to leading it. Datadaptive workers observe, articulate, do, and manage; datadaptive leaders coach and lead.

Diagram: The Datadaptive Framework for Data Literacy Development. A rising scale of six levels, from lower data literacy to higher: 1 Observe, 2 Articulate, 3 Do, and 4 Manage datadaptive work (datadaptive workers), then 5 Coach and 6 Lead datadaptive work (datadaptive leaders). Each level lists its tasks, such as using data to inform datadaptive work, building capacity for data use, and giving or receiving coaching and professional learning.

Use-cases

Datadaptive helps education organizations construct use-cases. Three examples:

AI-powered personalized learning

Diagram: A Datadaptive Use-case, AI-powered Personalized Learning. An organization's data passes through a funnel of four filters (organizational culture, data governance, data literacy, and good data practices), each tied to a datadaptive framework, to yield good data, then good artificial intelligence, then AI-powered personalized learning. Beside it, unfiltered internet data flows straight into generative artificial intelligence, marked with a question mark.

A 5-whys funnel to define priority problems-of-practice

Diagram: Educator Preparation Data Analytics System, a 5-whys funnel to define priority adaptive problems-of-practice. Starting from what educational leadership preparation program leaders, faculty, and staff don't know about their applicants, participants, and completers, five successive why statements narrow to a root cause: a lack of data literacy to conceptualize the technological, organizational, and environmental factors for adopting a data analytics system.

A decision tree to determine priority use-case #1

Diagram: Educator Preparation Data Analytics System, a decision tree to determine priority use-case #1. Four priority problems-of-practice each pass through the same questions: Is it an adaptive challenge? Does data exist? Is the data robust, accessible, and comprehensible? The answers rank the use-cases, and priority use-case #1 is applicant, enrollment, completion, and program completer employment trends.

Put the frameworks to work

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