Data-Driven Interventions

Teacher leader uses data to design an effective intervention with other educators.
Made by Jacobs Institute for Innovation in Learning at USD
Earn Graduate Credit
Graduate-level credit is available for this micro-credential. You can apply for credit through one of our university partners after successfully completing the micro-credential.
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About this Micro-credential

Key Method

Teacher leader uses data to identify and communicate areas where other educators can shift their practice to maximize student improvement.

Method Components

Strategies for identifying and communicating areas of improvement for other educators

  • Arrange a time for you and another educator to review the educator’s data with them collaboratively.
  • Ask the educator what they noticed about their data; exercise wait time, allowing for the teacher to respond.
  • Use data to work with the educators to identify instructional patterns, differentiating between areas of strength and areas of growth.
  • Invite the educator to reflect on what is working well in their areas of strength. Some examples of this may be: the educator allows for more partner work, builds in more formative assessment, works with the after-school program to reinforce certain areas, etc.
  • Invite the educator to reflect on what is not working well in areas of growth.
  • Develop an intervention plan with the other educator in order to address areas of growth.
  • Work with the other educator to determine an assessment strategy for testing the efficacy of their intervention plan.
  • Plan a follow-up time to look at the data again after an assessment.

Potential challenges in using data to inform practice and possible solutions

  • Some data sets are inaccurate or incomplete. If this is the case, make sure unenrolled students are removed, students included have been regularly attending class, and data is accessible in a timely manner.
  • Some data sets have not been disaggregated. Reviewing the data and looking at different variables can help reduce teacher assumptions about student performance.
  • It can be powerful to look at data through the lens of specific questions, and gaining more perspectives can provide objective feedback.
  • Some educators may not be motivated to use data. Soliciting support and buy-in from grade level and/or content area teams may help motivate educators.

*For more information, go to:

Research & Resources

Supporting Research

  • Mandinach, E. B. (2012). A Perfect Time for Data use: Using Data-Driven Decision Making to Inform Practice. Educational Psychologist, 47(2), 71-85. Chicago

Data-driven decision-making has become an essential component of educational practice across all levels, from chief state school officers to classroom teachers, and has received unprecedented attention in terms of policy and financial support. It was included as one of the four pillars in the American Recovery and Reinvestment Act (2009), indicating that federal education officials seek to ensure that data and evidence are used to inform policy and practice. This article describes the emergence of data-driven decision-making as a topic of interest, some of the challenges to and opportunities for data use, and how the principles of educational psychology can and must be used to inform how educators are using data and the examination of its impact on educational practice.

  • Halverson, R., Grigg, J., Prichett, R., & Thomas, C. (2007). The New Instructional Leadership: Creating Data-Driven Instructional Systems in Schools. Journal of School Leadership, 17(2), 159

This article considers how local school leaders build data-driven instructional systems to systematically improve student learning. Such systems are presented as a framework involving data acquisition, data reflection, program alignment and integration, program design, formative feedback, and test preparation. This article reviews data collected in a yearlong study of four schools to describe how leaders structure opportunities to engage in data-driven decision-making.

  • Lachat, M. A., & Smith, S. (2005). Practices That Support Data Use in Urban High Schools. Journal of Education for Students Placed at Risk, 10(3), 333-349.

This article presents initial findings of a case study focusing on data use in five low-performing urban high schools undergoing comprehensive school-wide reform. Study findings point to several key factors that have an impact on data use in the study sites: the quality and accuracy of available data, staff access to timely data, the capacity for data disaggregation, the collaborative use of data organized around a clear set of questions, and leadership structures that support school-wide use of data. The findings build on current literature and also contribute new knowledge of the key roles played by a data team and a data coach in fostering effective data use in high school reform.


Submission Requirements

Submission Guidelines & Evaluation Criteria

To earn this micro-credential, you must receive a passing evaluation for Parts 1 and 3 and a “Yes” for Part 2.

Part 1. Overview question

(300-word limit)

  • Please describe a meeting with another educator and interventions where you were able to use data to plan for interventions that improved the other educator’s instruction. How did you leverage data to prepare for your conversation with another educator?

Part 2. Work examples/artifacts

Please submit several artifacts that were created while meeting with another educator or educators to plan instruction based on data (such as links to writing, audio, images, video, or other products) including such items as:

  • Meeting agenda with next steps
  • A revised unit plan based on data
  • Video clip of the meeting
  • Photos of the meeting

Part 3. Reflection

(300-word limit):

Provide a reflection on what you learned using the following questions as guidance:

  • How has using data to inform another educator’s instruction impacted that educator’s students?
  • How might you improve your ability to improve another educator’s practice through data-driven interventions in the future?

Except where otherwise noted, this work is licensed under:
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)


Download to access the requirements and scoring guide for this micro-credential.
Requirements for Data-Driven Interventions
How to prepare for and earn this micro-credential - in a downloadable PDF document

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