What do we mean by Data Review?
Data Review is the process of collecting data, reflecting on it, and distilling it into actionable insights. This process is how you can turn data into knowledge and knowledge into action. Data Review requires going "below the surface" to find root causes for your results (both positive and negative) and planning actionable changes to continue improving your program.
Why should you set aside intentional time for routine Data Review?
Creating a regular routine for Data Review helps your program institutionalize a focus on learning and improvement. Data is the fuel that drives improvement; review is the engine that converts it into action. As with any engine, consistent and repeated cycles are what keep you moving forward. Regular cycles of Data Review help your program maintain consistent progress toward its goals, create a program-wide culture of iterative improvement and learning, and hold itself accountable for making a positive impact on all students. Once you have a clear Performance Measurement Plan with dates for regular data collection, set aside regular and consistent time to review that data at all levels of the organization.
What should you consider when planning data routines?
For each dataset you collect for your Performance Measurement Plan, outline the following:
- Who is responsible for collecting this data? When and how will they collect it?
- Who is responsible for reviewing this data? When and how will they review it and distill actionable insights?
- Who is responsible for acting on the insights distilled from the Data Review?
- Who is responsible for supporting those who are acting on the data, and what form will this support take?
- Who needs to be informed about the data, insights, and actions? Who will do the informing, and by when?
Example: Delineating Data Review Responsibilities
This example is not exhaustive, but provides a brief reference for programs looking to build their Data Review protocols.
| Type of Data | Who reviews this data? | When will they review this data? | How will this data be used? | Who will be informed? |
|---|---|---|---|---|
| Baseline Data Assessments |
|
Within one week of administration | To determine who will be eligible for tutoring | School Administrators |
| Session Assessments |
|
At the end of each tutoring session | To determine student mastery of session content and tailor subsequent tutoring sessions |
|
| Quarterly Surveys from Parents, Student, Teachers and Stakeholders |
|
Within one week of survey closing | To incorporate feedback and improve sessions/collaboration with stakeholders |
|
| End of Program Student Assessments and Survey Data from All Stakeholders |
|
Within one week of assessments | To evaluate achievement of program goals |
|
Example: Program-Wide Data Review Routine (Calendar)
Below is an example calendar of Data Review for a tutoring program that collects quarterly data. Note that the sequencing is not arbitrary, but intentional. In week one, the data is collected, with the program purposefully scheduling students’ quarterly academic assessments and all stakeholders’ satisfaction surveys for the same week. In week two, individual school site teams meet to reflect on their own data, set next steps, and communicate them to stakeholders. In week three, the central program staff repeat that same process at the next level up. If this program scaled up significantly, another week of review could easily be added for another layer of program staff; the routine is self-similar at all levels.
| Monthly Review Calendar | |||||
|---|---|---|---|---|---|
| Monday | Tuesday | Wednesday | Thursday | Friday | |
| Week 1 | Students take quarterly assessments and all stakeholders (students, parents, teachers, school administrators) complete surveys | ||||
| Week 2 | Data Analysis is rolled up for each school site | Data Reflection Meeting: School Site Team (Tutor, Teacher, Site Administrator) reviews the data and delineates next steps | Summary of Data and Next Steps are communicated to Students, Parents, School Administrators at the School Site Goal Setting with Students and Families | ||
| Week 3 | Program-Wide Data Analysis is rolled up including data disaggregated by demographics | Program Wide Data is Reviewed at the Organizational Level including Tutor Data and next steps are delineated | Summary of Data and Next Steps are communicated to Students, Parents, School Administrators at the School Site and to Organizational Stakeholders (Leadership Team, etc) | ||
Examples: Tutors’ Data Review Routines (Overviews)
Routine review of student data enables tutors to target and customize sessions to meet specific students’ individual needs. Tutors should regularly review other forms of feedback as well; however, the kinds of additional information tutors consider will vary depending on Program Type.
| Example for a School-Based Tutoring Program with Paraprofessionals | Tutors at the partner school site meet collectively with a school administrator and the program’s Site Director to analyze a weekly roll-up of student data and plan for tutoring sessions for the following week. |
|---|---|
| Example for a School-Based Tutoring Program with Volunteers | Some programs that rely on volunteers opt to focus on building volunteer skills in instruction and shift responsibility for Data Review to either teachers or program site staff. The teachers/program staff analyze student data, determine action steps, and then explain to the volunteers exactly what skills they should target with specific students. |
| Example for a Virtual Tutoring Program | Virtual Tutoring Programs contract with specialists or develop their own internal capabilities for digital capture and automated analysis of student data through online platforms. These platforms are able to provide direct feedback to tutors regarding exactly what a tutor should focus on for each session. |