Get Full Government Meeting Transcripts, Videos, & Alerts Forever!
Get email alerts on the Data Practices topic
No spam. Unsubscribe anytime.
PPS outlines 'step back' data reviews to inform school improvement and targets
Summary
District staff described recent 'step back' meetings where school leadership teams reviewed fall baseline assessments and culture/climate indicators, using multiple data sources (attendance dashboards, behavior referrals and state reporting) to set school improvement goals and plan next steps.
Get email alerts on the Data Practices topic
No spam. Unsubscribe anytime.
Haley updated the committee on a district practice called 'step backs' in which school leadership teams and district staff come together to review fall baseline data and reflect on progress toward school improvement goals.
She said the step‑back meetings took place in late October and early November, typically ran about 90 minutes, and included school leadership teams alongside central office staff to problem‑solve in real time. Schools examined a range of measures — fall baseline assessments for grades 3–8 and grade 10, unit assessments and course grades, attendance dashboards in PowerSchool, behavior referral data in SWIS, and state‑required behavior reporting in Infinite Campus.
Haley said the goal of step backs is to make data actionable: use it to set goals, monitor progress and adjust practice during the year rather than waiting for year‑end results. She described plans for a next round of step backs in February and said staff will continue to support schools in translating leadership learning back to teacher teams. Committee members asked whether step backs reach the teacher level and whether professional development on data interpretation is included; staff said step backs are leadership‑level reviews intended to inform teacher practice through school teams and highlighted plans to strengthen distributed leadership and PD where needed.
Committee members praised the district’s emphasis on data culture but urged attention to bias, particularly around multilingual learner identification and interpretation of behavioral data. Staff said they will continue to refine equitable data practices and provide professional development.

