York City SD rolls out instructional —networks' to boost classroom coaching, teacher retention
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Summary
District leaders presented a new networked coaching model (K–12 and specialty networks) focused on in‑school coaching, data huddles and talent management to improve instruction and address chronic underperformance, with early signals on observation metrics.
District leaders presented the York City School District's instructional networks model to the board, describing a multi‑network approach (Dream It Forward, Opportunity & Access, Schools of Innovation) that places coaches in buildings to support teachers, target instructional gaps and improve student outcomes.
The lead presenter framed the networks as a shift "from compliance to meaningful support that empowers school success" and grounded the model in the instructional core (learner‑teacher‑content). Coaches are expected to spend 80% of their time in classrooms and 20% convening data huddles to triage instructional and operational issues.
Network coaches introduced themselves and their priorities. Ebony Harmon (talent management coach) said the district redesigned its new‑teacher induction week and created a mentorship toolbox to give mentors and mentees checklists and resources. Adam Landis, the district math coach, described plans to analyze assessment data, observe math classrooms and deliver targeted PD. Jermaine Bailey (special populations lead) described work to support students with special needs and to help future educators schedule certification exams.
High‑school network staff described work to align a new ELA curriculum, run professional development rounds with the Intermediate Unit and use data to identify students below grade level for targeted intervention. K–8 coaches outlined literacy efforts that include fine‑grained data use (e.g., nonsense‑word decoding to isolate vowel issues) and small‑group planning to accelerate early reading.
Board members pressed for specifics on how quickly instructional gains should appear. Presenters pointed to early improvements flagged in formal observation components (example: modest growth in Danielson component 3b) and to operational indicators such as a reported drop in out‑of‑school suspensions in October at the high school. Presenters emphasized that some outcomes require sustained effort and that the district will share disaggregated data over time.
Board members asked about gaps the networks need to fill and requested future updates linking network activity to classroom‑level student outcomes. Presenters said continued budget and community support would be needed to scale the model.