NYSTCE School Building Leader (109/110)

This chapter covers Part One, Competency 0001, indicators 2, 5 and 10: collecting and analyzing data to set goals and plan improvement, using assessment and other data collaboratively to find strengths and needs while recognizing systemic bias, and responding to data trends while advocating for each student. It is the core content behind the extended data-analysis performance task.

Educational researcher Victoria Bernhardt describes four broad measures of school data. Each answers a different question, and the richest insight comes from looking at where they intersect.

Process data are the most often overlooked and the most actionable, because they describe what adults control. If Grade 8 math results are weak, the data on who is placed in which course, how much time each group receives, and what walkthroughs show about instruction will usually point to the root cause more directly than another achievement report.

Disaggregation means breaking results down by student group: race and ethnicity, economic disadvantage, multilingual learner status, disability status, gender, homelessness, and other relevant categories. An overall average can hide a large gap. A school where 70 percent of all students are proficient may have a group at 35 percent.

Indicator 5 asks leaders to recognize systemic bias in data. This includes:

• Measures that are biased in design, such as a reading screener normed on a population unlike the school's or a test in English given to a newly arrived student.
• Patterns that reveal biased systems, such as gifted identification based on teacher nomination alone or discipline referrals concentrated on one group for subjective offenses like defiance.
• Missing data, such as surveys offered only in English or only online.
• Small group sizes that make percentages unstable.

Common Trap: Treating a gap as evidence about students rather than about systems. A strong leader asks what in the school's practices, access and supports produces the gap, not what is wrong with the group.

State results arrive too late to change instruction for the same students during the year, so they are most useful for identifying patterns and setting annual goals. Interim and common formative data drive the adjustments in between.

Exam Tip: If a question asks which data a team should use to adjust instruction for students this month, choose interim or common formative data. If it asks how to evaluate a program's annual impact, state and multi-year data are appropriate.

Triangulation means confirming a finding with at least three sources of different types before acting on it. If interim math scores dropped, a leader checks walkthrough evidence, student work samples, attendance during math blocks, and perhaps student survey responses about math class. Triangulation protects against acting on a single noisy measure.

A root cause is a cause within the school's control that, if addressed, would prevent the problem from recurring. Common tools include the "five whys," fishbone diagrams, and a protocol that sorts causes into those adults can and cannot control. Poverty or home language are not root causes in this sense; how the school schedules intervention, assigns teachers, or scaffolds instruction can be.

Memory Aid: Symptoms show up in student results; root causes live in adult practice and school systems.

Once a priority need is clear, the team writes a goal that is Specific, Measurable, Achievable, Relevant, and Time-bound. A useful goal also names the student group and the measure.

Effective plans pair an outcome goal with process goals (for example, "all Grade 4 teachers implement daily structured academic conversation routines, verified through monthly walkthroughs"), because process goals can be monitored long before outcome data arrive.

Most school improvement models follow a cycle in which collaborative teams:

1. Collect and organize relevant data.
2. Analyze strengths, needs and patterns, including disaggregated results.
3. Identify a priority need and investigate root causes.
4. Set SMART goals.
5. Select research-based strategies and plan actions, owners, timelines and resources.
6. Implement and monitor with interim and process data.
7. Evaluate results and revise.

New York schools identified for support under the state's ESSA accountability system develop a School Comprehensive Education Plan (SCEP), built with stakeholders and grounded in a needs assessment. Even schools not identified for support typically maintain an improvement plan. The exam focuses less on the document's format than on the process: data review, stakeholder involvement, measurable goals, research-based strategies, and monitoring.

Worked Example: Lakeview Middle School's data team reviews Grade 7 math results.

Walkthrough notes show that math lessons rely heavily on teacher explanation and word problems, with few visuals or vocabulary supports. Multilingual learners receive stand-alone ENL during part of the math intervention period.

1. Identify the strength: students with disabilities, while low in proficiency, show interim growth (+10) slightly exceeding the overall rate (+9), suggesting that co-teaching supports are working.
2. Identify needs: overall proficiency is low (41 percent); multilingual learners have both the lowest proficiency (12 percent) and almost no growth (+2); economically disadvantaged students trail the overall rate.
3. Select the priority: multilingual learners, because both status and growth are weakest, and the process data suggest a cause the school controls.
4. Triangulate: walkthroughs show language-heavy instruction without scaffolds, and the schedule removes these students from intervention time.
5. Frame key questions: How are language demands in math lessons supported? Why does the schedule place stand-alone ENL during math intervention?
6. Plan actions: adjust the schedule so multilingual learners receive both ENL and math intervention; provide professional learning on math language scaffolds; monitor with common formative assessments.

Result: The team sets a SMART goal for multilingual learner growth on the spring interim, changes the schedule conflict, and studies the co-teaching practices that produced growth for students with disabilities to extend them to other classes.

The extended performance task follows this cycle almost exactly: identify a significant strength and three needs, choose the priority need and justify it with data, pose two key questions, explain how you would answer them (which additional data and from whom), describe actions, explain how to leverage the strength, and anticipate challenges. Cite specific numbers and student groups; general statements such as "scores are low" earn limited credit.

• Use all four data types; process data often reveal the most actionable causes.
• Disaggregate results and look for systemic bias in measures, access and practices.
• Match the data to the decision: state results for annual patterns, interim and formative data for adjusting instruction.
• Triangulate and find root causes within adult control before choosing strategies.
• Write SMART goals that name the group and the measure, and pair them with process goals.