Activity 3: Data for Change

Activity Guide

Schools and districts can encourage effective and consistent data use by investing in leadership and professional development as well as through structured time for collaboration. Because local school achievement data must inform and support all cultural, programmatic, and instructional decisions made in a school, school leaders must establish this culture.

Use the following protocols to drive conversations about data and school culture.

Principals or facilitators will guide participants through the data-driven dialogue process twice: first with data unrelated to the school and then with school or district data. For this first dialogue exercise, provide participants with a set of data that is not “personal” to your school.

Suggestions: AP Report to the Nation, February 8, 2012

  • Promoting Equity. p. 16, Fig. 5: Demographics of the Class of 2011 versus AP Exam-takers.
  • Fulfilling AP Potential. p. 17, Fig. 6: Participation in Recommended AP Subjects by Race/Ethnicity of Graduates with AP Potential.
  • Fulfilling AP Potential. pp. 18–19, Fig. 7: AP Equity and Excellence for Underserved Students (Black/ African American, Hispanic, Latino, American Indian/Alaska Native).
  • A Closer Look AP Students: Low Income. p. 20, Fig. 8a: Demographics of Low-income AP Exam-takers from the Class of 2011.
  • A Closer Look at AP Students-Black/African American. pp. 21–23, Fig. 8b, 8c, 8d: Top Five Most Popular AP Exams Taken by Black/African American, Hispanic/Latino, American Indian/Alaska Native Graduates.

Conduct a Data-Driven Dialogue

This process asks participants to suspend judgment and builds awareness and understanding of their viewpoints, beliefs, and assumptions about data. Throughout the process, all participants have an equal voice. The three phases of the process help groups make shared meaning of data. Because the emphasis is on data-based facts, this dialogue tool should help participants replace hunches and feelings, examine patterns and trends of performance indicators, and generate “root-cause” discussions that move from identifying symptoms to finding possible causes of student performance. This process also helps faculty members develop a common “data vocabulary.”

Phase 1—Predictions: This phase surfaces perspectives, beliefs, assumptions, predictions, questions, and expectations.

Before providing teachers with data, ask them to reflect privately and record preliminary thoughts about the data. The following thought-starters may help:

  • I assume…
  • I predict…
  • I wonder…
  • My questions/expectations are influenced by…
  • Some possibilities for learning that this data may present include…

Example: I expect that boys scored lower than girls in reading but higher in mathematics.

Phase 2—Observations: Participants engage with the actual data and make note of only the facts that can be observed in the data. Conjectures, explanations, conclusions, and inferences are off-limits. Ask teachers to make statements about quantities (e.g., more than 50% of male students) or the presence of specific information (e.g., the percentage of African American students performing at or above grade level in math improved by 7%).

Ask participants to study the provided data privately and record their observations. Reminder: Just the facts.

  • I observe that…
  • Some patterns/trends I notice are…
  • I can count…
  • I’m surprised to see…

Example: I see that twice as many boys as girls exceeded expectations on the eighth-grade science assessment.

Phase 3—Inferences: Participants a) generate multiple explanations for their Phase 2 observations, b) identify additional data that may be needed to confirm/contradict their explanations, c) propose solutions/responses, and d) identify data needed to monitor implementation of their solutions/responses.

Before beginning the Phase 3-Inferences dialogue with colleagues, teachers should reflect privately, using one or more of the following prompts to engage their thinking:

  • I believe the data suggests…because…
  • Additional data that would help me verify or support my explanations are…
  • I think the following are appropriate solutions/actions that address the needs implied in the data…
  • Additional data that would help guide implementation of the solutions/actions and determine if they are working…

Example: I believe the data suggest that more instructional time should be allotted to mathematics instruction, because the addition of more instructional time for reading appears to have resulted in improved student outcomes in reading.

Extend and Apply

To use this tool effectively, and in a manner specific to your school’s improvement work, provide participants with grade-level, school, or district data reports such as school attendance data, discipline data, pass/fail data for a particular grade level or test score data by gender or other subgroup.

Using the school or district data you have provided, repeat the steps for each phase of the guided-data dialogue below. Remind participants that this time, their inferences will be the foundations used to make changes in instructional practice, program implementation (or elimination), policy changes, professional development acquisition, etc.

Phase 1—Predictions: This phase surfaces perspectives, beliefs, assumptions, predictions, questions, and expectations.

Before providing teachers with data, ask them to reflect privately and record preliminary thoughts about the data. The following thought-starters may help:

  • I assume…
  • I predict…
  • I wonder…
  • My questions/expectations are influenced by…
  • Some possibilities for learning that this data may present include…

Example: I expect that the Marshall Middle School boys scored lower than girls in reading but higher in mathematics.

Phase 2—Observations: Participants engage with the actual data and make note of only the facts that can be observed in the data. Conjectures, explanations, conclusions, and inferences are off-limits. Ask teachers to make statements about quantities (e.g., more than 50% of male students) or the presence of specific information (e.g., the percentage of African American students performing at or above grade level in math improved by 7%).

Ask participants to study the provided data privately and record their observations. Reminder: Just the facts.

  • I observe that…
  • Some patterns/trends I notice include…
  • I can count…
  • I’m surprised to see that…

Example: I see that twice as many Marshall Middle School boys as girls exceeded expectations on the eighth-grade science assessment.

Phase 3—Inferences: Participants a) generate multiple explanations for their Phase 2 Observations, b) identify additional data that may be needed to confirm/contradict their explanations, c) propose solutions/responses, and d) identify data needed to monitor implantation of their solutions/responses.

Before beginning the Phase 3-Inferences dialogue with colleagues, teachers should reflect privately, using one or more of the following prompts to spur their thinking:

  • I believe the data suggests…because…
  • Additional data that would help me verify or explain my explanations are…
  • I think the following are appropriate solutions/actions that address the needs implied in the data…
  • Additional data that would help guide implementation of the solutions/actions and determine if they are working include…

Example: I believe the data suggest that more instructional time should be allotted to mathematics instruction, because the addition of more instructional time for reading appears to have resulted in improved student outcomes in reading.