1. NYC Teacher Data Initiative: An introduction for Teachers ESO Focus on Professional Development December 2008

2. 2 Session Objectives Become familiar with the new Teacher Data Reports Consider ways to incorporate this new tool into school-wide professional development Locate support resources

3. 3 Teachers and principals use an array of instruments to reflect on practice and determine developmental needs Classroom observations Lesson plans Participation in professional development Quality of student work products Student performance on state assessments No one measure gives us the full story, but the various pieces come together to create a more reliable picture Areas of convergence and dissonance in our observations are equally useful

4. 4 Teacher Data Initiative (TDI) provides a new lens to reflect on practice and determine developmental needs Purpose: To contribute another lens through which to look at teacher contributions to student learning Rationale: Teachers make a big difference, and value-added data provides a lens to focus on what teachers bring to students rather than what students bring to the classroom Framing Question: How might the TDI data tool fit into existing school plans for instructional improvement and professional development? TDI should not be viewed as a silver bullet, big initiative, or accountability metric that will be forced upon schools. Rather it is a new tool available to principals to incorporate into their larger instructional and professional development plans.

5. Teacher Data reports measure teacher contributions to student growth as measured by test scores Test scores are an incomplete but important measure of student growth The work of the teacher is a major factor in how well students score on tests We know that other factors are also in play (e.g., prior test scores, family income) Rather than looking at absolute scores, teachers & principals often estimate the weight of factors that are beyond teacher control Teacher Data reports help eliminate this guess work by mathematically accounting for most of the measurable factors that might also contribute to student test scores

6. 6 TDI mathematically factors in measurable characteristics to predict student scores Student characteristicsClassroom characteristicsSchool characteristics Prior year reading Prior year math Free or reduced price lunch Special education status English Language Learner status Number of suspensions and absences (prior-year) Student retained in grade Attended summer school New to school Race Gender Prior year teacher Average prior year reading and math Percent free or reduced price lunch Percent special education status Percent English Language Learner status Average number of suspensions and absences (prior) Percent of students retained in grade Percent attended summer school Class size Percent by race Percent by gender Average classroom characteristics Average class size Total tested by grade/subject Year starting and ending school Teacher characteristics (used when comparing teachers to peer teachers) Years of experience Years teaching in the same grade and subject

7. 7 TVI draws on 10 years of city-wide data (test scores, student, teacher, and school characteristics) to predict individual student gains The predicted gains are compared to the actual gains for each student to determine the teachers contributions or the value added The teachers contribution for each student is averaged, and then compared to other 4-8 ELA and Math teachers or rank ordered top 20%, middle 60%, and bottom 30%. How Teacher Data Works Predicted Score Mathematically isolates factors beyond teacher control e.g. prior year test scores Teacher Contribution Factors within teacher control e.g. quality of instruction & high expectations Actual Test Score Student scores on ELA & Math tests

8. 8 How TDI Works: The Model Value added for one student Proficiency rating 3 rd Grade 4 th Grade 3 - - 2- Predicted Gain Actual Value Added Baseline (Previous Years) Score Teacher A Teacher B Teacher E Teacher D Teacher C Least Gain Most Gain The value added is the difference between the predicted and actual scores Value added is averaged for all students in a class The value added is measured in proficiencies TDI orders teacher from least to most gain to determine a percentile rank

9. 9 Individual teacher reports will also be available for principal use and to share with teachers Similar to the Progress Reports, TDI compares teachers to the following groups: 1. All teachers on the same grade level, City-wide 2. Peer teachers (similarly situated in terms of teacher experience, and student, school, and classroom characteristics) 0%25%50%75% 2007-08 66%95% Last 3 years 69%92% 100% My percentile (0%-100%) 79% Range* 81% My percentile 0%25%50%75% 2007-08 55%85% Last 3 years 58%78% 100% My percentile (0%-100%) 68% Range* 70% My percentile

10. Teachers should analyze their report to look for trends and consider key questions Identify a trend or something that you find interesting in the Teacher Data Report List some questions that this prompts List some sources where you might seek additional information List some actions that might be taken based on your findings

11. Example: Think through a specific trend Teacher report reveals high scores on everything except for ELL students What is being taught? Might ELL students require additional instruction Analyze test items for trends in ELL responses How is it taught? Is teacher differentiating instruction? Request peer observations Consult with others who have high ELL score Maps will probably not be this linear, but here is a sample to prompt thinking Consider discussing your map with a mentor teacher, your principal, or other person that you trust to help The Teacher data report should be only a starting point to prompt further research and action

12. 12 Teacher can access additional support Principals and chapter leaders are prepared to help teachers understand and use their reports (they will not share reports with other teachers) Visit the Teacher Resource page