1. School Level Data Dive LEADING WITH DATA

2. School Level Data Dive Outcomes Reflect on the power of leading with data to drive rigorous instruction for all students Explore research on Howard County student outcomes that supports a pathway to college and career readiness Deepen a skill set for analyzing data to drive rigorous instruction

3. Driving Continuous Improvement with Data Understanding Research-Based Trends Analyzing School Level Data Interpreting the Data Connecting Data to Rigorous Instruction Monitoring Progress Toward College Readiness Refining Actions and Plans

4. Understanding Research-based Trends Students who achieved a PSAT ≥ 145 in Grade 10 were 13 times more likely to be college ready and enroll in college immediately following high school HCPSS students who participated in an advanced math course by Grade 8 (Algebra 1) are 3 times more likely than their peers who did not participate in Algebra 1 to be college ready

5. Gr. 5 Above (CC6) Gr. 6 CC 7 Gr. 7 CC 8 Gr.8 Algebra 1 Gr. 9 Geometry Gr. 10 Algebra 2 Gr. 11 Pre-Calculus Gr. 12 AP Calculus AB Making Connections: Math Progression Gr. 5 CC 5 Gr. 6 CC 6 Gr. 7 CC 7 Gr.8 CC 8 Gr. 9 Algebra 1 Gr. 10 Geometry Gr. 11 Algebra 2 Gr. 12 AP Statistics

6. Making Connections Across levels, we are looking to build a profile of students who meet our Preliminary Performance Benchmarks and using this information to increase rigor for all students

7. Analyzing/Interpreting Data Beginning with School-Level Data

8. Description of Dataset – At least 3 years of data for GT placement and MSA performance – Previous SY data for HSA first-time test-taker performance – Previous SY data and Fall 2013/14 MAP scores – Data reported by student group – Note the sheets/tabs at the bottom Dataset codebook: School-Level Data Reference Guide OPEN EXPLORATION: Orienting to the School-Level Data

9. Activity 5 minutes (be prepared to share) 1.What trends do you notice? 2.How are these trends changing? 3.Or not changing? 4.What surprised you? 5.What might you want to explore further? 6.What other data would be helpful? OPEN EXPLORATION: Orienting to the School-Level Data

10. We know: Students who took advanced mathematics (Algebra 1) by G8 were more likely to be college and career ready and enroll in college immediately following graduation. Question: How can understanding the profile of the students who meet this Benchmark help us increase access to rigor for all students? Analyzing/Interpreting School-Level Data

11. 1.Select GT tab 2.Filter  Student Group = All Students  Grade = 8  Content = Math Q1) What number and % of G8 students are participating in GT math? Q2) What is this trend across time? Analyzing/Interpreting School-Level Data

12. 3.Clear All Students filter on Student Group 4.Filter  Year = 2014  Compare/analyze data by Student Groups Q3) What do you notice about each student group’s GT math participation in SY 2014? Q4) Select Year 2013. Do you see similar trends across groups over time? Analyzing/Interpreting School-Level Data

13. Analyzing/Interpreting Data Diving into Student Data

14. School-Level data are your back story/context Student-Level data are your actionable data From School- to Student-Level Data

15. Description of Dataset SY 2014 Grade 8 students Current and prior years’ data Each line represents one student Dataset Codebook: Student-Level Reference Guide Activity (5 minutes) Explore the variables Use your filters OPEN EXPLORATION: Orienting to the Student-Level Data

16. We know: Students who took advanced mathematics (Algebra 1) by G8 were more likely to be college and career ready and enroll in college immediately following graduation. Question: How can understanding the profile of the students who meet this Benchmark help us increase access to rigor for all students? Analyzing/Interpreting Student Data

17. Exercise 1 Filter for students who were enrolled in Pre-Algebra in G7, received a grade of A or B in this course, and DID NOT take Algebra 1 by G8. – Do you have any students? Should you? Analyzing/Interpreting Student Data How can understanding the academic profile of students who were on target to meet this Benchmark, but DID NOT help us increase access to rigor for similar students starting in G6?

18. Exercise 2 Filter for students who took Algebra 1 by G7. – Describe them: performance based factors – What should they continue to take in HS to reach rigorous math courses by Grade 12? – Does it appear as though they will continue to follow anticipated sequence considering performance? Analyzing/Interpreting Student-Level Data How can understanding the academic profile of students who DID meet the identified Benchmark help us increase access to rigor for these and similar students?

19. Exercise 3 Filter for students who DID NOT take Algebra 1 by G8 AND had a final grade of A or B in G7 Math course – Describe them: performance based factors – What Math class are they recommended to take in G9? – Where do these students fall on the path to take an advanced Math course? – What are potential on ramps for these students related to eventual advanced math enrollment? Analyzing/Interpreting Student-Level Data How can understanding the profile of students who DID NOT meet the identified Benchmark, but are performing well in their courses help us increase access to rigor for these and similar students?

20. Exercise 4 Filter for students who received a final letter grade of C, D or E in Math in G7 and DID NOT take Algebra 1 by G8 ‒Describe them: performance based factors ‒What additional data would you want for these students? ‒What Math courses did they take in G6 and G7? ‒What is the highest Math course they could take by G11? Analyzing/Interpreting Student-Level Data How can understanding the profile of struggling students who DID NOT meet the identified Benchmark, help us increase access to rigor for these and similar students?

21. Exercise 4a Now, filter for students who received a letter grade of C, D, or E in Math in G7, but are being recommended to take Algebra II or Geometry in G9. ‒ Describe them: other performance based factors ‒ What additional data would you want for these students? Analyzing/Interpreting Student-Level Data How can understanding the profile of students who DID meet the identified Benchmark, but are struggling in their courses help us increase access to rigor for these and similar students?

22. TABLE TALK How will you use this data exercise, the Preliminary Performance Benchmarks, and the resources to support college readiness to lead with data? Connecting the Data to Rigorous Instruction