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School Level Data Dive. LEADING WITH DATA. 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. - PowerPoint PPT Presentation
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School Level Data DiveLEADING WITH DATA
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
Driving Continuous Improvement with Data
Understanding research-based
trends
Analyzing school data
Interpreting the data
Connecting the data to rigorous
instruction
Monitoring progress
toward college readiness
Refining actions
and plans
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
Indicators of College Readinessscore conversion chart
Gr. 5 Above (CC6)
Gr. 6 CC 7
Gr. 7CC 8
Gr.8Algebra 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. 7CC 7
Gr.8CC 8
Gr. 9Algebra 1
Gr. 10 Geometry
Gr. 11 Algebra 2
Gr. 12 AP Statistics
Across levels, we are looking to build an academic profile of students who meet our
Preliminary Performance Benchmarks and using this information to increase
rigor for all students.
Making Connections
Beginning with School-Level Data
Analyzing/Interpreting Data
OPEN EXPLORATION:Orienting to the School-Level Data
Description of Dataset–At least 3 years of data–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
Activity 15 minutes (share at your tables)1. Pick a tab/sheet.2. What trends do you notice?3. How are these trends changing?4. Or not changing?5. What surprised you?6. What might you want to explore further?7. What other data would be helpful?
Analyzing/Interpreting School-Level Data
We know: HCPSS students who participated in an advanced math course by G8 (Algebra 1) are 3 times more likely than their peers who did not participate in Algebra 1 to be college ready.
Question: How can understanding the academic profile of these students who meet this Benchmark help us increase access to rigor for all students starting in elementary school?
Analyzing/Interpreting School-Level Data
Exercise 1:
1. Go to GT Participation tab2. Filter for “All Students” in Student Group3. Filter for Grade “5”
Q1) What number and percentage of your Grade 5 students are participating in G/T Math?
Q2) What is the trend across time?
Analyzing/Interpreting School-Level Data
Exercise 2:1. Clear the Student Group filter.2. Filter for Year “2014” + Grade “5.” Look at your
data by Student Group.Q3) What do you notice about each student
group’s G/T Math participation in SY 2014?Q4) Select Year “2013.” Do you see similar trends
across groups over time? Repeat for another year.
Be prepared to share out
Analyzing/Interpreting Data
Diving into Student-Level
Data
From School- to Student-Level Data
School-level data are your back story/context
Student-level data are your actionable data
OPEN EXPLORATION:Orienting to the Student-Level Data
Description of the Dataset– SY 2014 Grade 5 students– Current and prior years’ data– Each line represents one student
Dataset Codebook– Student-Level Data Reference Guide
Activity 5 minutes– Explore the variables– Use your filters, freeze top row
Analyzing/Interpreting Student-Level Data
We know: 1) Based on research, Algebra 1 by G8 is linked to
college readiness and college enrollment2) Based on HCPSS math course progression, student
needs to be in above-grade level or G/T Math by G5 to be able to take Algebra 1 by G8
Question: How can understanding the academic profile of the students who meet this PPB help us increase access to rigor for all students starting in elementary school?
Analyzing/Interpreting Student-Level Data
Exercise 1: Filter for students who are in Above Grade Level Math (GRD5_MATH_INSTR_LEVEL_Q2: “Above”)
1. How many students do you have? 2. Describe their academic profile.
a. Performance-based factors (e.g., CogAT, MAP, MSA, SCAT, report card grades)
Analyzing/Interpreting Student-Level Data
Exercise 2: Clear the “Above” filter; filter for “On” Grade Level(GRD5_MATH_INSTR_LEVEL_Q2: “On”)1. Do you have students who have a similar
academic profile that you have just identified but are not in “Above” Math?
Analyzing/Interpreting Student-Level Data
Question: How do we support struggling students with different needs (e.g., On or Above Math but poor grades; Below Grade Level Math)?
Exercise 3: Students in AGL Math but poor gradesFilter for students who are in Above-grade-level Math (GRD5_MATH_INSTR_LEVEL_Q2 “Above”) andHave a report card grade lower than B (REPORT_CARD_MATH_Q2 “B”)
1. How many students do you have? 2. Describe their academic profile.
Exercise 4: Performing well in current Math, not in AGL Math• Filter for students who were “Advanced” on MSA Math
in Grade 4 (GRD4_MSA_MATH_LEVEL) and • Are being instructed “On” Grade Level Math
(GRD5_MATH_INSTR_LEVEL_Q2) and• Add a filter for students who earned an “A” in Math in
Quarter 2 (REPORT_CARD_MATH_Q2). Q1) What is their academic profile? Q2) What contributed to their current math instructional level
placement?
Analyzing/Interpreting Student-Level Data
Exercise 5: Below Grade Level MathFilter for students who are being instructed “Below” Grade Level Math (GRD5_MATH_INSTR_LEVEL_Q2)Q1) What is their academic profile like? Historical
data?Q2) What might rigorous instruction look like for
these students?
Analyzing/Interpreting Student-Level Data
TABLE TALKHow 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