School Level Data Dive
description
Transcript of School Level Data Dive
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School Level Data DiveLEADING WITH DATA
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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
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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
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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
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Indicators of College Readinessscore conversion chart
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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
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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
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Beginning with School-Level Data
Analyzing/Interpreting Data
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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
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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?
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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?
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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?
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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
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Analyzing/Interpreting Data
Diving into Student-Level
Data
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From School- to Student-Level Data
School-level data are your back story/context
Student-level data are your actionable data
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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
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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?
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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)
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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?
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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.
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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
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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
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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