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Using Data to Drive the Work of Professional Learning Communities
Dan Kuzma Dean of Instruction
Chicago Bulls College Prep
Agenda: 1) Link Backward Design, Professional Learning Communities (PLCs), and Data-Based Decision-Making
2) Crash Course in PLCs
3) Case Study: Using PLCs as a lever for student achievement
Backward Design?
Professional Learning Communities?
Data-Based Decision-Making?
Word Jargon?
Potential Framing Device (1)
Backward Design
1) Specify instructional target(s) / desired results.
2) Determine acceptable evidence for learning
acquisition. 3) Plan learning experience
and instruction.
Data-Based Decision-Making
1) Establish course goals and assessments.
2) Collect and analyze simple, purposeful data.
3) Improve student learning outcomes horizontally
and vertically.
Summary:
Plan logically and coherently to maximize learning.
Summary:
Use data to improve learning short-term and long-term.
Potential Framing Device (2)
Backward Design
1) Specify instructional target(s) / desired results.
2) Determine acceptable evidence for learning acquisition.
3) Plan learning experience and instruction.
Summary:
Plan logically and coherently to maximize learning.
Summary:
Use data to improve learning short-term and long-term.
Data-Based Decision-Making
1) Establish course goals and assessments.
2) Collect and analyze simple, purposeful data.
3) Improve student learning outcomes horizontally and vertically.
Professional Learning Communities:
The framework / norms/ protocols / agreementsthat institutionalize Backward Design and Data-Based Decision-Making
Agenda: 1) Link Backward Design, Professional Learning Communities (PLCs), and Data-Based Decision-Making
2) Crash Course in PLCs
3) Case Study: Using PLCs as a lever for student achievement
Key Concepts of PLCs
Focus on Student Learning
Collaborative Culture
Results Orientation
What does a PLC look like?
• Protected Time• Norms and Protocol• Collective Learning and its Application
(SMART Goals)• Shared Personal Practice & Ownership• Celebrating the Work
Agenda: 1) Link Backward Design, Professional Learning Communities (PLCs), and Data-Based Decision-Making
2) Crash Course in PLCs
3) Case Study: Using PLCs as a lever for student achievement
Case Study: Chicago Bulls College Prep
Professional Learning Communities as a lever for student achievement
Case Study: Chicago Bulls College Prep
PLCs focused on student achievement, collaborative culture, and results-orientation foster consistent results.
At CBCP, the structure of Professional Learning Communities is leveraged to: 1) Create quantifiable, measurable course goals.
2) Create and implement reliable common assessments.
3) Collect simple, clear, purposeful data from common assessments.
4) Analyze data through bucketing and item analyses.
5) Make data-based curricular decisions (horizontal and vertical).
Collecting Sound Data is the Foundation for Evidence-Based Curricular Decisions
Meaningful instructional & curricular decisions must be tethered to reliable and valid data.
What data should be collected?
The simplest data connected with
overarching course goal(s).
What is the most effective manner to analyze this data?
Organize data by ‘bucket’ and / or
perform item analysis.
What comes next?Use data to inform
decisions on pacing, coverage, and rigor.
Scenario 1: Chemistry Departmental Data1) Create quantifiable, measurable course goals.
Goal 1: Make the high-character decision every time. Goal 2: Prepare for AP and introductory college science classes. Goal 3: Average 75%+ on unit exams and semester finals. Average 83%+ for course grade. Goal 4: Grow 2+ points on the Science ACT
Scenario 1: Chemistry Departmental Data2) Create and implement reliable common assessments.
Common unit assessments exist across three Chemistry teachers for (a) honors, and (b) academic courses.
Eight common unit assessments and two semester finals have been developed using Backward Design.
Exams are given on the same day throughout the department.
Scenario 1: Chemistry Departmental Data3) Collect simple, clear, purposeful data from common assessments.
16-17 Semester 1 Final 75%
15-16 Semester 1 Final 71%
14-15 Semester 1 Final 68%
13-14 Semester 1 Final 64%
Course Goal: Students score 75% on departmental Final Exams
Item A B C D Bucket
40 0 0% 22 27% 20 24% 41 49% Thermo
41 56 67% 6 7% 14 17% 7 8% Thermo
42 14 17% 47 57% 7 8% 15 18% Thermo
43 7 8% 1 1% 60 72% 14 17% Thermo
44 13 16% 46 55% 20 24% 4 5% Atomic Models
45 53 64% 5 6% 15 18% 10 12% Atomic Models
46 66 80% 3 4% 8 10% 6 7% Subatomic Particles
47 1 1% 0 0% 82 99% 0 0% Subatomic Particles
48 41 49% 7 8% 5 6% 30 36% Atomic Models
49 17 20% 7 8% 1 1% 57 69% Atomic Models
Goal = 75%
Actual = 71%
Thermo = 61%
Atomic Models = 59%
Subatomic Particles = 90%
Scenario 1: Chemistry Departmental Data4) Analyze data through bucketing and item analyses.
Bucket Reasoning Action Step
ThermoStudents cannot effectively transfer information regarding Heat Capacity & Specific Heat.
Increase number of application and transfer problems in Unit 2 next year.
Atomic Models
Students cannot differentiate the nuances of atomic models, especially with regards to positioning of subatomic particles.
More compare / contrast analysis in first week of Unit 3 on Atomic Models.
Subatomic Particles
Independent Mastery No change
Scenario 1: Chemistry Departmental Data5) Make data-based curricular decisions (horizontal and vertical).
At CBCP, the structure of Professional Learning Communities is leveraged to: 1) Create quantifiable, measurable course goals.
2) Create and implement reliable common assessments.
3) Collect simple, clear, purposeful data from common assessments.
4) Analyze data through bucketing and item analyses.
5) Make data-based curricular decisions (horizontal and vertical).
Scenario 2: Standardized Test Data
3) Collect simple, clear, purposeful data from common assessments.
Class Incoming Score
Mid-Year Score
EOY Score Growth
Period 1 19.14 20.15 22.25 3.11
Period 2 18.55 19.58 21.17 2.65
Scenario 2: Standardized Test Data
4) Analyze data by bucketing and item analyses.
Bucket Reasoning Action Step
New InfoStudents struggling to incorporate new information into existing study or narrative and draw appropriate conclusion.
Produce homework questions (content) 2x per week where new information is presented and a conclusion must be made based on this new information.
EMIStudents struggling to comprehend / decipher high-level scientific text.
Increase Lexile / reading level of classroom handouts; use one Scientific American article per week as discussion text.
Scenario 2: Standardized Test Data
5) Make data-based curricular decisions (horizontal and vertical).
Key Concepts of PLCs
Focus on Student Learning
Collaborative Culture
Results Orientation
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