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