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PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

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Page 1: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

PLCs & Data: Key Drivers for Successful Response to

Intervention

Matthew Burns, Ph.D. University of Minnesota

Page 2: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Contributions to Learning – Hattie 2009

• The student d = .40

• The school d = .23

• The teacher d = .49

• The curriculum d = .45

Page 3: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Interventions for Children with LD

Reading comprehension 1.13 Direct instruction .84 Psycholinguistic training .39 Modality instruction .15 Diet .12 Perceptual training .08

Kavale & Forness, 2000

Page 4: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

, at no cost to

the parents or guardians, to meet the

of a child with a

disability.

Individualized instruction

unique

needs

Page 5: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

The answer??

“All hands on deck” – Judy Elliott, Chief Academic Officer of Los Angeles Unified Schools

Education

Page 6: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

And DATA!

Unique learning needs = Education that is SPECIAL

Page 7: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Keys to SuccessSt. Paul Pioneer Press June 4th 2006

• Reading Above All Else– Emphasize reading and writing especially K-2

• Beyond the Classroom– After school programs and social services

• Continuous Assessment/Small-Group Instruction– Formal and informal assessments to provide an

appropriate level of challenge

• Effective Staff– Strong leadership and cohesive staff with co-planning

• Structured, Disciplined Environment

Page 8: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota
Page 9: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota
Page 10: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota
Page 11: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

MTSS

The systematic use of assessment data to most efficiently allocate resources in order to enhance learning for all students.

Burns & VanDerHeyden, 2006

Page 12: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Professional Learning Communities • Teams of teachers

– All of those who teach a particular grade level– A forum to collectively problem-solve at the

school, classroom, and student level (DuFour, Eaker, DuFour, 2005)

• PLCS focus on student data and a culture of collaboration (DuFour, 2005).

• Many do not have common assessments, criteria to judge student proficiency, or a process to collaboratively analyze data (DuFour et al., 2005; Love, 2009).

Page 13: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

PLC Meetings: Agenda   

PLC: 1st weekly meeting of the month (Content Focus)

Grade level teams and coaches with additional personnel as appropriate

School-site established PLC focus on various topics (e.g., math, STEM, behavior, environment, or other school topical initiatives)

PLC: 2nd weekly meeting of the month RTI (Core Instruction Literacy Focus)

Grade level teams and coaches with additional personnel as appropriate

Examine various formal and informal data to drive core instruction

Agenda will include embedded professional development on topics that address opportunities and challenges for core instruction

PLC: 3rd weekly meeting of the month (Content Focus)

Grade level teams and coaches with additional personnel as appropriate

School-site established PLC focus with schools studying varied topics

PLC: 4th weekly meeting of the month RTI (Data Analysis)

Grade level teams and coaches with additional personnel as appropriate (data management team)

Analyze screening/benchmark data Analyze progress monitoring data Discuss, monitor and adjust tiered interventions.

Page 14: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Four Purposes of AssessmentProgram evaluation: How is the education system working for students overall?

• State test

Screening: Which of my students are not meeting grade level expectations given Universal Instruction?

• E.g., MAP

Diagnostic: What are the specific needs of students who struggle with reading or math?

E.g., measures of specific skills

Monitoring Progress: What does the student’s growth look like?

E.g., CBM

Page 15: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Screener MAP < 25th %ile MAP > 25th %ile Total

Oral Reading Fluency (ORF)

ORF < Benchmark Goal 276 145 421

A B

ORF > Benchmark Goal 46 501 547

C D

Total 322 646 968

Informal Reading Inventory (RI)

RI < Benchmark Goal 90 189 279

A B

RI > Benchmark Goal 200 367 567

C D

Total 290 556 846

Sensitivity = a / (a + c) = .86 for ORF and .31 for F&P, Specificity = d / (b + d) = .78 for ORF and .66 for F&P, Overall Correct Classification = (a + d) / N = .80 for ORF and .54 for F&P

Page 16: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

 Screening/

Benchmark Diagnostic

Monitor Progress

Skill

Monitor

Progress

General

Emergent

(Typically K-1)

PA to decoding

Alphabetic

Principle (PA)

Quick

Phonemic

Awareness

(QPA)

Weekly

DIBELS PSF

(Specific PA task –

e.g., Rhyming Task, )

Every other week

DIBELS PSF

Beginning

(Typically 1st -2nd)

Decoding

ORF QPA, NWF, &

WTW

Weekly

DIBELS NWF

(Specific NWF - e.g.,

long vowel sounds)

Every other week

ORF

Page 17: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

 Screening/

Benchmark Diagnostic

Monitor Progress

Skill

Monitor

Progress

General

Transitional

(Typically 2nd – 3rd)

Decoding to Fluency

ORF & MAP MAP, ORF, &

Word Their

Way (WTW)

Weekly

DIBELS NWF or

DIBELS

Instructional-level

ORF

Every other

week

ORF

Intermediate

(Typically 3rd)

Fluency to

Comprehension

ORF & MAP MAP, ORF, &

WTW

Weekly

DIBELS

Instructional-level

ORF

Every other

week

ORF

Page 18: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Path to Reading Excellence in School Sites

wwww.cehd.umn.edu/reading/PRESS/default.html

Page 19: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

MTSS and Problem-Solving

Mea

sure

men

t Pre

cisi

on Measurem

ent Frequency

Problem-Analysis

TIER I

TIER I I

TIER III

Page 20: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Problem Solving• Tier I – Identify discrepancy between

expectation and performance for class or individual (Is it a classwide problem?)

• Tier II – Identify discrepancy for individual. Identify category of problem. (What is the category of the problem?)

• Tier III – Identify discrepancy for individual. Identify causal variable. (What is the causal variable?)

Page 21: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Grade Level Team Meeting• Is there a classwide problem?

• Who needs Tier 2?

• Did we miss anyone?

• What should we do for Tier 2?

• Should we go to Tier 3?

Page 22: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Developmental Activities 1st grade – Phonemic awareness and phonics

instruction 2nd grade – Explicit phonics instruction, writing,

and fluency 3rd grade – Fluency and comprehension 4th grade – Read to learn Upper elementary & Middle School – Vocabulary

and comprehension High school – Comprehension and application

Page 23: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

What is the Class Median?• Median: the middle value in a list of

numbers when the values are arranged from lowest to highest.

• Finding the class median:– Order student scores from the lowest to highest

value.– The score in the middle of the list is the

median.– If there is an even number of scores, take the

average of the middle two scores.

Minnesota Center for Reading Research

Page 24: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

What is the Class Median?Winter Benchmark 101

Student Grade ORFWRC Errors

B 3 18 6A 3 21 8E 3 46 6N 3 49 6K 3 50 8R 3 76 3P 3 86 6C 3 87 1G 3 89 3Q 3 89 2F 3 92 1U 3 94 2J 3 96 2M 3 97 1H 3 98 1O 3 105 0D 3 110 0S 3 112 3I 3 119 2L 3 122 2T 3 141 1Class Median 92

Winter Benchmark 101

Student Grade  ORF  WRC Errors

A 3 21 8B 3 18 6C 3 87 1D 3 110 0E 3 46 6F 3 92 1G 3 89 3H 3 98 1I 3 119 2J 3 96 2K 3 50 8L 3 122 2M 3 97 1N 3 49 6O 3 105 0P 3 86 6Q 3 89 2R 3 76 3S 3 112 3T 3 141 1U 3 94 2

Class MedianMinnesota Center for Reading Research

MODEL

Page 25: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

What is the Class Median?Spring Benchmark 90

Student Grade ORFWRC Errors

F 2 18 2 0E 2 21 1 0B 2 22 5 0K 2 26 4 0Q 2 32 6 0R 2 35 2 0N 2 46 8 1S 2 51 1 1M 2 54 0 1G 2 60 0 1A 2 64 5 2D 2 68 4 2H 2 70 2 2O 2 70 3 3T 2 71 1 4P 2 75 0 4C 2 77 0 5J 2 77 0 5I 2 84 0 6L 2 89 1 8

Class Median 62 1.5

Spring Benchmark 90

Student Grade  ORF  WRC Errors

A 2 64 5B 2 22 5C 2 77 0D 2 68 4E 2 21 1F 2 18 2G 2 60 0H 2 70 2I 2 84 0J 2 77 0K 2 26 4L 2 89 1M 2 54 0N 2 46 8O 2 70 3P 2 75 0Q 2 32 6R 2 35 2S 2 51 1T 2 71 1

Class Median

Minnesota Center for Reading Research

MODEL

Page 26: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

GUIDE:

1. Find class median for WRC and errors on the “Second Grade Practice Data” worksheet

Page 27: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Minnesota Center for Reading Research

Is there a problem?

Page 28: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Classwide Need and Instructional PLC• What do highly effective teachers do? • What will we as a TEAM do?• How will we know if it works?

• What data can we collect (outcome)?• For what will we look (process)?• How will coach provide feedback?

•  What will we do next?– What is the implementation plan (e.g., observe, first steps,

etc.)?– Coaches role (what will be modeled/shared)?– Who else will help?– What process and outcomes will be reported at the next

meeting?

Page 29: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

National Reading Panel

• Google – National, reading, panel, and teachers

• Tim Shanahan

• Get PLCs using this

Page 30: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota
Page 31: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Partner ReadingPartnerships

Minnesota Center for Reading Research

Page 32: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Procedure

Partner Reading Paragraph Shrinking

1. Stronger reader reads aloud for 5 minutes

2. The weaker reader reads aloud the SAME text for 5 minutes

3. Weaker readers sequence the major events of what has been read for 1 minute

1. For 5 minutes the stronger read continues reading new text in the story, stopping after each paragraph to summarize

2. For 5 minutes the weaker reader continues with the new text, stopping after each paragraph to summarize

Minnesota Center for Reading Research

Page 33: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Timeline

Collect Data: Pre-test (fluency and comprehension)

• Day 1: Train Students on Set Up Procedures and Partner Reading, Practice Reading for 10 minutes, Error Correction

• Day 2: Train Students on Paragraph Shrinking, Practice Reading for 10 minutes

• Day 3-10: Partner Reading, Paragraph Shrinking 15 minutes every day

Collect Data: Post-test (fluency and comprehension)

Minnesota Center for Reading Research

Page 34: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Partner Reading

• First Reader reads for 5 minutes.

• Second Reader reads the same text for 5 minutes.

• Second Reader retells for 1 minute.

 

Talk only to your partner and only talk about Partner Reading

Keep your voice low Help your partner

Try your best!

RULES

Minnesota Center for Reading Research

Page 35: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Paragraph Shrinking• Name the most important who or what.

• Tell the most important thing about the who or what.

• Say the main idea in 10 words or less.

Minnesota Center for Reading Research

Page 36: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Correction Procedures

STOP. That word is______________

What word?______________________

Good Job!

Go back and read that line again.

Minnesota Center for Reading Research

Page 37: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Point System• Transitions• Staying on task• Following correct procedures

1st Reader 2nd Reader

Mohamed Jibril

Sally Keisha

Farhiya Jackie

Sam Roger

Minnesota Center for Reading Research

Page 38: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

What we found: 3rd grade Partner Reading data

Third Grade

Third Grade Benchmark

91 Words Read Correctly (WRC)

 

  Pre Intervention Class Median

(WRC)

Post Intervention Class Median

(WRC)

Slope (WRC)

Class 1 81 104 11.5

Class 2 87 115 14

Minnesota Center for Reading Research

Page 39: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

WRC WRC after PALSStudent 1 48 92Student 2 122 142Student 3 126 147Student 4 82 113Student 5 102 117Student 6 77 97Student 7 51 70Student 8 84 95Student 9 80 82Student 10 102 127Student 11 83 106Student 12 38 47Student 13 104 115Student 14 152 161Student 15 143 158Student 16 115 125Student 17 142 160Student 18 114 127Student 19 13 40Student 20 75 92Student 21 141 136Student 22 87 105Student 23 49 47

Median 87 113

Page 40: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

What we found: 3rd grade Partner Reading data

  Students Below Benchmark Pre

Intervention

Students Below Benchmark Post

Intervention

Total Students in Class

Third Grade Class 1

10 5 20

Third Grade Class 2

13 5 23

Minnesota Center for Reading Research

Page 41: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Growth from Winter to SpringClass-Wide Interventions

10 Classrooms K-3

0

10

20

30

Actual Growth Winter to SpringTargeted Growth (one yr of growth) Win-ter To Spring

Page 42: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Growth from Winter To SpringNO Class-Wide Interventions

11 Classrooms K-3

0

10

20

30

Actual Growth Fall To WinterTargeted Growth (one year growth) Fall To Winter

Page 43: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Class-wide Interventions Implemented in 10 of the 21 Classes Below Winter

Benchmark:9 of the 10 Above Spring Benchmark

Class-wide Interventions0

1

2

3

4

5

6

7

8

9

10

Above Spring BenchmarkBelow Spring Benchmark

Page 44: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

NO Class-wide Intervention Implemented in 11 Classes Below Winter Benchmark

2 of the 11 Above Spring Benchmark

No Class-wide Intervention0

1

2

3

4

5

6

7

8

9

10

Above Spring BenchmarkBelow Spring Benchmark

Page 45: PLCs & Data: Key Drivers for Successful Response to Intervention Matthew Burns, Ph.D. University of Minnesota

Minnesota Center for Reading Research