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Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai
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Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

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Page 1: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Data Analysis MiBLSi Project

September 2005Based on material by

Ed Kameenui

Deb Simmons

Roland Good

Ruth Kaminski

Rob Horner

George Sugai

Page 2: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Purpose

The intent of this section is to

• Review data keeping in mind the need to focus on key elements

Page 3: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

EBS Team Implementation Survey: On-Going Activity

0

1

2

3

4

5

6

Quarter

Num

ber o

f Ite

ms (

Six

Poss

ible

)

Achieved In Progress Not Started

Page 4: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.
Page 5: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Fourth Grade Reading MEAP Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

03-04 04-05 05-06 06-07 07-08 08-09

Year

Pe

rcen

t

Exceeded Met Basic Apprentice

Page 6: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year Reports

• The End of Year Report is designed to summarize referral rates per 100 students by:– Year– Problem behavior– Grade level– Location

• In addition, a suspension/expulsion report and the “triangle” summary data are provided. Summaries are organized for use at the district-level.

TM

Page 7: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year ReportReferrals Per 100 Students

Page 8: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year ReportProblem Behavior Report

Page 9: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year ReportGrade Report

Page 10: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year ReportLocation Report

Page 11: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year ReportSuspension/Expulsion Report

Page 12: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

End of Year ReportTriangle Data

Page 13: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Team Work Time

Take time with your team to look at your school’s behavior support data

1. What is working well with your school (based on the data)?

2. What areas do you need to focus on?

Page 14: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Dynamic Indicators of Basic Early Literacy Skills (DIBELS)

Data ReviewFall 2005

based on the work of:

Roland Good

Ruth Kaminski

Page 15: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Data ReviewWho Needs Phonological Awareness?

QuickTime™ and aSorenson Video decompressorare needed to see this picture.

Page 16: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Interpreting and Using DIBELS™ Data

Page 17: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Assess Progress Toward Outcomes DIBELS Benchmark Goals

• Initial Sound Fluency:

• Phoneme Segmentation Fluency:

• Nonsense Word Fluency:

• DIBELS™ Oral Reading Fluency:

–35 sounds per minute by Spring Kindergarten

–25 sounds per minute by Winter Kindergarten

–40 words correct per minute by Spring First Grade

–50 sounds per minute with at least 15 words recoded by Winter First Grade

–90 words correct per minute by Spring Second Grade

–110 words correct per minute by Spring Third Grade

–118 words correct per minute by Spring Fourth Grade–124 words correct per minute by Spring Fifth Grade–125 words correct per minute by Spring Sixth Grade

Page 18: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Three Risk CategoriesUsed Prior to Benchmark Time

• Low risk – Has met progressive benchmark and is on track to achieve

benchmark goal – 80%-100% probability of reaching next benchmark goal

• Some risk – Low emerging skills; is making progress and has 50/50 chance of

achieving benchmark goal – 50% probability of reaching next benchmark goal

• At risk – Seriously below progressive benchmark; at risk for achieving

benchmark goal – 0%-20% probability of reaching next benchmark goal

Page 19: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Three Status Categories:Used at or After Benchmark Goal Time

• Established – Achieved the benchmark goal – 80%-100% probability of reaching next benchmark goal

• Emerging– Low emerging skills but has not achieved the benchmark goal – 50% probability of reaching next benchmark goal

• Deficit – Seriously below benchmark goal– 0%-20% probability of reaching next benchmark goal

Page 20: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Three levels of InstructionBased on Pattern of Performance Across All

Measures

• Benchmark Instruction - At Grade Level: – 80% - 100% probability of reaching next benchmark goal.– Provide Core Curriculum focused on big ideas.

• Strategic Instructional Support - Additional Intervention– 50% probability of reaching next benchmark goal.– Provide extra practice; adaptations of core curriculum; small group

instruction with supplementary program.

• Intensive Instructional Support - Substantial Intervention– 0% - 20% probability of reaching next benchmark goal.– Provide focused, explicit instruction with supplementary intensive

curriculum; small group/individual instruction.

Page 21: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Vocabulary

Risk Categories Used Prior to

Benchmark Time

Status Categories Used At or After Benchmark Time

Instructional Level

Low Risk Established Benchmark

Some Risk

(Prevention Mode)

Emerging

(Remediation Mode)

Strategic

At Risk

(Prevention Mode)

Deficit

(Remediation Mode)

Intensive

Page 22: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Using DIBELS™ Data

• What is the purpose of your assessment?– What do you need to know? What question(s) do

you have?– What data can you use? What type of

information will answer the question(s) you have?

Page 23: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

ODM Step Question(s) Data

1. Identify Need Are there students who may need support? How many? Which students?

Benchmark data: Histograms, Box Plots, Class List Report

2. Validate Need

Are we confident that the identified students need support?

Benchmark data and additional information: Repeat assessment, use additional data, knowledge of/information about student

3. Plan Support What level of support for which students? How to group students? What goals, specific skills, curriculum/program, instructional strategies?

Benchmark data and additional information: Individual student booklets, additional diagnostic information, knowledge of/information about student

4. Evaluate Support

Is the support effective for individual students? Progress monitoring data: Individual student progress graphs, class progress graphs

5. Evaluate Outcomes

As a school/district: How effective is our core (benchmark) support? How effective is our supplemental (strategic) support? How effective is our intervention (intensive) support?

Benchmark data: Histograms, Cross-Year Box Plots, Summary of Effectiveness Reports

Page 24: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Step 1. Identify Need for Support

• What do you need to know?– Are there students who may

need additional instructional support to achieve benchmark goals?

– How many students may need additional instructional support?

– Which students may need additional instructional support?

• What data to use? – Histograms

– Boxplots

– Class lists

Page 25: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Histograms (Bar Charts)• The Histogram Report summarizes the distribution of scores of all children in a grade within a school or

district relative to the progressive benchmark/benchmark goal for the time. Student performance is depicted in three categories according to students who have (a) met established goals/progressive benchmarks, (b) are making progress toward goals/progressive benchmarks, or (c) are seriously below target goals/progressive benchmarks.

• The goal is to have most/all students to be on track, i.e. have met established goals/progressive benchmarks

• Over the year, you should begin to see more students who meet established goals and fewer students who are seriously below target goals.

From DIBELS Data System, University of Oregon, 2000-2005

Page 26: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Legend for Interpreting Histograms

= Low Risk or Established

= Some Risk or Emerging

= At Risk or Deficit

Note: Split bars are used when the cutoff scores between categories occur in the middle of a score range. The number of student is indicated by the size of the split part.

From DIBELS Data System, University of Oregon, 2000-2005

Page 27: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Histograms

• True or False– Histograms tell us if there are students who

need additional support– Histograms tell us how many students need

additional support– Histograms tell us who needs additional

support

Page 28: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Box Plots• True or False

– Box plots are another way of summarizing the distribution of performance in a class at a single point in time. The box depicts the range of scores for a school or district relative to the progressive benchmark/benchmark goal.

– The goal is to have most/all students to be on track, i.e. have met established goals/progressive benchmarks. The box and corresponding spindle should be at or above the gray bar.

– Over the year, you should begin to see more students who meet established goals and fewer students who are seriously below target goals.

From DIBELS Data System, University of Oregon, 2000-2005

Page 29: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Legend for Interpreting Box Plots

= progressive benchmark/ benchmark goal

From DIBELS Data System, University of Oregon, 2000-2005

Page 30: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Box PlotSchool A - Fall Kindergarten - ISF

From DIBELS Data System, University of Oregon, 2000-2005

Page 31: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

HistogramSchool A - Fall First Grade - PSF

47% Established PSF

39% Emerging PSF

14% Deficit PSF

From DIBELS Data System, University of Oregon, 2000-2005

Page 32: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

HistogramSchool A - Fall First Grade - NWF

42% Low Risk

29% Some Risk

29% At Risk

From DIBELS Data System, University of Oregon, 2000-2005

Page 33: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Practice• Divide into 2 groups of 3. One group will review

kindergarten and the other review first grade data.– Review Histograms and Boxplots for kindergarten and

first grade for Emerald City School District• What do you know from the data?• What are the implications for curriculum and instruction,

professional development/teacher support for each grade level?

– Discuss your grade level findings with the other group at your table.

Page 34: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

Team Work Time

Take time with your team to look at your school’s reading support data

1. What is working well with your school (based on the data)?

2. What areas do you need to focus on?

Page 35: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

DIBELS Class List Report

Page 36: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

DIBELS

Page 37: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

DIBELS

Page 38: Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.

DIBELS Class List Report