MOY Data Analysis Increasing Achievement and Growth Grant NW BOCES

Slides:



Advertisements
Similar presentations
An Overview of Indianas Special Education Rules Professor Daniel J. Abbott ED 242 Fall 2009.
Advertisements

Judith J. Carta, Ph.D. Juniper Gardens Children’s Project University of Kansas with help from Elaine Carlson, WESTAT.
November 2009 Oregon RTI Project Cadre 5.  Participants will understand both general IDEA evaluation requirements and evaluation requirements for Specific.
STAR Assessments: Using data to drive your instruction 2012.
Plan Evaluation/Progress Monitoring Problem Identification What is the problem? Problem Analysis Why is it happening? Progress Monitoring Did it work?
1 Module 2 Using DIBELS Next Data: Identifying and Validating Need for Support.
First Sound Fluency & Phoneme Segmentation Fluency Phonemic Awareness
Improving Outcomes for Students with Disabilities Office of Exceptional Children Cathy Boshamer, Director John Payne, Team Lead November 7, 2013.
1 Data-Based Leadership Cohort B March 2, 2006 (C) 2006 by the Oregon Reading First Center Center on Teaching and Learning.
Oregon Reading First (2009)1 Oregon Reading First Webinar Data-based Action Planning Winter 2009.
Oregon Reading First (2010)1 Oregon Reading First Regional Coaches’ Meeting May 13, 2010.
1 National Reading First Impact Study: Critique in the Context of Oregon Reading First Oregon Reading First Center May 13, 2008 Scott K. Baker, Ph.D. Hank.
Oregon Reading First (2009)1 Oregon Reading First Regional Coaches’ Meeting May 2009.
Oregon Reading First (2010)1 Winter 2010 Data Based Planning for Instructional Focus Groups.
1 Oregon Reading First: Cohort B Leadership Session Portland, Oregon May 27, 2009.
Universal Screening and Progress Monitoring Nebraska Department of Education Response-to-Intervention Consortium.
EOY Reading 3D NC Comparison Templates The following slides contain a combination of state-level EOY mCLASS reports paired with blank templates.
From Data to Dialogue: Facilitating meaningful change with reading data Ginny Axon misd.net) Terri Metcalf
School Improvement Specialist Meeting
Grade-level Benchmark Data Meetings
Interpreting DIBELS reports LaVerne Snowden Terri Metcalf
Project MORE Independent Evaluation Completed by The Center for Evaluation Services Bowling Green State University Updated 11/12.
Elementary Assessment Data Update Edmonds School District January 2013.
1. 2 Roots of Ontario Legislation and Policy Bill 82 (1980), An Amendment to the Education Act: –Universal access: right of all children, condition notwithstanding,
RTI Procedures Tigard Tualatin School District EBIS / RTI Project Jennifer Doolittle Oregon Department of Education, January 27, 2006.
Introduction to GREAT for ELs Office of Student Assessment Wisconsin Department of Public Instruction (608)
From Screening to Verification: The RTI Process at Westside Jolene Johnson, Ed.S. Monica McKevitt, Ed.S.
Special Education Referral and Evaluation Report Oregon RTI Project Sustaining Districts Trainings
Data Analysis MiBLSi Project September 2005 Based on material by Ed Kameenui Deb Simmons Roland Good Ruth Kaminski Rob Horner George Sugai.
HOW DO WE USE DIBELS WITH AN OUTCOMES-DRIVEN MODEL? Identify the Need for Support Validate the Need for Support Plan Support Evaluate Effectiveness of.
Benchmark Data Meetings Presented to Coaches September 6, 2013 Adapted from MiBLSi materials.
EOY DIBELS Benchmark Data for Intervention Programs Oregon Reading First Schools June, 2009 © 2009 by the Oregon Reading First Center Center on Teaching.
1 New Hampshire – Addenda Ppt Slides State Level Results (slides 2-7) 2Enrollment - Grades 3-8 for 2005 and Reading NECAP 4Mathematics
Evaluation and Eligibility Using RTI Crook County School District February 26, 2010.
EBIS Effective Behavior & Instructional Supports Tina Rodriguez June 23, 2013.
Special Education Module #1 : Legislative Overview.
Significant Developmental Delay Annual State Superintendent’s Conference on Special Education and Pupil Services October 20-21, 2015.
Where Do You Stand? Using Data to Size Up Your School’s Progress Michael C. McKenna University of Virginia.
We will start shortly. Feel free to relax and chat while you wait for class to begin. Our agenda for tonight’s seminar is to discuss Response to Intervention,
Examining Student Work Middle School Math Teachers District SIP Day January 27, 2016.
1 Average Range Fall. 2 Average Range Winter 3 Average Range Spring.
Progress Monitoring Goal Setting Overview of Measures Keith Drieberg, Director of Psychological Services John Oliveri, School Psychologist Cathleen Geraghty,
Revisiting SPL/IIT/SAT/SLD AND OTHER ALPHABETIC ANOMOLIES!
Anderson School Accreditation We commit to continuous growth and improvement by  Creating a culture for learning by working together  Providing.
Responsiveness of Students With Language Difficulties to Early Intervention in Reading O’Conner, R.E., Bocian, K., Beebe-Frankenberger, M., Linklater,
Policy Recommendation Best Practices in Reading Achievement to Address Reading Failure Roxanne Boyd Walden University.
Time for Change: Examining Utah Data Relating to Student Performance
Completing the School Census
Special Education Today
Wake County Public School System
What is Value Added?.
Special Education Program Evaluation
Data-Based Leadership
CASD K-6 Transition Plan from DIBELS to DIBELS Next
RTI & SRBI What Are They and How Can We Use Them?
DIBELS Next Overview.
Reading Goals and Reading Growth A Proposal for Cohort A
Overview: Understanding and Building a Schoolwide Assessment Plan
RSU #38 Board of Education
Reading Goals and Reading Growth A Proposal for Cohort A
Anderson Elementary School
Integrating Outcomes Learning Community Call February 8, 2012
Lauren Kinsella Dr. Wright ITEC 7305
Using Strategies, Protocols, and Tools to Analyze Data A Presentation of the National Reading Technical Assistance Center (NRTAC) Speaker’s notes Additional.
School-based evaluations
Building Capacity to Use Child Outcomes Data to Improve Systems and Practices 2018 DEC Conference.
P ! A L S Interpreting Student Data to
Insert School Name Here
RTI Procedures Tigard Tualatin School District EBIS / RTI Project
Presentation transcript:

MOY Data Analysis Increasing Achievement and Growth Grant NW BOCES February 6, 2014

Objectives for today The RLC will understand DIBELS Next growth data (BOY to MOY) for the entire BOCES population of K-3 students with disabilities Each literacy lead will understand how to do a similar evaluation of the data for their school/district Literacy leads will utilize the data analysis methods presented in this webinar to help develop next steps for students with disabilities in their school/districts

Analysis of growth Purpose: determine whether the instructional programming for a district, school, class, program and/or individual student has changed or maintained the trajectory of growth DIBELS is a “stoplight” assessment – lets us know whether a student is at risk of “reading failure” based on research Example of change – student has moved from yellow to red Example of maintain – student has stayed at the same level of risk

Percentiles A percentile or percentile rank in DIBELS is a description of how a student scored compared to a national sample of about 30,000 students For DIBELS, percentiles were last published for the 2011- 2012 school year. Amplify percentiles are local (i.e. compared only to your school)

Percentile rank example If a kindergartener at the beginning of the year has a composite score of 10, they are in the 25th percentile. That means that 25% of kindergarteners in DIBELS sample group scored at or below where this student scored DIBELS places their benchmark (green) score at or near the 40th percentile

Percentile growth If a student is making typical growth, they will stay at the same percentile ranking If a student is making less than typical growth, their percentile ranking will drop If a student is making more than typical growth, their percentile ranking will increase If a student’s percentile ranking changes enough, then their category of risk will change

Questions for our data Are the changes we are implementing through the grant making a difference for students? Which students are making adequate growth? Which students need changes to their systems of support in order to make adequate growth?

Our Data - Process Created cohort group Pulled BOY and MOY data from Amplify for cohort group Analyzed change from BOY to MOY based on risk category Analyzed change from BOY to MOY based on percentile ranking Disaggregated by disability I started by creating a “cohort group” of students who were on IEPs during BOY benchmarking (based on data pulled from IEP system) – this group includes any students who were exited between Sept. 30 and January 1. Looking for growth among this group – sometimes this means that they students will have been exited due to their performance, so we want to be sure to include these students as a success.

Our Data - BOY At/Above Below Well Below Total BOY Total BOY %   Well Below Total BOY Total BOY % Primary Disability BOY BOY % 01 I.D. or S.L.I.C. 1 50.00% 0.00% 2 100.00% 03 S.E.D. or Emotional 33.33% 16.67% 3 6 04 S.L.D. 4 8.51% 5 10.64% 38 80.85% 47 05 H.I. incl. Deafness or H.D. 66.67% 06 V.I. incl. Blindness or V.D. 07 Physical 10 37.04% 3.70% 16 59.26% 27 08 Speech/Language 21 27.27% 18 23.38% 49.35% 77 10 Multiple 11 D.D. or Preschooler 9.09% 13.64% 17 77.27% 22 13 A.S.D. or Autism 44.44% 22.22% 9 16 Other Health Impairment 75.00% 25.00% Grand Total 46 22.66% 33 16.26% 124 61.08% 203

Our Data - MOY At/Above Below Well Below Total MOY Total MOY %   Below Well Below Total MOY Total MOY % Primary Disability MOY MOY % 01 I.D. or S.L.I.C. 1 33.33% 0.00% 2 66.67% 3 100.00% 03 S.E.D. or Emotional 60.00% 40.00% 5 04 S.L.D. 10.64% 37 78.72% 47 05 H.I. incl. Deafness or H.D. 06 V.I. incl. Blindness or V.D. 07 Physical 10 37.04% 11.11% 14 51.85% 27 08 Speech/Language 29 38.67% 18 24.00% 28 37.33% 75 10 Multiple 11 D.D. or Preschooler 23.81% 14.29% 13 61.90% 21 13 A.S.D. or Autism 4 44.44% 9 16 Other Health Impairment 50.00% Grand Total 59 29.80% 36 18.18% 103 52.02% 198

Benchmark Strategic Intensive Our Data – BOY to MOY BOY: 22.66% (n=46)

The vast majority (71%) of kids stayed in the same risk level on DIBELS. 21% showed a decrease in risk (good!) 8% showed an increase in risk (bad!) Which of these groups do we need to look at on an individual basis and make changes to their systems of support?

Of the strategic and intensive students who stayed in the same range from BOY to MOY, here are their changes in percentile: 36% increased percentile ranking (significant is more than 10 percentile points) – good! 36% stayed the same – ok, but not good enough to catch up! 28% decreased (bad!) Which of these groups do we need to examine on an individual basis to make changes to their systems of support?

This chart shows the students who were at or above benchmark at both BOY and MOY. The same information about change in percentiles applies to them, although it’s probably ok if they stay at the same percentile because they are making typical growth. Which students do we need to look at on an individual basis here? Which would you add to a “watch” list to make sure that they stay at or above benchmark?

The majority of the students in our cohort group are either identified as having SLD (n=47)or a Speech/Language disability (n=75). The SLD category is a particularly difficult one with regard to reading achievement because their low achievement was likely why they were identified in the first place! If we can push these students’ growth, then we will unquestionably have been successful with our work. Again, the vast majority of students stayed at the same level of risk from BOY to MOY.

Our Data - BOY At/Above Below Well Below Total BOY Total BOY %   Well Below Total BOY Total BOY % Primary Disability BOY BOY % 01 I.D. or S.L.I.C. 1 50.00% 0.00% 2 100.00% 03 S.E.D. or Emotional 33.33% 16.67% 3 6 04 S.L.D. 4 8.51% 5 10.64% 38 80.85% 47 05 H.I. incl. Deafness or H.D. 66.67% 06 V.I. incl. Blindness or V.D. 07 Physical 10 37.04% 3.70% 16 59.26% 27 08 Speech/Language 21 27.27% 18 23.38% 49.35% 77 10 Multiple 11 D.D. or Preschooler 9.09% 13.64% 17 77.27% 22 13 A.S.D. or Autism 44.44% 22.22% 9 16 Other Health Impairment 75.00% 25.00% Grand Total 46 22.66% 33 16.26% 124 61.08% 203

Our Data - MOY At/Above   Below Well Below Total MOY Total MOY % Primary Disability MOY MOY % 01 I.D. or S.L.I.C. 1 33.33% 0.00% 2 66.67% 3 100.00% 03 S.E.D. or Emotional 60.00% 40.00% 5 04 S.L.D. 10.64% 37 78.72% 47 05 H.I. incl. Deafness or H.D. 06 V.I. incl. Blindness or V.D. 07 Physical 10 37.04% 11.11% 14 51.85% 27 08 Speech/Language 29 38.67% 18 24.00% 28 37.33% 75 10 Multiple 11 D.D. or Preschooler 23.81% 14.29% 13 61.90% 21 13 A.S.D. or Autism 4 44.44% 9 16 Other Health Impairment 50.00% Grand Total 59 29.80% 36 18.18% 103 52.02% 198 If we go back to this data, we see that the VAST majority of these students are in the intensive range and didn’t move out of it from fall to winter.

If we look at the change in percentile for these students, the number of students per category is low (which means we should be careful with how we interpret this data, but the trends are still clear. Most students either stayed at the same percentile (ok – but not enough to catch up) *caveat: how many stayed at the 1st percentile?* or increased (really good!) Which students do we need to look at more closely in order to improve their systems of support?

Our Data - BOY At/Above Below   Well Below Total BOY Total BOY % Primary Disability BOY BOY % 01 I.D. or S.L.I.C. 1 50.00% 0.00% 2 100.00% 03 S.E.D. or Emotional 33.33% 16.67% 3 6 04 S.L.D. 4 8.51% 5 10.64% 38 80.85% 47 05 H.I. incl. Deafness or H.D. 66.67% 06 V.I. incl. Blindness or V.D. 07 Physical 10 37.04% 3.70% 16 59.26% 27 08 Speech/Language 21 27.27% 18 23.38% 49.35% 77 10 Multiple 11 D.D. or Preschooler 9.09% 13.64% 17 77.27% 22 13 A.S.D. or Autism 44.44% 22.22% 9 16 Other Health Impairment 75.00% 25.00% Grand Total 46 22.66% 33 16.26% 124 61.08% 203 Our largest group of students (n=75) are identified as having a speech/language disability. We know that by 3rd grade (and beyond) many of these same students end up being relabeled as having an SLD. Because of the strong connections between speech, language, and reading, it is our hypothesis that if students who are identified with a speech/language disability early on receive the intervention they need, then they will exit special education rather than being relabeled as SLD. One of our measures of success in this grant is whether or not we see a reduction in this type of reclassification.

Our Data - MOY At/Above   Below Well Below Total MOY Total MOY % Primary Disability MOY MOY % 01 I.D. or S.L.I.C. 1 33.33% 0.00% 2 66.67% 3 100.00% 03 S.E.D. or Emotional 60.00% 40.00% 5 04 S.L.D. 10.64% 37 78.72% 47 05 H.I. incl. Deafness or H.D. 06 V.I. incl. Blindness or V.D. 07 Physical 10 37.04% 11.11% 14 51.85% 27 08 Speech/Language 29 38.67% 18 24.00% 28 37.33% 75 10 Multiple 11 D.D. or Preschooler 23.81% 14.29% 13 61.90% 21 13 A.S.D. or Autism 4 44.44% 9 16 Other Health Impairment 50.00% Grand Total 59 29.80% 36 18.18% 103 52.02% 198 For these students, the data looks a bit better than for the SLD students (good news!)

Again, the vast majority of students either had the same level of risk (ok, but not good enough) or a reduced level of risk (good!) from BOY to MOY. And again, which of these groups of students should have their systems of support re-evaluated?

Students with a speech/language disability who scored below grade level. This data is not quite as useful as in some other cases, because the number of students that we are looking at is so small. However, it is valuable to look at this information on a per-student basis to determine whether or not they made adequate progress to catch up.

Next Steps/Considerations What does your school’s data tell you? What are the big picture patterns? Which students need to be looked at individually? What other patterns could you look for in the data? What does it mean when a student is not making typical growth? (hint: think about their core instruction!) When a student is making accelerated growth, how can you replicate that success? Possible patterns – interventions, other?

Reflect What did you learn about analyzing student data today? What will be valuable to use with your staff? What is the best presentation method so that staff understand and have buy-in to what you share with them? How will you use what you learned today in your school/district? What questions do you want to answer with your data? What are you wondering about your data?