Advanced data based decision making
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Advanced Data Based Decision Making. Kimberly Ingram, Ph.D. Professional Development Coordinator Oregon Dept. of Education February 2008 Southern Oregon PBS Network Conference. Agenda. Part 1 Look at your SET data – Is your Correction Feature at 80%

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Advanced data based decision making

Advanced Data Based Decision Making

Kimberly Ingram, Ph.D.

Professional Development Coordinator

Oregon Dept. of Education

February 2008

Southern Oregon PBS Network Conference


Agenda

Agenda

  • Part 1

    • Look at your SET data – Is your Correction Feature at 80%

      • If Yes, the next few slide will be very meaningful

      • In No, the next few slides will be meaningful and we need to discuss ways to enhance that Feature – “corrections Packet”

    • Examine Data Decision Rules for

      • School-wide Interventions

      • Targeted Group Interventions

      • Individualized Interventions

    • Practice Data-based Decision-Making

    • Additional Data for further prevention efforts

      • Ethnicity reports

      • Special education

  • Part 2 – Prepare for First Day of School


Advanced data based decision making

CONTINUUM OF

SCHOOL-WIDE

POSITIVE BEHAVIOR

SUPPORT

Tertiary Prevention:

FBABSP for Students with High-Risk Behavior

~5%

~15%

2-5 ODR

Primary Prevention/Universal Interventions:

School/Classroom-

Wide Systems for

All Students,

Staff, & Settings

Secondary Prevention:

Specialized Group

Systems for Students with At-Risk Behavior

~80% of Students

0-1 Referrals


Advanced data based decision making

School-wide Positive Behavior Support

Supporting

Decision

Making

Supporting

Staff Behavior

INFORMATION

SYSTEMS

PRACTICES

Supporting

Student Behavior


Improving decision making

Improving Decision-Making

Solution

Problem

From

Problem

Solving

Solution

Problem

To

Information


Using data for on going problem solving

Using Data for On-Going Problem Solving

  • Start with the decisions not the data

  • Use data in “decision layers” (Gilbert, 1978)

    • Is there a problem? (overall rate of ODR)

    • Localize the problem

      • (location, problem behavior, students, time of day)

  • Get specific

  • Use data to guide asking of “the right questions”

  • Don’t drown in the data

  • It’s “OK” to be doing well

  • Be efficient


  • Using discipline data for decision making

    Using Discipline Data for Decision Making


    Use referral data to inform intervention

    Use Referral data to Inform Intervention

    • In order to maximize school resources, it is important to know where the majority of behavior problems are occurring

      • Prevention measures, such as:

        • Re-teaching expectations

        • increasing supervision and monitoring

        • increased use of acknowledgments, or

        • environmental restructuring

      • are often the best interventions for misbehavior especially when referrals are not successfully addressing the problem


    Using discipline data

    Using Discipline Data

    • There are many different data systems for tracking, organizing, and presenting discipline data:

    • You can either make your current system work for you, or

      • SWIS (School Wide Information System) is one of the best systems for flexibility in manipulating data and ease of presenting data to maximize the use of your data

      • eSIS has some similar graphing abilities (Big 5, and a few others). It is not as flexible as SWIS, however, it can still offer excellent data for decision-making


    Key features of data systems that work

    Key features of data systems that work.

    • The data are accurate and valid

    • The data are very easy to collect (1% of staff time)

    • Data are presented in picture (graph) format

    • Data are current (no more than 48 hours old)

    • Data are used for decision-making

      • The data must be available when decisions need to be made (weekly?)

      • Difference between data needs at a school building versus data needs for a district

      • The people who collect the data must see the information used for decision-making.


    Using data for decision making

    Using Data for Decision Making


    Pbs teams use data for

    PBS Teams use data for

    • School-wide, universal, interventions

    • Targeted group, secondary, interventions

    • Individual, tertiary, interventions


    Advanced data based decision making

    School-wide Positive

    Behavior Support

    Systems

    Classroom

    Setting Systems

    Nonclassroom

    Setting Systems

    Individual Student

    Systems

    School-wide

    Systems


    Sw v individual

    SW v. Individual


    What about cleo

    Suspensions/Expulsions Per Year

    2000-01

    2001-02

    Events

    Days

    Events

    Days

    In School Suspensions

    0

    0

    2

    2

    Out of School Suspensions

    1

    1

    3

    2.5

    Expulsions

    0

    0

    0

    0

    What about CLEO?

    • 12 By Dec. 2000 – Jun. 2001

    • 19 By Sep. 2001 – Dec. 2001


    Advanced data based decision making

    CLEO: # By/Day/Month


    Advanced data based decision making

    CLEO: # By by Type


    Advanced data based decision making

    CLEO: # BI by Location


    1 school wide systems if

    Elementary:

    > 1 ODR per day per month per 300 students (majors only)

    Middle:

    > 1 ODR per day per month per 100 students (majors only)

    >40% of students received 1+ ODR

    >2.5 ODR/student

    Modify universal interventions (proactive school-wide discipline) to improve overall discipline system

    Teach, precorrect, & positively reinforce expected behavior

    1. School-wide systems if…


    Swis summary 06 07 majors only 1974 schools 1 025 422 students 948 874 odrs

    SWIS summary 06-07 (Majors Only)1974 schools; 1,025,422 students; 948,874 ODRs


    Swis tm summary 05 06 majors only 1668 schools 838 184 students

    SWISTM summary 05-06 (Majors Only)1668 schools, 838,184 students


    Interpreting office referral data is there a problem

    Interpreting Office Referral Data:Is there a problem?

    • Absolute level (depending on size of school)

      • Middle, High Schools (> 1 per day per 100)

      • Elementary Schools (> 1 per day per 300)

    • Trends

      • Peaks before breaks?

      • Gradual increasing trend across year?

    • Compare levels to last year

      • Improvement?


    Advanced data based decision making

    Elementary School with 250 students


    Average referrals per day per month middle school of 600 students

    Average Referrals per Day per MonthMiddle School of 600 students


    Advanced data based decision making

    Middle School with 500 students


    Advanced data based decision making

    Middle school with 500 students


    Advanced data based decision making

    Middle School with 500 students


    Advanced data based decision making

    Is there a problem?

    Middle school with 500 students (Dec)


    Advanced data based decision making

    Is there a problem?

    Middle School with 500 students


    Advanced data based decision making

    Is there a problem?

    Middle School with 500 students (Dec 04-05)


    Advanced data based decision making

    Is there a problem?

    Middle School with 500 students (Feb 3, 04-05)


    2 classroom system if

    >60% of referrals come from classroom

    >50% of ODR come from <10% of classrooms

    Several teachers not writing referrals at all

    Enhance universal &/or targeted classroom management practices

    Examine academic engagement & success

    Teach, precorrect for, & positively reinforce expected classroom behavior & routines

    2. Classroom system if…


    3 non classroom systems if

    >35% of referrals come from non-classroom settings

    >15% of students referred from non-classroom settings

    Enhance universal behavior management practices

    teach, precorrect for, & positively reinforce expected behavior & routines

    increase active supervision (move, scan, interact)

    3. Non-classroom systems if…


    Referrals by location

    Referrals by Location


    Advanced data based decision making

    Middle School


    Advanced data based decision making

    Elementary School


    Referrals by time

    Referrals by Time


    4 targeted group interventions if

    >10-15 students receive >5 ODR

    Provide functional assessment-based, but group-based targeted interventions

    Standardize & increase daily monitoring, opportunities & frequency of positive reinforcement

    4. Targeted group interventions if….


    5 individualized action team system if

    <10 students with >10 ODR

    <10 students continue rate of referrals after receiving targeted group support

    Provide highly individualized functional-assessment-based behavior support planning

    5. Individualized action team system if...


    Referrals by student

    Referrals by Student


    Student referral report

    Student Referral Report


    School example

    School Example

    A middle school getting ready to implement targeted group interventions. They had been implementing school-wide interventions for one school year.


    Some questions abc middle school had re student needs

    Some Questions ABC Middle School had re: student needs

    • How many students in the middle of the triangle?

    • How many need at the top of the triangle?

    • How many students in the targeted group have 2, 3, 4, 5, thru 25 referrals?

    • What types of behaviors are targeted group students and tip of triangle students engaging in?

    • What percent of students in targeted and tip are Sped?

    • What percent of students in targeted and tip met AYP the previous year?


    Abc middle school

    ABC Middle School

    • 541 students

    • 1314 total number of referrals for SY 04-05

    • Pre and Post Set completed

    • Team attended 4 PBS trainings throughout year and implemented along the way

    • Team leader attended district leadership meeting consistently throughout year


    Triangle data

    Triangle Data

    • 0-1 referral: 381 (65% of students)

    • 2-5 referrals: 124 (21% of students)

    • 6+ referrals: 82 (14% of students)


    Break down of all referrals 1314 by behaviors

    Break down of all referrals (1314) by behaviors

    • Disrespect: 354

    • Disruption: 310

    • Tardy: 274

    • Inappropriate Language: 80

    • Fighting/Aggression: 73

    • Skip: 56

    • Harassment: 37

    • Theft: 28

    • Other: 82

    • Miscellaneous (drugs, lying, prop. damage, weapons, vandalism): 20


    At risk group 2 5 referrals 409 referrals generated by this group 31 of all referrals

    ‘At-Risk’ Group = 2-5 referrals409 referrals generated by this group (31% of all referrals)

    • Students with

      • 2 referrals: 46

      • 3 referrals: 37

      • 4 referrals: 22

      • 5 referrals: 19

        _______

        Total 124 students [12 (10%) on IEPs]


    Sample of behaviors from at risk group 8 students total 4 boys 4 girls 2 on ieps

    Sample of Behaviors from ‘At-Risk’ Group – 8 students total, 4 boys/4 girls, 2 on IEPs,

    • Tardy: 9

    • Disruption: 8

    • Disrespect: 3

    • Other (gum chewing): 3

    • Aggression/Fighting: 1

    • Combustible: 1

    • Tobacco: 1

    • Weapons: 1

    • Drugs: 1

      _________

      Total = 28 referrals


    Tip of triangle 6 referrals 859 referrals generated by this group 65 of all referrals

    Students with

    6 referrals: 11

    7 referrals: 11

    8 referrals: 9

    9 referrals: 9

    10 referrals: 11

    11 referrals: 4

    12 referrals: 7

    Students with

    13 referrals: 3

    14 referrals: 5

    15 referrals: 5

    17 referrals: 3

    18 referrals: 2

    19 referrals: 1

    24 referrals: 1

    _________

    Total = 82 students [18 (22%) on IEPs]

    ‘Tip of Triangle’ – 6+ referrals859 referrals generated by this group (65% of all referrals)


    Abc ms sped students and ayp

    ABC MS –SPED Students and AYP

    • 78 students in special education (14% of student body)

      • 39 students (50%) of sped students with 2 or more ODRs

        • 38 students (97%) of sped students with 2 or more ODRs did not meet AYP in 1 or more subjects


    Additional information

    Additional Information

    • Ethnicity Reports

      • Available on SWIS and eSIS

    • Special Education


    Ethnicity reports

    Ethnicity Reports

    • Rationale

      • The power of information

      • The risks and ethics of dis-proportionality

    • Format

      • Multiple reports are needed for decision-making

      • SWIS currently provides the numbers and output.


    Ethnicity reports1

    Ethnicity Reports

    • Key Questions

      • What proportion of enrolled students in school are from each ethnicity?

      • What proportion of referrals are contributed from students in each ethnicity?

      • What proportion of students with at least one referral are from each ethnicity?

      • What proportion of students within each ethnicity have received at least one office discipline referral?

    Ethnicity #2

    Ethnicity #3

    Ethnicity #1


    Advanced data based decision making

    • Data are good…but only as good as systems in place for

      • PBS

      • Collecting & summarizing

      • Analyzing

      • Decision making, action planning, & sustained implementation


    Monthly e mails december

    Monthly E-mails (December)

    • Dear Staff,

    • Thought I’d send this along before we go home ‘till 1999.

    • Through 11/30/98 there were 179 referrals involving 62 students (6.7%). 858 students (99.3%) have no referrals.

    • 27 students (2.9%) are responsible for 80% of all referrals through 11/30. The top 13 have earned 59% of the referrals.

    • Thank you for your efforts this fall in helping to carry a positive surge in momentum through the year’s end. Have a refreshing break.

    • Happiest Holiday Wishes!


    Monthly e mails february

    Monthly E-mails (February)

    • Ever have that feeling like you wondered if someone had gotten the license plate of the truck that hit you? February had a bit of that feel to it. Approximately 1/3 of the year’s referrals to date (143 out of 457) took place in February…In perspective, the month was truly out of character with the rest of the year. Thank you for your perseverance.

    • 85% of our students continue their good work and have no referrals.

    • The 457 referrals (9/98-2/99) are down 22% from the 581 referrals last year.

    • In April we will be seeking staff input through our EBS survey to help build a focus for next school year. Keep up your good work--


    Summary

    Summary

    • Transform data into “information” that is used for decision-making

    • Present data within a process of problem solving.

      • Use the trouble-shooting tree logic

      • Big Five first (how much, who, what, where, when)

    • Data should be collected to answer specific questions

    • Ensure the accuracy and timeliness of data.


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