Tier 1 New Team Training Positive Behavioral Interventions & Supports TFI 1.12 Discipline Data 1.13 Data-based Decision Making

(1)

Tier 1 New Team Training Positive Behavioral

Interventions & Supports

TFI 1.12 Discipline Data 1.13 Data-based

Decision Making

(2)

TFI 1.12: Discipline Data TFI 1.13: Data-based Decision Making

EXPECTATI

ON BEHAVIOR

ResponsiBe ble

 Make yourself comfortable

 Take care of your needs (water, food, restroom, etc.)

 Share your questions with the group

Be

Respectf ul

 Turn cell phones off or to “vibrate”

 Listen to others attentively by

staying quiet while they are speaking

 Follow up, and complete assigned tasks

Be

Engaged

 Ask what you need to know to understand and contribute

 Contribute to the team by sharing relevant information and ideas

Learning Expectations

(3)

Module Organization

CORE CONTENT:

Definition, Rationale &

Examples

PRACTICE:

Activities for Fluency

SELF-ASSESSMENT:

Benchmarks of Quality

ACTION PLANNING:

Applying the core content to

(4)

Professional Learning Roadmap

Team

1.1 Team Composition

1.2 Team Operating Procedures

Implementation

1.3 Behavioral Expectations 1.4 Teaching Expectations

1.5 Problem Behavior Definitions 1.6 Discipline Policies

1.7 Professional Development 1.8 Classroom Procedures

1.9 Feedback and

Acknowledgement 1.1

0 Faculty Involvement 1.1

1

Student/Family/Community Involvement

Evaluation

1.1

2 Discipline Data 1.1

3

Data-based Decision Making

1.1

4 Fidelity Data 1.1

5

Annual Evaluation

(5)

Purpose:

Prepare and plan for facilitating implementation of Data Analysis

You should be able to:

 Use your data system to collect and analyze Office Discipline Report (ODR) data

 Collect additional data (attendance, grades,

faculty attendance, surveys) to use for problem solving

 Share data with team and faculty monthly

TFI 1.12 & 1.13 Outcomes

(6)

TFI Critical Element

1.12 Discipline Data:

Tier I team has instantaneous access to

graphed reports summarizing discipline data organized by the frequency of problem

behavior events by behavior, location, time of

day, and by individual student. NI/PI /FI

Priority

1.13 Data-based Decision Making: Tier I team reviews and uses discipline data and

academic outcome data (e.g. Curriculum-

Based Measures, state tests) at least monthly for decision-making.

Hig h

Me d

Lo w

Benchmark of Quality

Data system is used to collect and analyze Office

Discipline Report (ODR) data

Additional data are collected (attendance, grades, faculty attendance, surveys) and used by PBIS Team

Data analyzed at least monthly

Data shared with team and faculty monthly

(minimum)

Implement ation Driver

System is in place for gathering, summarizing, and

sharing SW data (e.g. data in graphic format) Disaggregate data to inform and monitor equitable

practices.

Outcomes

(7)

CORE CONTENT:

TFI 1.12: Discipline Data

TFI 1.13: Data-based Decision Making

(8)

 Data are the many sources of information we use to make decisions about how to allocate our

resources of time and attention for teaching,

redirecting, prompting, and reinforcing behaviors.

 Data come in many forms such as office referrals, attendance records, grades, surveys, verbal

feedback, and observations.

 Data must be documented, and shared to have the most power to shape action planning.

Definition

(9)

 Data allow us to look at a problem more objectively.

 Without data, we are more likely to make ambiguous, or emotionally driven

decisions.

 Data can be used for identifying and

planning to address problems, celebrating successes, and accountability.

Rationale

(10)

 Turn to your shoulder partner or discuss as a table

 What are difference sources of data you use in the classroom? The

schools?

 How confortable are you accessing, and interpreting these data?

Activity

(11)

 Data helps us ask the right questions…it does not provide the answers.

 Use data to:

 Identify problems

 Refine problems

 Define the questions that lead to solutions

Data helps place the “problem” in the context rather than in the students.

Why Use Data For Decision Making?

(12)

Data-based Decision Making

(13)

 Decisions are more likely to be

effective and efficient when they are based on data.

 The quality of decision making depends most on the first step—

defining the problem to be solved.

 Big Idea: Define problems with precision and clarity.

Data-based Decision

Making

(14)

 Data help us ask the right questions.

They do not provide the answers.

 We use data to:

 Identify & refine problems

 Define the questions that lead to solutions

 Data help place the “problem” in the context rather than on the students.

Data-based Decision

Making

(15)

 Right Data/Format/Time/People

 Right Questions

 Solution Development

& Action Planning

How Do We Make Data-

based Decisions?

(16)

Improving Decision Making

Proble m

Proble Solvinm

g

Solutio n

Action Plannin

g

Proble

m Solutio

n

(17)

 Right Data/Format/Time/People

 What are the right data?

 What would be the right format?

 When should we look at the data?

 Who are the right people to be

discussing and using this data to address issues?

The Right Things for the Right Decisions

(18)

 The statement of a problem is important for team-based problem solving.

 Everyone must be working on the same problem with the same assumptions.

 Problems often are framed in “primary” form.

That form raises awareness and concern, but is not useful for problem solving.

 Frame primary problems based on initial review of data

 Use a more detailed review of the data to build precise problem statements which are solvable

Identifying the Problem

(19)

 What are the data we need for a decision?

 Precise problem statements include information about the following:

 What is the problem behavior?

 How often is the problem happening?

 Where is the problem happening?

 Who is engaged in the behavior?

 When is the problem most likely to occur?

 Why is the problem sustaining?

Ask the Right Questions

(20)

Primary vs. Precision Statements

Primary Statements Precision Statement

Too many referrals

There are more ODRs for

aggression on the playground than last year. These are most likely to occur during first

recess, with a large number of students, and the aggression is related to getting access to the new playground equipment.

September has more suspensions than last year

Gang behavior is increasing The cafeteria is out of control

Student disrespect is out of control

(21)

There are more ODRs for aggression on the playground than last year. These are

most likely to occur during first recess, with a large number of students, and the

aggression is related to getting access to the new playground equipment.

Example Precision Statements

Who?

What? When?

Where? Why?

More ODRs for aggression

On the playground

A large number of students

First recess

To get new playground equipment

(22)

 Goals allow you to analyze, monitor, and adjust professional practice.

 Reduce hallway ODRs by 50% per month (currently 24 per month average).

 Specific?

 Measureable?

 Achievable?

 Relevant?

 Timely?

Identify a Measureable Goal

(23)

 Goals allow you to analyze, monitor, and adjust professional practice.

 Reduce hallway ODRs by 50% per month (currently 24 per month average).

 Specific?

 Measureable?

 Achievable?

 Relevant?

 Timely?

Solution Development & Action Planning

(24)

 Prevention — How can we avoid the problem context?

 Teaching — How can we define, teach, and monitor what we want?

 Recognition — How can we build in systematic rewards for positive behavior?

 Extinction — How can we prevent problem behavior from being rewarded?

 Consequences — What are efficient, consistent consequences for problem behavior?

 Data — How will we collect and use data for evaluation?

(25)

TFI 1.12: Discipline Data

Solution

Component Action Step(s)

Prevention How can we avoid the problem context?

Ex: schedule lunch times, change lighting

Teaching How can we define, teach, and monitor what we want?

Ex: build “Quiet” curriculum, teach hallway expectations, buy decibel meter

Recognition

How can we build in systematic rewards for positive behavior?

Ex: 3 quiet days = 5 extra minutes of social time (at lunch or end of day)

Extinction How can we prevent problem behavior from being rewarded?

Ex: public posting of results Corrective

Consequence

What are efficient, consistent consequences for problem behavior?

Ex: continue current system (Major/Minor ODR) Implementation fidelity?

Ex: walkthrough reports, observations, self-assessments

(26)

 Are we collecting the right information? What, where, when, who (why?)

 Is data collection efficient?

 Less than 15 sec to fill out, less than 30 sec to enter

 Do we get data in the right format?

 Graphic format

 Do we get the data at the right time?

 Before and during meetings

 Data no more than 24 hours old

 Are data used for decision-making by all?

 Data presented to all faculty at least monthly

 Data available for whole school, small group and individual student evaluation

 Data collected on FIDELITY (what we do) as well as IMPACT (student behavior)

Do We Have an Efficient Data System?

(27)

(28)

Core SWIS Reports

(29)

Additional SWIS Reports

(30)

Additional SWIS Reports (cont.)

(31)

Additional SWIS Reports (cont.)

(32)

 Is there a Problem?

 What areas/systems are involved?

 Are there many students or a few involved?

 What kinds of problem behaviors are occurring?

 When are these problems likely to occur?

What is the most effective use of our resources to address this problem?

Data Analysis

(33)

Accurate and Consistent Input

(34)

 Consistent

 Every 24-48 hours

 Same people entering those data

 Train at least 3 people

 “Real time”

 Real time entry allows for real time look at those data

 Accountability

 Decision-making

Data Entry

(35)

 Other data can inform our behavioral supports:

 Attendance

 Student and teachers

 Grades

 Surveys

 Perception

Additional Data Sources

(36)

 Is there a Problem?

 What areas/systems are involved?

 Are there many students or a few involved?

 What kinds of problem behaviors are occurring?

 When are these problems likely to occur?

What is the most effective use of our resources to address this problem?

Data Analysis

(37)

(38)

 SHARE monthly

 Are these data accurate?

 Are we over writing, under writing?

 Are we being consistent in writing and definitions of behavior?

 Get feedback

 Communication is two way

 Stress to our staff the importance of accurate and consistent input

Data Sharing

(39)

PRACTICE:

(40)

 What is your elevator speech for why decisions should be data based in the school discipline context?

 What real world analogies can you generate to data based decision- making in schools?

Activity Rationale

(41)

Primary vs. Precision Statements

Primary Statements Precision Statement

Too many referrals

There are more ODRs for

aggression on the playground than last year. These are most likely to occur during first

recess, with a large number of students, and the aggression is related to getting access to the new playground equipment.

September has more suspensions than last year

Gang behavior is increasing The cafeteria is out of control

Student disrespect is out of control

(42)

There are more ODRs for aggression on the playground than last year. These are

most likely to occur during first recess, with a large number of students, and the

aggression is related to getting access to the new playground equipment.

Example Precision Statements

Who?

What? When?

Where? Why?

More ODRs for aggression

On the playground

A large number of students

First recess

To get new playground equipment

(43)

Primary

1. Too many kids are late on Monday morning.

2. Indoor recess is out of control.

3. The kinder hallway has way too much running.

Precision

 What?

 Where?

 When?

 Who?

 Why?

Activity: Precision

Statements

(44)

Solution

Component Action Step(s)

Prevention How can we avoid the problem context?

Ex: schedule lunch times, change lighting

Teaching How can we define, teach, and monitor what we want?

Ex: build “Quiet” curriculum, teach hallway expectations, buy decibel meter

Recognition

How can we build in systematic rewards for positive behavior?

Ex: 3 quiet days = 5 extra minutes of social time (at lunch or end of day)

Extinction How can we prevent problem behavior from being rewarded?

Ex: public posting of results Corrective

Consequence

What are efficient, consistent consequences for problem behavior?

Ex: continue current system (Major/Minor ODR) Data Collection

Implementation fidelity?

Ex: walkthrough reports, observations, self-assessments Impact on student outcomes?

Ex: SWIS ODR data

(45)

(46)

How prepared are you to use the self-assessment to create the action plan for this section?

Fidelity & Outcome Check

If you are below a five, what do you need to be more prepared?

 Data system is used to collect and analyze Office Discipline Report (ODR) data

 Additional data are collected (attendance, grades, faculty attendance, surveys) and used by PBIS Team

 Data analyzed at least monthly

 Data shared with team and faculty monthly (minimum)

 *System is in place for gathering, summarizing, and sharing SW data 9e.g. data in graphic format)

 Disaggregate data to inform and monitor equitable practices.

One to Five?

(47)

TFI Critical Element

1.12 Discipline Data:

Tier I team has instantaneous access to

graphed reports summarizing discipline data organized by the frequency of problem

behavior events by behavior, location, time of

day, and by individual student. NI/PI /FI

Priority

1.13 Data-based Decision Making: Tier I team reviews and uses discipline data and

academic outcome data (e.g. Curriculum-

Based Measures, state tests) at least monthly for decision-making.

Hig

h Me

d Lo w

Benchmark of Quality

Data system is used to collect and analyze Office

Discipline Report (ODR) data

Additional data are collected (attendance, grades, faculty attendance, surveys) and used by PBIS Team

Data analyzed at least monthly

Data shared with team and faculty monthly

(minimum)

Implement ation Driver

*System is in place for gathering, summarizing, and

sharing SW data 9e.g. data in graphic format) Disaggregate data to inform and monitor equitable

practices.

Outcomes

(48)

ACTION PLANNING:

Applying the core content to your school

(49)

WHAT NEEDS TO BE COMPLETED?

RESOUR CES NEEDED?

WHO? WHEN?

A.

B.

C.

Tier 1 New Team Training Positive Behavioral Interventions & Supports TFI 1.12 Discipline Data 1.13 Data-based Decision Making