Top Banner
2013-14 Fall RESA Training for School Counselors Linda H. Brannan NCDPI Consultant K-12 Student Support Services [email protected] Data! Data! Data! Find, Understand and Use Data to Develop Comprehensive School Counseling Programs
154

Data ! Data! Data!

Feb 22, 2016

Download

Documents

fionn

Data ! Data! Data! Find, Understand and Use Data to Develop Comprehensive School Counseling Programs. 2013-14 Fall RESA Training for School Counselors Linda H. Brannan NCDPI Consultant K-12 Student Support Services [email protected]. School Counseling Wikispace. - PowerPoint PPT Presentation
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Data ! Data! Data!

2013-14 Fall RESA Training for School Counselors

Linda H. BrannanNCDPI Consultant

K-12 Student Support [email protected]

Data! Data! Data!

Find, Understand and Use Data to Develop Comprehensive School

Counseling Programs

Page 2: Data ! Data! Data!

School Counseling Wikispace

http://schoolcounseling.ncdpi.wikispaces.net• Click Professional Development

• Click 2013-14 Professional Development Sessions

• Click Regional Fall RESA Trainings

• Day 1 Training

Page 3: Data ! Data! Data!

Presenters• Linda Brannan, NCDPI Educational Consultant - K-12 Student Support

Services

• Debora Williams, NCDPI Educational Consultant for Special Projects

• Amy Jablonski, NCDPI Educational Consultant for RtI

• Anne Nixon, NCDPI Educational Consultant for RtI

• Heather Reynolds, NCDPI Educational Consultant for PBIS

• Other Presenters across NC from RtI and PBIS

Page 4: Data ! Data! Data!

Training Purpose – Train the trainer

1. To understand how to develop a comprehensive school counseling program that meets the needs of all students using data-driven decisions

2. To move beyond simple collection of data to using the collected data to solve problems

3. To understand the tools within the NC Framework for School Counseling (ASCA National Model)

4. To understand the job description and the new School Counselor Evaluation

Page 5: Data ! Data! Data!

Now…. 21st Century School Counselor

Leadership

Advocacy

Collaboration“Implement a data driven, comprehensive, developmental school counseling program to promote systemic change to increase college and career readiness for all students.”

Page 6: Data ! Data! Data!

New Question for School Counselors

How are students different as a result of the School Counseling Program?

Page 7: Data ! Data! Data!

New School Counselor Evaluation (based on 2008 NC Professional Standards for SC)

Standard 1 – School counselors demonstrate leadership, advocacy, and collaboration.

Standard 2 – School counselors promote a respectful environment for a diverse population of students.

Standard 3 – School counselors understand and facilitate the implementation of a comprehensive school counseling program.

Standard 4 – School counselors promote learning for all students

Standard 5 – School counselors actively reflect on their practice.No Standard 6 or 8

Page 8: Data ! Data! Data!

School Counselor Job Description• Purpose – utilize leadership, advocacy and

collaboration to promote student success, provide preventive services, and respond to student needs

• Role – develop and deliver a comprehensive school counseling program that addresses academic, career and personal/social development for all students

Page 9: Data ! Data! Data!

Major FunctionsDuties & Responsibilities • Development & Management of comprehensive program to

support academic success

• Delivery of Comprehensive Services

– NC Guidance Essential Standards/Curriculum

– Individual Student Planning

– Preventive & Responsive Services

– System Support

• Accountability/Growth

Page 10: Data ! Data! Data!

School Counselor Job Description

Think Time

• Review the Job Description

• Jot down a few “aha’s”

• Share with a colleague

Page 11: Data ! Data! Data!

School Counselor Job Description

• New legislation reinforces job description– 80% of work time in direct support services to

students

– 20% in program support/management including “fair share” duties

– local LEAs will develop a plan to transition test coordinator duties from school counselors

Page 12: Data ! Data! Data!

New Legislation: G.S. §115C-316.1 “Duties of School Counselors”

• http://www.ncleg.net/Sessions/2013/Bills/Senate/PDF/S402v7.pdf

• “SC shall implement a comprehensive developmental school counseling program in their schools. Counselors shall spend at least 80% of work time in direct student services…”

Page 13: Data ! Data! Data!

Framework for NC School Counseling

Page 14: Data ! Data! Data!

Foundation• Beliefs, Vision, Mission• Program Focus/Goals:

Academics/Behavior/Career Use SMART Goals • Student Competencies: National Standards, NC

Guidance Essential Standards: Skills students should know and be able to do.

• Professional Competencies: NC Professional SC Standards , Job Description and School Counselor Evaluation

Page 15: Data ! Data! Data!

Program Focus/Goals

• Start with your School Improvement Plan

• Effective S.M.A.R.T. Goals:– Promote achievement, attendance, behavior,

and/or school safety

– Are based on school data

– Address school-wide data, policies and practices or address closing-the-gap issues

– Address student competencies

Page 16: Data ! Data! Data!

Framework for NC School Counseling

Page 17: Data ! Data! Data!

Management

Tools for Program Management:– Annual Agreement

– Action Plans

– Lesson Plans

– Calendars

– Advisory Council

• ASCA Tools: Competencies Assessment, Use of Time Assessment, Program Assessment, Annual Agreement, Action Plans, etc.

Page 18: Data ! Data! Data!

Calendars

• How detailed are your calendars?

• Can you see how you spend your time?

• Program Calendars - Monthly

• Individual Calendars - Weekly

Page 19: Data ! Data! Data!

Annual Agreement and Advisory Council

• Annual Agreement: – a tool to use with administrator/evaluator to

develop/refine the SC Program for the current year – use D3M

• Table Groups: Discuss how you may use this tool to communicate your program goals and meet criteria of SC Evaluation

• Advisory Council:– Group of stakeholders to INFORM your program

– This is your moment to SHINE

Page 20: Data ! Data! Data!

Example from a School District:Comprehensive School Counseling Plan & Agreement

Page 21: Data ! Data! Data!

Data & Action Plans• School Data Profile (p.66-67) reviews all sources of data

from your school to develop plans for Academic, Attendance, Behavior

• Three types of action plans from D3M:– Curriculum Action Plan – all students - Guidance

Essential Standards – collaborative work with PLCs and to fill gaps

– Closing-the Gap Action Plan – aligns with SIP - a “must-have” aligns with program goals

– Small Group Plan – specific need(s) of a few

Page 22: Data ! Data! Data!

Data, Data, Data

• Three types of data collected from Action Plans:– Process (what happened – what you did for

whom)

– Perception (what knowledge/skill/behavior was learned)

– Outcome (how were students different)

Page 23: Data ! Data! Data!

Framework for NC School Counseling

Page 24: Data ! Data! Data!

Delivery• 80% of time working with or for students

• Direct (with students)

– Core Curriculum (NC Guidance Essential Standards)

– Individual Student Planning

– Responsive Services

• Indirect (for students)

– Referrals

– Consultation

– Collaboration

• New Legislation: G.S. §115C-316.1 Section 8.35 “Duties of School Counselors”

(Page 136 for Use of Time)

Page 25: Data ! Data! Data!

Framework for NC School Counseling

Page 26: Data ! Data! Data!

Accountability

• Review and analyze Action Plans and Program Goals:– Data Analysis: Review Curriculum Results, Action Plan Results

Use of Time Analysis, School Data

– Program Results: Program Goal Results Reports

• Evaluation and Improvement – What worked and didn’t work? Continuous Improvement (Growth)

Growth model - like the DPI School Counselor Evaluation

Page 27: Data ! Data! Data!

Types of Data

Page 28: Data ! Data! Data!

Data/Assessment Tool Choice

Measurement of student growth; assessment tool choice is dependent on the purpose and use of measurement results- RtI Action Network

Page 29: Data ! Data! Data!
Page 30: Data ! Data! Data!

Types of Data from ASCA

Process Perception Outcome

What happened? What you did and for whom? What was learned?

How was a student different as a result of

the work?

Ex: Number of students you work with

during the year.

Ex: What did the students learn as a

result of the work you did?

Ex: How does this impact the larger data

sets (academics, behavior, attendance)?

Page 31: Data ! Data! Data!

Connecting the Data Sources

Process Perception Outcome

Surveyed 300 students in 8th grade about

bullying in the school

Survey following anti-bullying lessons

regarding changes in bullying behavior

Measure impact on Office Discipline

Referrals (ODR) in the area of bullying

Page 32: Data ! Data! Data!

Connecting the Data Sources

Process Perception Outcome

Made contact with the identified 100 students

in grades 9-12 who had 7 more absences

last year than the district allows.

Tracked attendance data in Home Base on a monthly basis to see

progress in attendance.

School Improvement Plan (SIP) goal is to reduce number of absences by 50%.

Measure reduction of absences at the end of

the semester. Compare to last year’s

data.

Page 33: Data ! Data! Data!

Connecting the Data Sources

Process Perception Outcome

Identify common reasons (ex: 90% of last year’s dropouts were teen parents).

Give survey to 30 teen parents to identify

barriers to completing school.

School Improvement Plan (SIP) goal is to

reduce number students dropping out

(ex: 40 students).

Number of teen parents dropping out.

Attendance data

Give survey after strategies/supports are

given to determine reduction of barriers .

Monitor class attendance and

reasons for absences (ex: child care issues).

Identify current teen parents:

30 students in grades 9-12.

Page 34: Data ! Data! Data!

Types of DataNumber of students you are working with throughout the year in various groups and settings – Classroom Guidance/Small Groups/Closing the Gap GoalsIndividual Think Time

• How do you track this?

• What method do you utilize to identify students to work with?

• Write this on a sticky note.

Process

Page 35: Data ! Data! Data!

Types of Data

Data collected to measure how student learning or behavior has changed as a result of the implemented strategies

Individual Think Time

• How do you determine this?

• What tools do you utilize to capture what was learned?

• Write this on a sticky note.

Perception

Page 36: Data ! Data! Data!

Types of DataData collected to show the impact of your work with the students in connection to the SIP such as with the Closing the Gap Goals(ex: academic, attendance, behavior – reflects at end of year in your Outcome/Results Reports)

Individual Think Time

• What tools do you utilize to capture what was learned?

• How do you link it back to the School Improvement Plan?

• Write this on a sticky note.

Outcome

Page 37: Data ! Data! Data!

Networking

• Find someone in the room from another school district

• Introduce yourselves and discuss process, perception, and outcome sticky notes and the connections between the three types of data

Page 38: Data ! Data! Data!

Accountability - Data Sources & UsageWhat do you do now?

Page 39: Data ! Data! Data!

Activity

• Utilize the blank data worksheet to list the data sources you utilize.

• Think about additional data sources you can use

Page 40: Data ! Data! Data!

Curriculum Action Plan

• Do you know what lessons you will be teaching at the beginning of the year?

• Do you plan a scope of learning for your program for the various grade levels?

• Use the curriculum action plan

At your table groups:– Pick ONE grade level and talk about how you could

collect process, perception, and outcome data over the course of the year.

Page 41: Data ! Data! Data!

Small Group

• The power of setting goals for your groups.

At your table groups:– Pick one type small group or that you run and

talk about the three types of data.

Page 42: Data ! Data! Data!

Types of Data

Page 43: Data ! Data! Data!

Graduation Rate = 82.5%

http://www.newsobserver.com/2013/08/08/3092325/nc-graduation-rate-rises-above.html

Page 44: Data ! Data! Data!

DROPOUT PREVENTION EARLY WARNING REPORTS

School Counseling/GuidanceFall Regional RESA Trainings

Presenters: Debora Williams and Betsy Baugess

Page 45: Data ! Data! Data!

Do You Know?

Annual Number of Dropouts in U.S.

Page 46: Data ! Data! Data!

High School Dropout Statistics

Annual number of high school dropouts in U.S.

3,000,000+

http://www.statisticbrain.com/high-school-dropout-statistics/

Page 47: Data ! Data! Data!

Do You Know?

Number of dropouts in North Carolina (2012)

Page 48: Data ! Data! Data!

High School Dropout Statistics

Number of students dropping out of school in North Carolina (2012)

13,488Grades 9-12

http://www.ncpublicschools.org/docs/research/discipline/reports/consolidated/2011-12/consolidated-report.pdf

Page 49: Data ! Data! Data!

Do You Know?

Number of NC students retained in 3rd Grade

(2012)

Page 50: Data ! Data! Data!

Do You Know?

32,000(2012)

Page 51: Data ! Data! Data!

PURPOSE

To facilitate an early warning system with research-based risk factors for identifying students who may be at risk of

dropping out of school.

Page 52: Data ! Data! Data!

RESEARCH ONEARLY WARNING INDICATORS

On-track CreditsAn on-track student has accumulated the requisite number of credits to move to the next grade level

and no more than one semester “F” in a core subject. (Allensworth, 2009)

AttendanceEight times more predictive of failure than prior test scores. A student who has more than 5 absences

in one semester of the 9th grade year has a 63% chance of graduating in four years. (Allensworth, 2009) In North Carolina, attendance issues were the most often noted for a reported dropout, accounting for 41.5% of all dropouts.

GPAStudents with a “B” (3.0) average or higher in the first year have better than a 93% chance of

graduating; students with a D+/C- (1.0-2.5 GPA) average have a 53-92% chance of graduating; students with less than a “D” average (1.0) have less than a 6% chance of graduating. (Allensworth, 2009)

Page 53: Data ! Data! Data!

RELEVANT SIS DATA• Attendance data, both current and historical

• State assessment achievement level data

• Final marks for all middle school/high school courses

• Most recently calculated GPA (calculated at year-end for grades 9-12)

• Student name, pupil number, birth date, ninth-grade entry date

Page 54: Data ! Data! Data!

PowerSchool At Risk Report

The At Risk Report provides a listing of courses, sections,

and grades associated with students who are currently a

risk of failing for the current term.

Page 55: Data ! Data! Data!

PowerSchool At Risk Report

Allows administrators and teachers to take a proactive

approach to address potential issues and ensure student

accountability.

Page 56: Data ! Data! Data!

At Risk Report Fields

• Attendance Mode• Daily• Meeting• Time

• Students to Include• Selected students only• All students

Page 57: Data ! Data! Data!

At Risk Report FieldsAttendance Codes

• ALL CODES• Illness or Injury• Medical/Dental Appt• Death in Family• Quarantine• Court/Admin Proc• Religious Observance• Educational Opportunity• Teacher-in-Treatment

• Local School Bd Policy• Child Care• Excused Tardy• Medically Fragile• Deployment Activity• Unexcused Absence• Unex No Immunization• Unexcused Tardy• Suspensions

Page 58: Data ! Data! Data!

Attendance Codes

Page 59: Data ! Data! Data!

At Risk Report Fields• Reporting Segment or Begin Date and Ending Date

• Reporting Segment• Chose from reporting segment from the pop-up menu

• Begin Date and Ending Date• Specify a date range in using mm/dd/yyyy OR mm-dd-yyy• Failure to use this format issues an alert• Date must fall within the selected school year

Page 60: Data ! Data! Data!

At Risk Report – Data to be Filled? Use Attendance Report Query

• Enter minimum number of occurrences of selected attendance codes

? Use Grades Report Query• Uses three Grade fields to query students for the report

• Select final grade type (Stored or Current)• Minimum number of classes with failing grades

? Use Discipline Report Query• Discipline/incident check inn PowerSchool does NOT consider the

PowerSchool discipline/incident functionality that NC will be using. This section should not be used at this time.

Page 61: Data ! Data! Data!

\

Page 62: Data ! Data! Data!
Page 63: Data ! Data! Data!

At Risk Report

Page 64: Data ! Data! Data!

NCWISE REPORTS

• Reporting Hub Accessible to NC WISE School Data Managers, the LEA Coordinators, Principals, and Others

• Four reports available on NCWISE Reporting Hub• 9th Grade Dropout Early Warning Report • 10th Grade Dropout Early Warning Report• 11th Grade Dropout Early Warning Report• 12th Grade Dropout Early Warning Report

• Most data is collected once after Year-End-Transition

Page 65: Data ! Data! Data!

NCWISE REPORTS(continued)

• Reports are designed for availability just prior to the start of school allowing counselors to identify the “at risk” students.

• Reports analyze the student’s historical data.

• Executed at the beginning of school year for identification of individual student issues.

Page 66: Data ! Data! Data!

GR Risk Indicators• Attendance through middle/high school

• Final course marks in key elementary/middle school courses

• Successful completion of high school courses essential to graduation within an appropriate timeframe, as indicated by state assessments

• Cumulative credits earned in each high school year

• Continuous cumulative grade point average

Page 67: Data ! Data! Data!

Report OrganizationFour available reports

• A ninth grade report, to be run at the start of ninth grade, analyzes the previous 3 years

• A tenth grade report, to be run at the start of tenth grade, considering ninth grade data

• An eleventh grade report, to be run at the start of eleventh grade, considering tenth grade data

• An twelfth grade report, to be run at the start of twelfth grade, considering eleventh grade data

Page 68: Data ! Data! Data!

Risk CriteriaNinth grade report

• Final grade for standard required Math course (grades 6,7,8) < C

• Final grade for standard required English course (grades 6,7,8) < C

All reports• Total daily absences, in any one school year, exceed 5

daily absences

Page 69: Data ! Data! Data!

Tenth/Eleventh/Twelfth Grade Risk Criteria

• GPA < 2.0

• Achievement Level of I or II for EOCs • English I• Algebra I

• Failure to achieve an expected level of course credits after each year in high school• At beginning of grade 10, less than 5 earned credits• At beginning of grade 11, less than 10 earned credits• At beginning of grade 12, less than 15 earned credits

Page 70: Data ! Data! Data!

Report Details

• All reports are school level reports• Each report only contains students “at risk” for dropout

• Each student has only one record in the report

• Each column of the report represents a potential dropout risk factor

Page 71: Data ! Data! Data!

Report Details (continued)

• If there is no data for a particular risk factor, the column for that factor indicates “no data”

• Risk factor is calculated using only the available data points

• There is no variable weighting factor. All risk factors carry the same weight.

• Risk factor must be .25% or above for the student to be included in the report

Page 72: Data ! Data! Data!

Ninth Grade Sample ReportSCHOOL NAME

PUPIL#

9thGradeEntryDate

BIRTH_DATE

STUDENTName

SIXth _GrdABS

SEVNGrdABS

EIGHTGrdABS

SIXthGrdENGL

SEVNGrdENGL

EIGHTGrdENGL

SIXthGrdMATH

SEVNGrdMATH

EIGHTGrdMATH

RISK

NC WISE HS 1 7/1/2010 6/30/1995 Student1 2 8 5 C+ D F D- F D 0.67

NC WISE HS 2 7/1/2011 7/12/1996 Student2 14 3 28 no data C F 80 C D- 0.5

NC WISE HS 3 7/1/2011 9/20/1996 Student3 16 6 0 no data B A- no data B- A- 0.29

NC WISE HS 4 7/1/2010 8/29/1993 Student4 no data 6 18 no data no data F no data P F 0.8

NC WISE HS 5 7/1/2010 4/10/1995 Student5 9 24 no data D no data no data F no data no data 1

NC WISE HS 6 7/1/2010 8/18/1995 Student6 3 5 5 C+ D F C F D 0.44

NC WISE HS 7 7/1/2011 3/20/1996 Student7 1 4 13 A C- B- A C- C 0.33

NC WISE HS 8 7/1/2010 2/20/1995 Student8 5 9 13 C- B- F C+ B- D 0.56

NC WISE HS 9 7/1/2011 8/23/1996 Student9 7 6 6 A C D C C C 0.44

NC WISE HS 10 7/1/2011 2/18/1996 Student10 no data 9 7 no data 94 92 no data 94 92 0.33

Page 73: Data ! Data! Data!

Example: Student #1• Rising ninth grader

• 2 absences in sixth grade • 8 absences in seventh grade - risk factor• 5 absences in eighth grade • Sixth grade English final mark is “C+”• Seventh grade English final mark is “D” - risk factor• Eighth grade English final mark is “F” - risk factor• Sixth grade math final mark is “D-” – risk factor• Seventh grade math final mark is “F” – risk factor• Eighth grade math final mark is “D” – risk factor

• RISK FACTOR is 67% (6 of 9 factors qualify as risks)

Page 74: Data ! Data! Data!

Twelfth Grade Sample Report

http://dpi.state.nc.us/docs/graduate/resiliency/sample-report.pdf

Page 75: Data ! Data! Data!

Contacts• Debora Williams, Special Assistant Graduation Initiatives (919) 807-3912 [email protected]

• Betsy Baugess, Manager Data/Software & Reporting (919) 807-3276 [email protected]

Page 76: Data ! Data! Data!

Data-Based Decision Making Using the TIPS model

Page 77: Data ! Data! Data!

Desired Outcomes

The goal of this training is to help schools move beyond simple collection of data to using the collected data to solve problems.

Page 78: Data ! Data! Data!

RationaleData should be used regularly to solve problems,

because it…

• provides a more objective viewpoint of the current state.

• increases efficiency by making needed action items clear.

• provides information about effectiveness of practices so modifications can be made in a timely manner.

• ensures that strategies and interventions are based on precise problem statements.

Page 79: Data ! Data! Data!

Data-Based Decision Making

Problem-Solving Process

80

Page 80: Data ! Data! Data!

Problem-Solving Process: DefinitionsProblem: Difference exists between

expected/desired student behavior and current student behavior.

Problem identification: Difference is discovered and significance is determined.

Problem-solving: A plan is created to reduce or eliminate difference.

Page 81: Data ! Data! Data!
Page 82: Data ! Data! Data!

Data-Based Decision Making

Meeting Foundations

Page 83: Data ! Data! Data!

Meeting Foundations: Elements

• Team purpose • Defined agreements about processes• Established roles and responsibilities• Electronic meeting minutes

Page 84: Data ! Data! Data!

Meeting Foundations: Overview

1. Meeting starts and ends on time

2. Consistent attendance by team members

3. Agenda is used to guide meeting topics

4. Process is in place to monitor progress of implemented solutions (review previous meeting minutes)

5. System is used for documenting decisions

6. Next Meeting is scheduled

Page 85: Data ! Data! Data!

Meeting Foundations: Overview7. Team members prepare for and meet

responsibilities during meeting

8. All team members (absent or present) are given minutes within 24 hours of the meeting

9. Decision makers are present when needed

10.Protocol is established for when administrator is unable to attend

11.Efforts are making a difference in the lives of children/students

Page 86: Data ! Data! Data!

Meeting Foundations: Team Purpose

District

School

Grade/Department

Individual Student

District implementationDistrict-wide problem-solvingProvide support to schools

School implementationSchool-wide problem-solving

Provide support to staff

Grade/Department (PLC?) problem-solving

Individual student problem-solving

Page 87: Data ! Data! Data!

Meeting Foundations: Defined Processes • Meeting dates/times

• Storage of information

• Contact information

• Ground rules/agreements/norms

1. Inform facilitator of attendance issues before meeting

2. Avoid side talk3. Remind each other to stay focused4. Start and end on time5. Be an active participant

Example

Page 88: Data ! Data! Data!

Meeting Foundations: Roles

• Core roles– Facilitator– Minutes taker– Data analyst– Time keeper

– Administrator

– Active team members

Are all skill sets for problem solving present or easily accessible?

Can one person serve multiple roles?

Are there other roles needed? EX: communication coordinator, timekeeper

Are team members culturally responsive?

• Backup for each role

• RtI Coach

• PBIS team member

Page 89: Data ! Data! Data!

Meeting Foundations: Minutes (Documentation)

Agenda •today’s items

•next week’s items

•Discussion items, decisions made, tasks and timelines assigned

•Problem statements, solutions/decisions/tasks, responsible people, timelines assigned, and an evaluation plan

Logistics date

time

location

roles

Page 90: Data ! Data! Data!

Meeting Foundations: Minutes (Review)

Meeting minutes An effective process

check

Baseline for current meeting

Visual tracking of focus topics during and after meetings

Prevents side conversations

Prevents repetition

Encourages completion of tasks

Page 91: Data ! Data! Data!

Meeting Foundations: Environment

92

Problem

SolutionOut of

Time

Use Data

A key to collective problem solving is to provide a visual context that allows everyone to follow and contribute.

Consider using one form to guide the activities of the meeting and using a projector so that all team members view the content and participate.

Page 92: Data ! Data! Data!

Meeting Foundations: Structure/Flow1. Attendance, roles for meeting

2. Next meeting date

3. Review agenda for meeting

4. Review/status update of previous meeting minutes

5. Review data and use problem solving model to prompt the development of a comprehension action plan

6. Reports needed for next meeting

7. Team assessment of meeting

8. Dissemination of meeting minutes

Page 93: Data ! Data! Data!

Data-Based Decision Making

Identify Problems

Page 94: Data ! Data! Data!
Page 95: Data ! Data! Data!

Identify Problem(s)

• Behavioral/academic definition

• Concrete, observable and measurable

• Stranger test?

• Typically begins broad

Page 96: Data ! Data! Data!

Data-Based Decision Baking

Develop Hypothesis

Page 97: Data ! Data! Data!
Page 98: Data ! Data! Data!

Develop and Test Hypothesis: Determine Cause of Problem

• Determining the cause, or uncovering why a problem is occurring, is essential to developing interventions.

• The goal is to help students be successful.

• When collecting data, it is important for staff to make their best, most informed guesses about why students are struggling.

Page 99: Data ! Data! Data!

Develop and Test Hypothesis: ICEL

• We must ask questions to form a hypothesis regarding “What is the problem? Why is it occurring?”

• We ask questions across four domains.

Instruction Curriculum Environment Learner

Domains within our control

Page 100: Data ! Data! Data!

Instruction

Review

Interview

Observe

Test

Curriculum

Review

Interview

Observe

Test

Environment

Review

Interview

Observe

Test

Learner

Review

Interview

Observe

Test

Develop and Test Hypothesis

Page 101: Data ! Data! Data!

Develop and Test Hypothesis

Review

Curricular materials

Curriculum Maps

Etc…

Interview

Previous Teachers

Current Teachers

Etc. . .

Observe

Teacher’s instructional

strategies

Instructional Time

Etc. . .

Test

Group Diagnostic

Test

Individual Testing data

Skill specific test on sample

of students

Etc. . .

Examples to Consider

Page 102: Data ! Data! Data!

Develop and Test Hypothesis: Things to Consider• A hypothesis is an explanation for what the data

and your experience tell you.

• Use multiple sources of data.

• Staff with the most direct experience with the problem need to add their insight to accurately define cause.

• An accurate hypothesis is crucial to designing solutions that will be effective.

Page 103: Data ! Data! Data!

Develop and Test Hypothesis

ACCURATELY

DEFINED PROBLEM

ACCURATE HYPOTHES

IS

PRECISE PROBLEM STATEMEN

T

Page 104: Data ! Data! Data!

Activity: Identifying Precise Problem Statement

Which partial statement is more precise?

Too many ODRs 15 instances of disrespect

24 ODRs between 1:00 and 1:30 Too many ODRs in the afternoon

Too many ODRs outside the classroom

25% of students have at least 2 ODRs

Too many ODRs on the playground

6 ODRs on the playground

Many students have ODRs

Total of 12 ODRs for aggression on the playground in the last month; this number is more than last year and shows an increasing trend for this year; these incidents are occurring during the first recess, and there are different students involved each time. The curriculum and instruction used to teach playground expectations do not address alternatives to aggression.

Which statement is a complete Precise Problem Statement?

Page 105: Data ! Data! Data!

Data-Based Decision MakingDiscuss and Select Solutions

Page 106: Data ! Data! Data!
Page 107: Data ! Data! Data!

Discuss and Select Solutions

• Solution should be based on the precise problem statement which is developed around instruction, curriculum, environment and/or learner.

• Precise, clear goal must be set before plan implementation.

• Goal should be a specific description of desired change in student behavior.

• Goal should be a SMART goal.

Page 108: Data ! Data! Data!

Discuss and Select Solutions

S- Specific

M- Measurable

A- Attainable

R- Relevant

T- Time-Bound

Page 109: Data ! Data! Data!

Activity: SMART Practice

•List 5 items on your “Bucket List.”

•Write a SMART goal for one of your items.Is it Specific?Is it Measureable? Is it Attainable?Is it Relevant?Is it Time-bound?

Ex: After training for twelve weeks, I will complete the Outer Banks Half-Marathon on November 10, 2013.

Page 110: Data ! Data! Data!

Data-Based Decision MakingDevelop Action Plan

Page 111: Data ! Data! Data!
Page 112: Data ! Data! Data!

Develop and Implement Action Plan: Design• The most effective plans utilize multiple strategies

that affect all causes of the problem.

• To ensure fidelity of implementation, specific tasks need to be assigned to individuals with a timeline for checking back.

• Change takes time, so implement the plan for a set amount of time (dependent on complexity and intensity of problem) before evaluating.

Page 113: Data ! Data! Data!

Develop and Implement Action Plan: Design

Data Decision Guidelines• Intensity of plan• Frequency of monitoring• Tool(s) being utilized

How will we determine success along the way?

Page 114: Data ! Data! Data!

Develop and Implement Action Plan: After Creation

• Once strategies are selected, specific tasks need to be delegated and tracked using an action plan.

• Action plans should drive the creation of future meeting agendas as well as generate clear action steps at the end of each meeting.

• Effective action plans are:• Used regularly• Frequently reviewed and updated• Accessible to all staff involved in implementing plan• Made of specific, manageable action steps with clear timelines• Developed using data from staff and teams

Page 115: Data ! Data! Data!

Fidelity

Use weekly 1-5 survey from teachers to assess implementation of plan.

Newton, J.S., Todd, A. W., Horner, R.H., Algozzine, B., & Algozzine K., 2010

Are we doing the plan?

1 ….. 2 …..3 ….. 4 ….. 5No Yes

Develop and Implement Action Plan: Ensuring Fidelity

Page 116: Data ! Data! Data!

Data-Based Decision MakingEvaluate and Revise Action Plan

Page 117: Data ! Data! Data!
Page 118: Data ! Data! Data!

Evaluate and Revise Action Plan

Review data to answer the following question:

Has the goal been met?

• If yes, explore the following options:

1. Increase goal for the same problem.

2. Choose another problem to address.

3. Gradually fade plan.

Page 119: Data ! Data! Data!

Evaluate and Revise Action Plan

Review data to answer the following question:

Has the goal been met?

•If no, explore the following options:

1. Evaluate fidelity of implementation.

2. Assess problem identification.

3. Consider the accuracy of the hypothesis.

4. Alter the timeline.

5. Find ways to adjust the solutions.

6. Review “like peer” data.

Page 120: Data ! Data! Data!

Making Connections• Evaluate

and Revise Action Plan

Connecting Types of Data within TIPS

Page 121: Data ! Data! Data!

Where do you see utilizing PERCEPTION data in this model

Where do you see utilizing PROCESS data in this modelWhere do you see utilizing

OUTCOME data in this model

Page 122: Data ! Data! Data!

Data-Based Decision Making

School-Wide Case Study

The following activities work best when sitting in small groups based on building level (ex: elementary, middle, high school)

Page 123: Data ! Data! Data!

Case Study: Context

• Mid year school improvement team (SIP) meeting

• Academic and behavior outcome data is presented

Page 124: Data ! Data! Data!
Page 125: Data ! Data! Data!

School-Wide Case Study Data

Fall Winter0

10

20

30

40

50

60

70

80

90

100

6560

10

27

Academic and Discipline Data

Academic Data

Discipline Data

As a team choose a grade level and academic area to apply to this data set.

Page 126: Data ! Data! Data!

Activity: Identify Problems in School-Wide Case Study

• Note discrepancies between what is desired and what exists.

• Identify any patterns or trends.

• Is there a problem?

• As a team, agree on the problem statement

Page 127: Data ! Data! Data!
Page 128: Data ! Data! Data!

Instruction

Review

Interview

Observe

Test

Curriculum

Review

Interview

Observe

Test

Environment

Review

Interview

Observe

Test

Learner

Review

Interview

Observe

Test

School-Wide Case Study:Develop Hypothesis

Page 129: Data ! Data! Data!

Activity: Develop the Hypothesis for School-Wide Case Study

• As a team, develop the hypothesis(es) by considering Instruction, Curriculum, Environment and (ICE).

• Plan how to Review, Interview, Observe and/or Test (RIOT) in order to test your hypothesis(es).

• Be prepared to share with the group.

Page 130: Data ! Data! Data!

School-Wide Case Study: Digging DeeperWhat questions do you have about instruction, curriculum and environment for this grade level?

Instruction

Curriculum

Environment

Handout

Page 131: Data ! Data! Data!
Page 132: Data ! Data! Data!

Activity: Discuss and Select Solutions for School-Wide Case Study133

• Discuss and select solutions connected to your precise problem statement.

• Create a goal for possible solutions.

• Ensure the creation of a SMART goal by asking these questions:Is it Specific?Is it Measureable? Is it Attainable?Is it Relevant?Is it Time-bound?

Page 133: Data ! Data! Data!
Page 134: Data ! Data! Data!

Activity: Develop and Implement Action Plan for School-Wide Case Study• Create some action items for this sample team.

• Do your action items include a time/date for completion?

• Do your action items name the person(s) responsible?

• Is there a way to measure fidelity of implementation?

• Determine procedure for progress monitoring (tools and frequency).

Page 135: Data ! Data! Data!
Page 136: Data ! Data! Data!

Evaluate and Revise Action Plan of the School-Wide Case Study

If yes, explore the following options:

1. Increase goal for the same problem.

2. Choose another problem to address.

3. Gradually fade use of the plan or convert successful plans into policy/procedure/practice.

If no, explore the following options:

1.Evaluate fidelity of implementation.

2.Assess problem identification.

3.Consider the accuracy of the hypothesis.

4.Alter the timeline.

5.Find ways to adjust the solutions.

6.Review “like peer” data.

Use the data to answer: Has the goal been met?

Page 137: Data ! Data! Data!

Closing the Gap Action Plan

• These plans use D3M, support the SIP and support SC Program Goals in Foundation.

• At your table groups

– Take the TIPS Problem/Goal your group just created in the School-wide Case Study to develop a School Counseling Closing the Gap Action Plan

Page 138: Data ! Data! Data!

Data-Based Decision Making

Individual Student Case Study

Page 139: Data ! Data! Data!
Page 140: Data ! Data! Data!
Page 141: Data ! Data! Data!

Individual Student Data - Jessica

• Attendance issues: has missed 12/40 school days

• At risk to fail math: has failed first 2 tests and turned in 5 assignments late, initial benchmark scores were 30% below the target score (as established in HomeBase)

• Has been tardy to class 8 times

• 1 out-of-school suspension for unexcused absences

Page 142: Data ! Data! Data!

Individual Student Data - Johnnie

K - Spring 1st - Fall 1st - Winter

PSF 37 (40) 30 (40)

NWF 25 (28) 25 (27) 29 (43)

ORF 17 (23)

TRC C (D) C (D) D (G-H)

Black = Johnnie’s scoresRed = Benchmark scores

Attendance Office Referrals

Kindergarten 173/180 0First Grade 80/92 3 (disruptive behaviors)

Page 143: Data ! Data! Data!

Activity: Identify Problems in Individual Student Case Study

• Note discrepancies between what is desired and what exists.

• Identify any patterns or trends.

• Is there a problem?

• As a team, agree on the problems to be addressed first.

Page 144: Data ! Data! Data!
Page 145: Data ! Data! Data!

Activity: Individual Student Case Study Data: Digging Deeper

What questions do you have about instruction, curriculum, environment and the learner based on this data?

Instruction

Curriculum

Environment

Learner

Handout

Page 146: Data ! Data! Data!

Instruction

Review

Interview

Observe

Test

Curriculum

Review

Interview

Observe

Test

Environment

Review

Interview

Observe

Test

Learner

Review

Interview

Observe

Test

Individual Student Case Study:Develop Hypothesis

Page 147: Data ! Data! Data!

Activity: Develop the Hypothesis for Individual Student Case Study

148

• As a team, develop the hypothesis(es) by considering Instruction, Curriculum, Environment and Learner (ICEL).

• Plan how to Review, Interview, Observe and/or Test (RIOT) in order to test your hypothesis(es).

• Be prepared to share with the group.

Page 148: Data ! Data! Data!
Page 149: Data ! Data! Data!

Activity: Discuss and Select Solutions for Individual Case Study

150

• Discuss and select solutions connected to your precise problem statement.

• Create a goal for possible solutions.

• Ensure the creation of a SMART goal by asking these questions:Is it Specific?Is it Measureable? Is it Attainable?Is it Relevant?Is it Time-bound?

Page 150: Data ! Data! Data!
Page 151: Data ! Data! Data!

Activity: Develop and Implement Action Plan for Individual Student Case Study• Create some action items for this sample team.

• Do your action items include a time/date for completion?

• Do your action items name the person(s) responsible?

• Is there a way to measure fidelity of implementation?

• Determine procedure for progress monitoring (tools and frequency).

Page 152: Data ! Data! Data!
Page 153: Data ! Data! Data!

Evaluate and Revise Action Plan of the Individual Case Study

If yes, explore the following options:

1. Increase goal for the same problem.

2. Choose another problem to address.

3. Gradually fade use of the plan.

If no, explore the following options:

1.Evaluate fidelity of implementation.

2.Assess problem identification.

3.Consider the accuracy of the hypothesis.

4.Alter the timeline.

5.Find ways to adjust the solutions.

6.Review “like peer” data.

Use the data to answer: Has the goal been met?

Page 154: Data ! Data! Data!

Data-Based Decision Making

Questions?