Groton Data DayAccountability, Performance, and

Balanced Assessments

Facilitated by:

Neal CaponeDistrict Data Coordinator

CNYRIC

Agenda

• Grounding – Spring Synectic• Data Literacy – Accountability and Assessment• 3-8 ELA/Math Collaborative Learning Cycle

– Score Trend Comparison – Cohort Trend and Subgroup Performance

• Balanced Assessment– Rick Stiggens– Self-Evaluation

The Region Serviced by the CNYRIC

Data Flow

SIS (Student Management System)

PD Data System

IEP Direct

NutriKids/Transfinder

Level 2 Repository (SED)

Data Warehouse (Level 1)

Level 1 ContainerCOGNOS

DataMentor

nySTART

Synectic

What are some popular Spring Activities?

SYNECTIC

Data Analysis is like … because ...

Syn (bring together) Ectic (diverse elements)

Grounding Exercise

• Name• Position• Share your Synectic

“Using data effectively does not mean getting good at crunching numbers. It

means getting good at working together to gain insights from

student-assessment results and to use the insights to improve instruction.”

- Kathryn Boudett, Elizabeth City, & Richard Murnane, “When 19 Heads Are Better Than One,”

Education Week, December 7, 2005.

http://www.notonthetest.com/

Word Splash

Work with a partner to define as many terms as you can on the Word Splash

Data Warehouse

SIRS

NYSSIS

Continuous Enrollment

Performance IndexAYP

AMO

Effective AMO

NYSAA

Participation Rate

Accountability Subgroups

Safe Harbor

BEDS

NYSESLAT

Accountability Cohort

AVR

Graduation Cohort

COGNOS

Diff

eren

tiate

d A

ccou

ntab

ility

Triangulating Data

Word Splash

Sampling Principle

Summative AssessmentFormative Assessment

Scale Score --- Raw

Score

Standards-Referenced Test

Calculation of the Performance Index (PI)

Elementary-Middle Levels:PI = [(number of continuously enrolled tested students scoring at Levels 2, 3, and 4 + the number scoring at Levels 3 and 4) ÷ number of continuously enrolled tested students] X 100

Secondary Level:PI = [(number of cohort members scoring at Levels 2, 3, and 4 + the number scoring at Levels 3 and 4) ÷ number of cohort members] X 100

A Performance Index (PI) is a value from 0 to 200 that is assigned to an accountability group, indicating how that group performed on a required State test (or approved alternative) in English language arts, mathematics, or science. PIs are determined using the following equations:

Level 1: 5 students

Level 2: 15 students

Level 3: 45 students

Level 4: 10 students

PI = (15+45+10) + (45 + 10)

75

PI = 167

X 100

Measure/Purpose Cohort Used Standard/AMO Subgroup Accountability

Performance

All grade 3-8 students) or designated ungraded students) reported in the repository as continuously enrolled (one-year continuous enrollment = enrolled BEDS day through assessment dates)

English: PI of 167Math:PI of 152Science;PI of 100

30 or more students for ELA or Math

Participation Rate

All grade 3-8 students (or designated ungraded students) reported in the repository as enrolled during assessment administration and make-up dates

ELA and Math: 95%Science: 80% for “all students”

40 or more students for ELA or Math

2010-2011 Elementary/Middle Level Accountability

2010-2011 High School Accountability

Measure/Purpose Cohort Used Standard/AMO Subgroup Accountability

English and Math Performance

2007 Accountability Cohort (one-year continuous enrollment in fourth year of HS = enrolled BEDS day through June 30, 2011)

English: PI of 183Math:PI of 180

30 or more students for ELA or Math

English and Math Participation

All students reported in State Repository as enrolled in grade 12 on June 30, 2011 and students who graduated between July 1, 2010 and June 30, 2011

95% 40 or more students

Graduation Rate2006 Graduation-Rate Cohort (five months’ enrollment) including transfers to GED

80% for “all students”

An Effective AMO is the lowest PI that an accountability group of a given size can achieve in a subject for the group’s PI not to be considered significantly different from the AMO for that subject. If an accountability group's PI equals or exceeds the Effective AMO, the group is considered to have made AYP.

Effective AMOs

Further information about confidence intervals and Effective AMOs is available at:http://www.emsc.nysed.gov/irts/school-accountability/confidence-intervals.htm

2010–11 Safe Harbor Calculation for ELA and Math

Safe Harbor is an alternate means to demonstrate AYP for accountability groups whose PI is less than their Effective AMO. The Safe Harbor Target calculation for ELA and Math for 2010-11 using the 2009-10 PI is:

Safe Harbor Target = {2009-10 PI} + [(200 – {2009-10PI}) 0.10]*

For a group to make safe harbor in English or math, it must meet its Safe Harbor Target and also meet the science (at the elementary/middle level) or graduation rate (at the secondary level) qualification for safe harbor. To qualify at the elementary/middle level, the group must make the State Standard or its Progress Target in science in grades 4 and/or 8. At the secondary level, it must make the State Standard or its Progress Target for graduation rate.

21

Phase

Diagnostic

Differentiated Accountability Model

Category

CORRECTIVE ACTIONIMPROVEMENT RESTRUCTURING

CURRICULUM AUDITSCHOOL QUALITY REVIEW ASSIGNMENT OFJoint Intervention Team and

Distinguished Educator

FOCUSED COMPBASIC FOCUSED COMPREHENSIVE FOCUSED COMP

SURR

Intensity of Intervention

FAILED AYP 2 YEARS

FAILED AYP 2 YEARS

Plan/Intervention CORRECTIVE ACTION PLAN & IMPLEMENTATION

OF CURRICULUM AUDIT

IMPROVEMENT PLANCREATE AND IMPLEMENT

External personnel to revise and assist school implement the most

rigorous plan or, as necessary,PHASE-OUT /CLOSURE

Oversight& Support

SED provides TA to districts: sustaining greater latitude and more responsibility for

addressing schools

SED empowers districts: gives them the support and assistance necessary to take primary

responsibility for developing and implementing improvement strategies

SED & its agents work in direct partnership with

the district

Data Warehouse

SIRS

NYSSIS

Continuous Enrollment

Performance IndexAYP

AMO

Effective AMO

NYSAA

Participation Rate

Accountability Subgroups

Safe Harbor

BEDS

NYSESLAT

Accountability Cohort

AVR

Graduation Cohort

COGNOS

Diff

eren

tiate

d A

ccou

ntab

ility

Triangulating Data

Word Splash

Sampling Principle

Summative AssessmentFormative Assessment

Scale Score --- Raw

Score

Standards-Referenced Test

District Report Card

Managing Modeling Mediating Monitoring

Data-Driven DialogueThe Collaborative Learning Cycle

"He uses statistics as a drunken man uses lamp-posts...

Andrew Lang (1844-1912)

In reference to an individual who misuses data:

…for support rather than illumination."

Data-Driven DialogueThe Collaborative Learning Cycle

Activating and Engaging

Managing Modeling Mediating Monitoring

• What are some predictions we are making?• With what assumptions are we entering? • What are some questions we are asking?• What are some possibilities for learning that this experience presents to us?

What is a prediction you made?

What might be some assumptions that influenced your prediction?

Data-Driven DialogueThe Collaborative Learning Cycle

Managing Modeling Mediating Monitoring

Exploring and Discovering

• What important points seem to “pop out”?• What are some patterns, categories, or

trends that are emerging?• What seems to be surprising or

unexpected?• What are some things we have not yet

explored?

Principles of Data-Driven Dialogue

• Importance of Predictions

• Conscious Curiosity

• Purposeful Uncertainty

• Visually Vibrant Information

• Third Point

Data-Driven DialogueThe Collaborative Learning Cycle

Managing Modeling Mediating Monitoring

Exploring and Discovering

• What important points seem to “pop out”?

• What are some patterns, categories, or trends that are emerging?

• What seems to be surprising or unexpected?

• What are some things we have not yet explored?

Data-Driven DialogueThe Collaborative Learning Cycle

Managing Modeling Mediating Monitoring

Organizing and Integrating

• What inferences/ explanations/ conclusions might we draw?

• What additional data sources might we explore to verify our explanations?

--------------------------------------------------• What are some solutions we might

explore . . . ?• What data will we need to collect to

guide implementation?

“My team has created a very innovative solution,but we’re still looking for a problem to go with it.”

Curriculum Instructional methods and

materials Teacher knowledge and skills Student readiness Infrastructure

Causal Arenas

Theories of Causation

Observation: record three possible theories of causation re: your observation1.

2.

3.

Circle one theory. In this space, record at least three sources of data you could use to confirm this theory.

Data-Driven DialogueThe Collaborative Learning Cycle

Managing Modeling Mediating Monitoring

Organizing and Integrating

• What inferences/ explanations/ conclusions might we draw?

• What additional data sources might we explore to verify our explanations?

--------------------------------------------------• What are some solutions we might

explore . . . ?• What data will we need to collect to

guide implementation?

Time to Share

• Share ONE observation

• Share ONE theory of causation

• Share additional data sources that you would want to explore to confirm or disprove your theory

Balanced Assessment