Common Data Views Data Driven Instruction and Inquiry (DDI) is a critical component of the Regents’ Reform Agenda. As educators strive to effectively implement the NYS Common Core Curriculum, having access to high-quality data related to student learning has become increasingly important.

As a result, the 12 Regional Information Centers are collaborating in effort to provide all school districts with a common report package that can be leveraged to support the following needs:

• Provide educators, across the state, with a common framework to discuss effective teachingstrategies.

• Improve the quality of reports available by leveraging programming and data analysis expertiseacross the state.

Please review the following pages to learn more about the 12 Regional Information Centers and the Common Data Common Data Views

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Data Driven Instruction and Inquiry (DDI) is a critical component of the Regents’ Reform Agenda. As educators strive to effectively implement the NYS Common Core Curriculum, having access to high-quality data related to student learning has become increasingly important. !As a result, the 12 Regional Information Centers are collaborating in an effort to provideall school districts with a common report package that can be leveraged to support the following needs: !• Provide educators, across the state, with a common framework to discuss effective

teaching strategies.

• Improve the quality of reports available by leveraging programming and data analysisexpertise across the state. !

Please review the following pages to learn more about the 12 Regional InformationCenters and the Common Data Views. As you explore your local Cognos environments, please note these Common Data Views may look slightly different based on regional input.

Overview of

Contents:Page 1

Common Data Reports Initiative

Page 2 Regional Information Centers’ Expertise

Pages 3-8

Exploring NYS Common Data Views

Central New York RIC 6820 Thompson Rd. Syracuse, NY 13211 !

Greater Southern Tier RIC 459 Philo Rd.

Elmira, NY 14903 !Lower Hudson RIC

44 Executive Boulevard Elmsford, NY 10523 !Mid-Hudson RIC 175 Rt. 32 North

New Paltz, NY 12561 !Mohawk RIC

4937 Spring Rd. Verona NY 13478 !

Monroe RIC 11 Linden Park

Rochester, NY 14626

Nassau RIC 1 Merrick Ave.

Westbury, NY 11590 !Northeastern RIC

900 Watervliet Shaker Road Albany, NY 12205 !

South Central RIC 435 Glenwood Rd.

Binghamton, NY 13905 !Suffolk RIC

15 Andrea Rd. Holbrook, NY 11741 !

Western New York RIC 355 Harlem Rd.

West Seneca, NY 14224

Regional Information Centers’ Expertise The Regional Information Centers (RICs) are organized under the Board of Cooperative Educational Services (BOCES).  Regional Information Centers (RICs) offer 21st century classroom tools that assist districts in optimizing student achievement, as students are prepared to be college and career ready. Similar to BOCES, RICs are a trusted provider of collaborative services. By regionalizing services, the RICs, in particular, make a wider range of technology skill sets available to school districts. This relationship increases the buying power of a district and promotes consistent technical standards. This cost effective system continues to lighten the burden placed on local taxpayers and has leveled the playing field so that no matter the size of a district, the best resources remain within reach for New York students. 

RICs provide an array of services that support New York State’s Testing Program and Data Driven Instruction and Inquiry (DDI), including those highlighted below.

12!NYS IT Centers

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NYS Data Warehouse Support

3-8 Testing Support

Regents Scanning

Benchmark and Local Assessments Support

Erasure Analysis 

Data Integration Support

Data Report Development

Data Analysis Support

Network and Inquiry Teams Support

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Data Instructional Use

CognosData Views

3-8 ELA/ Math

4/8 Science Regents

Data Analysis Meetings

Indiv. Student Progress

Grade Level/

Building Progress

Program Analysis

Regional Sharing

Individual Student Performance

X X X X X

Regional Gap Analysis X X X X X X X

Released Question Analysis

X X X X X

Constructed Response Analysis

X X X X X X

P-ValueX X X X X

Coming Soon!Scatter Plot X X X X X

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In the fall of 2013, the twelve Regional Information Centers (RICs) worked collaboratively to identify the five common data views that would be most beneficial to school districts across the state for instructional purposes. The project began with the development of the 3-8 Individual Student Performance Reports, Regional Gap Analysis, Released Question Analysis, Constructed Response Analysis and P-Value Reports. When the 2014 testing data was released in July, all five reports were available for all districts to use for professional development and instructional planning for the 2014-2015 school year.

This fall, centers have been working to prepare the same views at the Regents level. The reports will be available in Cognos at every center after the January 2015 Regents item maps are available. Currently, item maps are developed by curriculum experts from across New York State. NYSED is exploring the feasibility of providing the RICs with item maps in order to support more timely access to critical student data. In addition, centers are working to develop Regional Scatter Plot Reports that illustrate the relationship between economically disadvantaged students and proficiency rates at both the 3-8 and Regents levels. The Scatter Plots will be available in the spring of 2015 at all twelve centers.

The plan is to continue to expand the views as data driven instructional practices are refined across the state and data analysis needs evolve. The following pages provide an overview of the four Common Regents Data Views along with the Scatter Plot that will be available in the spring.

Common Data Views Development Process

Individual Student Performance For all students taught in a particular section or building, this view provides information related to students’ individual performance. Each student’s results are grouped by domain, cluster, and standard.

Data Analysis Best Practice: When interpreting results for single students, the greater number of questions examined, the greater the confidence in the interpretations about the student’s performance on questions grouped in meaningful ways.

“It’s important for students to have access to their own data and to get that feedback from teachers so they can start to direct their own learning.”

-Deb Duffy, Mohawk Regional Information Center Network Team Member and Data Leader

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Regional Gap Analysis This common view shows the percentage of total possible points earned for the district, as compared to the regional percent correct for each item.

Data Analysis Best Practice: Examining regional trends supports educators’ efforts to leverage regional expertise. Using this data, teachers and administrators are able to have informed collaborative conversations with colleagues and identify and share instructional best practices. C

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perspectives, longitudinal data and discrete skill item analysis our Network Team is able to help teachers triangulate the data and then help them refine their practice.”

-Laurie Hedges, Herkimer BOCES Assistant Superintendent

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Constructed Response Distribution This common view shows students’ performance on constructed response items. Tables and/or graphs show aggregated performance within a domain and/or distribution of performance by standard.

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“This report allows teachers and administrators to drill-down to the standards level... it allows us to align instruction with ongoing formative assessment to monitor student progress throughout the year.”

-Larry Dake, Union-Endicott Central School District Principal

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Data Analysis Best Practice: Analysis of constructed response distribution reports supports educators in improving writing across the content areas and ensures that students gain adequate mastery of a range of skills and applications.

P-Value This common view shows the p-value for each question. For multiple choice items, p-value is the proportion of students responding correctly. For constructed response items, p-value is the mean raw score divided by the maximum number of score points for an item.

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“If we’re giving assessments and don’t use data, how can we drive instruction? We would just keep teaching what we’re teaching and never close gaps.”

-Anne Marie Palladino, Utica City School District Principal

Data Analysis Best Practice: District performance on individual questions can be compared to regional levels to determine how similar students performed on a particular question. The larger the sample size the more accurate the results.

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Proficiency & Poverty Scatter Plot This common data view illustrates the relationship between economically disadvantaged students and proficiency rates. Districts placed in the upper-right quadrant are both high-need and high-performance; districts placed in the lower-right quadrant are those of high-needs and low-performance; districts in the upper- left are low-need and high-performance; finally, those in the lower-left quadrant are low-need and low-performance.

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Data Analysis Best Practice: This chart can be used to examine the correlation between economically disadvantaged students and performance. Particular interest should be paid to those districts who are placed, or approaching, the upper-right quadrant, as those schools have shown significant levels of achievement with significant barriers related to student poverty.