ED467688 2002-09-00 Data-Driven Equity

in Urban Schools. ERIC Digest.

ERIC Development Team

www.eric.ed.gov

Table of Contents

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Data-Driven Equity in Urban Schools. ERIC Digest..........................1

DATA USE TO ENHANCE EQUITY......................................... 2

DATA TYPES.................................................................... 2

DATA ANALYSIS............................................................... 3

DATA-DRIVEN DECISION MAKING........................................ 3

CHOOSING A TECHNOLOGY TOOL TO SUPPORT DATA-DRIVEN

DECISION MAKING...................................................... 3

DATA DISAGGREGATION TOOLS......................................... 4

REFERENCES.................................................................. 5

ERIC Identifier: ED467688

Publication Date: 2002-09-00

Author: Schwartz, Wendy

Source: ERIC Clearinghouse on Urban Education New York NY.

Data-Driven Equity in Urban Schools. ERIC

Digest.

THIS DIGEST WAS CREATED BY ERIC, THE EDUCATIONAL RESOURCES

INFORMATION CENTER. FOR MORE INFORMATION ABOUT ERIC, CONTACT

ACCESS ERIC 1-800-LET-ERIC

ED467688 2002-09-00 Data-Driven Equity in Urban Schools. ERIC Digest. Page 1 of 7
The 2002 reauthorization of the Elementary and Secondary Education Act mandates

that schools receiving Federal funding are required to disaggregate their student

performance data by race, gender, and socioeconomic status in order to provide

progress information to the community and the state. Data-driven decision making is

particularly important in urban schools whose populations are disproportionately poor,

minority, and in need of special services (Slowinski, 2002). This digest discusses the

types of data that schools should collect and the ways to use the information effectively

in decision making to enhance equity. It also identifies helpful technological resources.

DATA USE TO ENHANCE EQUITY

Collecting and analyzing meaningful data about the characteristics and academic

performance of students, and about school organization and management, helps

underresourced, underperforming, and highly diverse schools "identify achievement

gaps, address equity issues, determine the effectiveness of specific programs and

courses of study, and target instructional improvement" (Lachat, (2002, p. 3). The

process helps provide individualized instruction and services to meet the needs of a

single student or a specific group of students. It also enables a school continually to

monitor the effects of its efforts to determine whether changes are needed. Thus, a

school can help ensure the success of the students who most need special supports

and who otherwise might not have been noticed.

DATA TYPES

Conducting an overall evaluation to see how the school is doing is a good way to begin

data collection. This is often referred to as profiling, a process that helps school

administrators, counselors, and teachers explore, discover, and address the individual

needs of the school and students (Slowinski, 2002; Wade, 2001). Four basic categories

of data are important to collect (Bernhardt, 2001; Lachat, 2002; Slowinski, 2002):

Student Learning Data: These data comprise information on individual students. They

include, for example, prior schools attended, courses taken, and achievement; current

classroom assessments, grades, standardized test scores, and comparisons among

them; diagnostic assessments; programs enrolled in and courses taken (i.e., special

education, bilingual education, school-to-career); and participation in projects and

extracurricular activities. The resulting longitudinal data provide information about a

student over time and identify factors that may have influenced performance (i.e., a

school transfer, a change in program). Aggregating student learning data provides a

profile of the school, such as the total student group at the top level of achievement or

those receiving tutoring.

Student Demographics: These data comprise personal factors about each student:

gender, ethnicity, socioeconomic level, language proficiency, and other equity factors.

They also comprise characteristics related to a student's schooling, such as attendance

and discipline record, and school mobility.

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Perceptions Data: These data provide information about perceptions of school

personnel, parents, and the community; about the school at large; about the classes;

and about specific programs and strategies.

School Process Data: These data provide information about school management,

administration, organization, and operations, including the programs the school offers,

classroom strategies, and instructional practices.

DATA ANALYSIS

Disaggregating the data by breaking them down into smaller elements allows a school

to determine more accurately the effects of its programs and strategies on segments of

its student body. To conduct an initial equity analysis, for example, the students taking

advanced courses can be disaggregated by ethnicity, gender, and socioeconomic

status to ascertain the percentage, by group, enrolled in those courses and to consider

whether placement decisions are biased or whether the achievement of some groups is

routinely lower than that of others (Lachat, 2002).

To determine whether specific programs are not working for certain students,

underachieving students, students with excessive absenteeism, and dropouts, data can

be disaggregated by courses taken, or even by the teachers who taught them, to

consider relevant school influences. The performance of students with similar personal

characteristics can be disaggregated to determine which courses, instructional

strategies, etc., are most effective with them. Differences between student grades and

scores on standardized tests can be reviewed to determine whether there are lags in

course content or poor preparation for some types of tests (Lachat, 2002). Finally, item

analysis--the process of examining the group of students that missed a particular item

on one test or similar items on several assessments--can determine what, if any, factors

they have in common, such as the same teacher or limited opportunity to learn the

material (Slowinski, 2002).

DATA-DRIVEN DECISION MAKING

Disaggregating data uncovers interesting realities that can form essential questions

about the reasons for student outcomes and the ways to reverse poor results. These

questions should drive the investigation phase that involves, for example, researching

best practices and proven programs for students like those of the school. After

identifying the programs or program components, and the practices likely to meet the

needs of its students, the school is ready to develop an implementation plan. A pilot with

a small group can determine unintended consequences as well as the impact of the

intervention (Slowinski, 2002).

CHOOSING A TECHNOLOGY TOOL TO

SUPPORT DATA-DRIVEN DECISION MAKING

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A technology tool is required to manage data flow. Several factors need to be

considered in selecting one (Slowinski, 2002):

Functionality. At a basic level, a tool should be able to import data from a variety of

electronic sources into a relational database that can disaggregate the data. The tool

should also be able to generate a report easily through graphs and diagrams of the data

and their relationships.

Data Storage Capacity. The technology tool must accommodate the number of students

in the district or school and the amount of data collected over the time period

determined.

Training. The school needs to ascertain how easily the tool can be learned, whether an

individual at the local level will need to commit a significant amount of time to learning

and using it, the availability of such an individual, and the cost of training and technical

assistance.

Format. Decisions, to some extent based on relative costs, should be made about

whether to purchase a tool or outsource its use to a vendor, and where to house the

data (at the district or school or with the vendor). Schools also need to consider the

format of the tool (i.e., web-based), mode of access (i.e., via the Internet), ease of

access of school personnel, and security issues.

DATA DISAGGREGATION TOOLS

Ease-e Data Analyzer by TetraData: Is an online tool for data warehousing, mining,

analysis, and reporting. Data can be disaggregated and reported by gender, ethnicity,

and socioeconomic status. Standards can be created, tracked and analyzed with graphs

and reports generated. TetraData also offers a Data Manager tool to assist in the import

and export of data from a variety of sources.

http://www.ease-e.com/ease-e/products/products_services_analyzer.asp

Goalview: Offers teachers and administrators the ability to set goal, track performance,

and report results especially for special needs and Title I students. Includes Goalcard,

an individual report of student progress on standards that can be disseminated to

teachers and parents; and a database of 250,000 Education Standards and 10,000

Special Education goals to assist in creating goals and IEPs. Provides instructional

resources as well as access to Federal and state laws and regulations.

http://goalview.school.aol.com/GOALVIEW/index.asp

SchoolNet: Offers a number of web-based tools for data collection and analysis as well

as aligning curriculum and instruction to standards. http://www.schoolnet.com

Socrates, by the Center for Resource Management: Offers tools and services to help

schools import data, analyze, and ask good questions. Has a capacity of 2 billion pieces

of information and allows unlimited import of any electronic data source into a relational

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Page 4 of 7 ED467688 2002-09-00 Data-Driven Equity in Urban Schools. ERIC Digest.
data base that can disaggregate data for one-to-one, one-to-many, or many-to-many

relationship analyses. http://www.crminc.com/socrates/default.htm

Quality School Portfolio: Is a free data analysis system consisting of two tools: a Data

Manager, which allows a school or district to import data, disaggregate the data, and

report about the data; and a Resource Kit, which consists of 21 tools to collect data

including survey instruments, observation protocols, and questionnaires. Tools can

collect information in these areas: Safety & Security, Parent Involvement, Professional

Development, Curriculum & Instruction, Technology & Innovation, and Special

Programs. QSP can house 8,000 records with 120 variables for 7 years. Data analysis

is limited to mean, median, count, percentage, and distributions. Displays are limited to

pie charts, tables, bar graphs, cross tabulation, line chart, and histogram.

http://qsp.cse.ucla.edu/mainSub/whatSet.html

Source: Slowinski, 2002.

REFERENCES

Bernhardt, V.L. (2001, Winter). Intersections. Journal of Staff Development, 21(1),

33-36. (EJ 600 392) Available at: http://www.nsdc.org/library/jsd/bernhardt211.html

Lachat, M.A. (2002). Data-driven high school reform: The breaking ranks model.

Hampton, NH: Center for Resource Management.

Slowinksi, J. (2002). Data-driven equity: Eliminating the achievement gap and improving

learning for all students. Unpublished manuscript, Vinalhaven Schools, Vinalhaven, ME.

Wade, H. H. (2001, December). Data inquiry and analysis for educational reform. ERIC

Digest 153. Eugene, OR: ERIC Clearinghouse on Educational Management. Available

at:

http://eric.uoregon.edu/publications/digests/digest153.html

DATA SUPPORT RESOURCES

Toolbelt, by the North Central Regional Educational Laboratory: Includes a wealth of

resources to support data-driven decision making including: data retreat facilitation

guide, data tools, planning tools, surveys, check lists, etc. http://www.ncrel.org/toolbelt/

Data-Driven Implementation Tools: Includes three free tools for K-12 schools to use:

School Climate Inventory (SCI), Student Achievement Data, and School Observation

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ED467688 2002-09-00 Data-Driven Equity in Urban Schools. ERIC Digest. Page 5 of 7
Measure. http://www.serve.org/csrd/datadriven/data.html

Evaluating Whole-School Reform Efforts: A Guide for District and School Staff: Provides

an overview of conducting impact and implementation evaluations.

http://www.nwrac.org/whole-school/index.html

Using Data to Improve Student Achievement: Offers several tools to analyze and

interpret data that were designed to aid schools in analyzing Kentucky school data, but

provide helpful examples for all school personnel.

http://www.kde.state.ky.us/oapd/rsc6/dataanalysis.asp

Disaggregation without Aggravation: Is a multimedia training package for sale from the

Southwest Educational Development Laboratory that includes case studies of schools

as well as training and facilitation materials.

http://www.sedl.org/pubs/catalog/items/teaching06.html

Using Data to Improve Schools:

http://www.aasa.org/cas/UsingDataToImproveSchools.pdf

American Association of School Administrators' Center for Accountability Solutions:

http://www.aasa.org/cas/

Fundamental Assessment Principles for Teachers and School Administrators: Article.

http://ericae.net/pare/getvn.asp?v=7&n=8

-----

The Clearinghouse gratefully acknowledges the contributions of Joseph Slowinski to the

preparation of this digest.

This Digest was developed by the ERIC Clearinghouse on Urban Education, with

funding from the Office of Educational Research and Improvement, U.S. Department of

Education, under contract no. ED-99-CO-0035. The opinions in this Digest do not

necessarily reflect the position or policies of OERI or the Department of Education.

Title: Data-Driven Equity in Urban Schools. ERIC Digest.

Note: Digest Number 177.

Document Type: Information Analyses---ERIC Information Analysis Products (IAPs)

(071); Information Analyses---ERIC Digests (Selected) in Full Text (073);

Available From: ERIC Clearinghouse on Urban Education, Institute for Urban and

Minority Education, Box 40, Teachers College, Columbia University, New York, NY

10027. Tel: 800-601-4868 (Toll free); Tel: 212-678-3433; Fax: 212-678-4012; e-mail:

eric-cue@columbia.edu. For full text:

http://eric-web.tc.columbia.edu/pubget.asp?show=1.

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Page 6 of 7 ED467688 2002-09-00 Data-Driven Equity in Urban Schools. ERIC Digest.
Descriptors: Data Analysis, Data Collection, Decision Making, Educational Equity

(Finance), Elementary Secondary Education, Expenditure per Student, Federal Aid,

Minority Group Children, Poverty, Resource Allocation, Urban Schools

Identifiers:

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