NTA District Survey 2007-08 Page 1 of 20

ID No. OMB No. 1875-0241

Expiration date: 6/10

Study of Education Data Systems and Decision Making

Page 1 of 20

According to the Paperwork Reduction Act of 1995, no persons are required to respond to a collection of

information unless it displays a valid OMB control number. The valid OMB control number for this survey

is 1875-0241. The time required to complete this survey is estimated to average 60 minutes, including

the time to review instructions, search existing data sources, gather the data needed, and respond to the

survey questions. Send comments regarding this burden estimate or any other aspect of this collection of

information, including suggestions for reducing the burden, to the U.S. Department of Education,

Washington, DC 20202-4651.

NTA District Survey 2007-08

ID No.

Note to the recipients of this survey:

This survey concerns the nature of district data systems and the use of data from such

systems to improve instruction. Two or more district staff may need to collaborate in

responding to the survey. The staff member responsible for implementing information

systems may best be able to answer questions in Section B. Sections C and D should be

completed by the district staff member who has primary responsibility for leading

data-driven instructional improvement activities (e.g., the integration and analysis of

data and information from various data systems to support decisions intended to improve

teaching and learning at the school and classroom levels).

Topics addressed in the survey include:

 District and respondent background information

 Data elements of the district electronic data system

 Features of the data system

 Access to the data system

 Data quality

 Use of data to support instructional decision making

 Activities undertaken to increase the capacity of district and school staff to engage

in data-driven decision making

 Barriers to expanding the use of data-driven decision making practices

The survey is also available in electronic form. If you prefer to take this survey on-line, you

can go to the following URL: EdTechFuture.org. In order for us to determine which

districts have responded using the electronic form, you will be requested to enter the ID

number at the top left-hand corner of this page of the survey as well as the name and

location of your district. All the names of districts sampled for the survey will be deleted;

only the identification number assigned to each survey will be used in entering data.

2007-08 District Survey

25455
NTA District Survey 2007-08 Page 2 of 20

ID No.

Dear District Administrator:

Here are answers to some questions you may have about this survey.

Why is the U.S. Department of Education sponsoring this study?

This study is being conducted for the U.S. Department of Education to document the availability

of systems and supports for using data to improve instruction, and the prevalence and nature of

data-informed decision making in districts and schools. We are collecting data from a nationally

representative sample of 500 school districts.

The information collected through this study will be used in designing outreach efforts by the

U.S. Department of Education to stimulate and enhance data systems to improve instruction.

The data can also be used by state and local staff to identify and develop policies to support

promising practices in data-driven decision making intended to improve teaching and learning

at the school and classroom levels.

Why should I respond to this survey?

The law requires grantees of state-administered federal programs, like Enhancing Education

Through Technology (EETT), to cooperate with federal evaluations of the program (see 34

CFR, Section 76.591). We are conducting this survey with only a sample of districts from

across the country. Your response is very important because it represents many other

districts nationwide. Each respondent's participation is critical to our ability to provide policy

makers with complete and accurate information.

Completed surveys will be coded, entered into a data set, and stored in secure facilities. All the

names of districts sampled for the survey will be deleted; only the identification number

assigned to each survey will be used in entering data. Survey results will be reported in

aggregated form to ensure that no individual respondents can be identified.

What will I need to complete this survey?

The survey will require about 60 minutes of your time. You will be able to answer the majority of

questions on the basis of your knowledge and experience. Some items may concern topics with

which you are not familiar. We encourage you to ask the appropriate individuals in your district

for the information. Please complete each item according to the directions and return the survey

in the enclosed postage-paid envelope to:

THANK YOU FOR YOUR COOPERATION IN THIS IMPORTANT EFFORT.

Angeline Reyes

SRI International, Room BN325

333 Ravenswood Avenue

Menlo Park, CA 94025

The survey is also available in electronic form. If you prefer to take this survey on-line, you can

go to the following URL: EdTechFuture.org.

Whom should I contact for more information?

If you have questions or comments about your response to this survey or about the study,

please call Angeline Reyes at 650-859-5927, or e-mail Angeline at nta-survey@sri.com. (SRI is

an independent, not-for-profit research and consulting organization.)

25455
NTA District Survey 2007-08 Page 3 of 20

ID No.

Study of Education Data Systems and Decision Making

1. How many public schools are currently in your district?

Which of the following most closely describes the job title of the staff member or members who

completed each section of the survey? (Mark as many as apply for each column.)

2.

a. How many of these schools received Title I funds in the current (2007-2008)

school year?

b. For the current school year (2007-08), how many schools in the district did not

make adequate yearly progress (AYP) as defined by NCLB based on 2006-07

test scores?

A. Respondent Background

As described on the cover of the survey, two or more district staff may need to collaborate in

responding to the survey. The staff member responsible for managing or supporting information

systems may best be able to answer questions in Section B. Sections C and D should be

completed by the district staff member who has primary responsibility for leading data-driven

instructional improvement activities. Note that no respondents or districts will be identified;

survey results will be reported in aggregated form.

a. District Superintendent

b.

c.

d.

e.

f.

g.

Assistant/Deputy Superintendent

Chief Finance Officer

Researcher/Evaluator

Section D

Chief Operating Officer

Chief Information Officer

Division Director

h.

Instructional Technology Coordinator

i.

j.

Professional Development Specialist

Other:

For purposes of this survey we are interested in electronic student data systems and tools that

can enhance educational decision making (see Question 3 for definitions of these types of

systems). If you do not have a student data system that meets this definition, please check

here and return the survey in the postage-paid envelope.

Job title: Section B Section C

IMPORTANT NOTE:

Please use a BLACK pen. Blue or red pens and pencil cannot be read by our scanners.

When asked to mark boxes, make an "X" through the box.

 Sample: Right Wrong

Use block printing when you complete any text or numeric responses.

If you wish to change a response, please mark the correct response and CIRCLE it.

25455

n=422

n=418

n=412

1-817

0-643

0-412

10

n=417

n=417

6.0 6.7 6.5

15.3 17.5 16.3

1.4 1.0 1.4

30.4 22.3 24.0

1.4 1.0 1.0

25.8 28.5 25.8

33.0 28.2 29.7

10.1 12.0 11.5

4.6 6.7 6.5

n=93-108 25.8 22.3 22.5
NTA District Survey 2007-08 Page 4 of 20

ID No.

What electronic student data system or systems is driving instructional improvement in your

district? The first column describes the major function or components of the data systems.

Indicate which of these systems is driving instructional improvement in the district by writing in

the name of the system and identifying the developer of the system. (Mark as many as apply.)

3.

B. Student Data System

This section asks about the features and capabilities of the district's current electronic student

data system or systems, regardless of whether those features are used to their fullest capacity.

Elements of the Student Data System

Student information system: Provides

real-time access to student data such as

attendance, demographics, test scores, grades,

schedules, etc.

a.

b.

c.

d.

e.

f.

Data warehouse: Electronic data collection and

storage system that provides access to current

and historical data on students, personnel,

finance, etc.

Other system (describe):

Type of Student Data System

Instructional/curriculum management system:

Provides a unifying framework to provide access

to curriculum and instructional resources such as

planning tools, model lesson plans, creation of

benchmark assessments, linkage to state content

or performance standards, communication and

collaboration tools (e.g., threaded discussion

forums).

Assessment system: Rapidly organizes and

analyzes benchmark assessment data.

Other system (describe):

Name(s) of

Student Data

System (fill in)

Source/Developer

25455

n=417

 2.0 12.8 88.3 2.6

 (0.45) (3.00) (2.97) (1.63)

 8.6 23.4 55.7 3.1

 (2.34) (4.09) (4.65) (1.65)

 17.5 14.1 41.6 2.3

 (3.47) (3.30) (4.63) (1.22)

 12.0 16.6 46.9 5.7

 (2.97) (3.57) (4.63) (2.29)

(n=21)

(n=13)
NTA District Survey 2007-08 Page 5 of 20

ID No.

How long has your district had each of the kinds of electronic student data systems indicated in

Question 3, including systems that may predate the ones listed above (e.g., an earlier student

information system by a different developer)? (Mark one response for each type of system.)

4.

Less than

1 year

a. Student information system

b.

c.

d.

e.

f.

Data warehouse

Other system (describe):

Type of Student Data System

6 or more

years

1 to 2

years

3 to 5

years

Instructional/curriculum management

system

Assessment system

Other system (describe):

Don't

Have

System

Number of Years Had This Type of Data System

25455

 0.0 3.0 5.7 21.1 70.2

(0.00) (1.65) (2.29) (3.92) (4.38)

 22.5 9.3 8.6 20.7 38.9

 (4.00) (2.98) (2.40) (3.79) (4.68)

 36.0 10.1 12.7 24.4 16.9

 (4.76) (2.94) (2.99) (4.16) (3.77)

 21.0 9.9 14.7 38.3 16.0

(4.12) (2.94) (3.19) (4.71) (3.64)

(n=43)

(n=29)

n=414
NTA District Survey 2007-08 Page 6 of 20

ID No.

a. Student test scores on statewide assessments.

Does your district currently maintain electronically any of the following types of information (not

just from the data systems described above)? If yes, do you have access to data stored for at

least 3 years or longer (i.e., access to data in the same format across years)? (Mark one

response for each row in both columns.)

5.

Data available

electronically in

your district?

Longitudinal data

available for 3 years

or more?

Type of Information NO YES NO YES

Student grades (i.e., end of course, quarter or semester

grades).

b.

c.

d.

e.

f.

g.

h.

Differential codes for students no longer enrolled (e.g.,

transferred within the district, transferred out of the district,

dropped out).

Student graduation status (i.e., whether or not each student

graduated).

Status after graduation (e.g., attending college, working).

Student test scores on SAT, ACT, and Advanced Placement

tests.)

Student special education information (e.g., diagnostic data).

Teacher professional development (e.g., workshops

attended, courses taken).

Parent data (e.g., background, involvement, perceptions of

school).

i.

j.

k.

l.

Teacher qualifications (e.g., certification, education).

Student participation in educational programs (e.g., ELL

program, Title I, gifted and talented, special education, after

school learning programs, supplemental services tutoring).

Student attendance (e.g., daily attendance, tardies).

Student course enrollment histories (e.g., course completion

information).

Student test scores on district-administered assessments

(e.g., benchmark test, diagnostic test, local test).

Other (describe):

m.

n.

o.

Student demographics (e.g., campus of enrollment, grade

level, gender, ethnicity, English Language Learner-ELL

status, economically disadvantaged status, migrant status).

p.

Prior school(s) attended within the district.

q.

r.

Student behavior data (e.g., counselor reports, referrals,

discipline).

s.

Student test scores on school-administered assessments

(e.g., end of unit test, diagnostic test).

25455

n=371-413

7.0 93.0 15.5 84.5

(2.38) (2.38) (3.66) (3.66)

28.3 71.7 42.9 57.1

(4.46) (4.46) (5.12) (5.12)

47.8 52.2 57.3 42.7

(4.87) (4.87) (5.41) (5.41)

42.7 57.3 35.4 64.6

(4.85) (4.85) (5.31) (5.31)

5.0 95.0 14.5 85.5

 (2.06) (2.06) (3.59) (3.59)

8.5 91.5 12.0 88.0

(2.79) (2.79) (3.43) (3.43)

1.9 98.1 10.1 89.9

 (1.20) (1.20) (3.11) (3.11)

14.1 85.9 18.1 81.9

(3.48) (3.48) (4.06) (4.06)

15.8 84.2 21.1 78.9

(3.55) (3.55) (4.34) (4.34)

14.5 85.5 21.7 78.3

 (3.45) (3.45) (4.31) (4.31)

2.4 97.6 13.0 87.0

 (1.25) (1.25) (3.48) (3.48)

13.0 87.0 26.7 73.3

(3.27) (3.27) (4.60) (4.60)

 6.8 93.2 17.6 82.4

(2.36) (2.36) (3.93) (3.93)

10.2 89.8 11.7 88.3

(2.97) (2.97) (3.33) (3.33)

66.1 33.9 63.9 36.1

(4.69) (4.69) (5.82) (5.82)

27.2 72.8 24.9 75.1

(4.43) (4.43) (4.68) (4.68)

53.3 46.7 50.3 49.7

(4.80) (4.80) (5.62) (5.62)

75.5 24.5 73.2 26.8

(4.22) (4.22) (5.34) (5.34)

(n=14)
NTA District Survey 2007-08 Page 7 of 20

ID No.

What data would your district like to have electronically that you do not currently have?

(Specify below.)

6.

Does your district student data system(s) have the capability to support queries (i.e., allowing

the user to make specific data requests)? If yes, indicate whether the query function is made

available to district and/or school staff-answer yes if some or all staff have access to any of the

types of information listed (e.g., school staff can make queries about the performance data of

students in their school but not students in other schools). (Mark "No" or indicate which type

of staff have access for each row.)

7.

Features of the Student Data System

Yes, Query

 Function Made

Available To:

District

staff

School

staff

No, System

Lacks This

Query

Capability

a. Student performance linked to Adequate Yearly Progress (AYP)

subgroups (e.g., low-income students, African-American students).

Type of Query

b. Student performance linked to specific teachers (e.g., reading

achievement of students in a specific classroom).

Student performance linked to teacher information or

characteristics (e.g., reading performance of students assigned to

first-year teachers).

c.

d. Student performance linked to specific instructional programs (e.g.,

achievement gains of third graders in the after school reading

program).

Individual student assessment performance over time (e.g., reading

test score history).

e.

f. Individual student history over time (e.g., cumulative grades,

schools attended).

Linking school performance and finance data (e.g., how high- and

low-performing schools compare in terms of actual per pupil

expenditures).

g.

25455

n=414-416

33.6 64.9 40.9

(4.55) (4.59) (4.56)

33.2 62.8 43.0

(4.51) (4.62) (4.57)

61.6 35.0 19.5

(4.55) (4.45) (3.61)

57.7 38.7 25.1

(4.62) (4.55) (3.95)

27.8 64.1 56.5

 (4.31) (4.58) (4.67)

17.2 78.3 60.6

 (3.72) (4.03) (4.63)

73.4 23.9 10.8

(4.12) (3.94) (2.96)
NTA District Survey 2007-08 Page 8 of 20

ID No.

Does your district data system(s) include the following features or tools for use by district and

school staff, regardless of whether those capabilities are used? (Mark all that apply for

each row.)

8.

Yes, Feature

Available To:

District

staff

School

staff

No, Not

Available

on System

a. Ability to generate standard accountability reports or district report

card and school report cards (e.g., student performance by

subgroup).

Feature or Tool

b. Transaction capture (e.g., daily and class attendance,

disciplinary actions, sick time, etc.)

Drill-down capability (e.g., the ability to query a school-level finding

to efficiently examine a subset of data at the grade, classroom, or

student level).

c.

d. Assessments available in reading, mathematics, or other core

subject areas that students take online.

Tools for communicating with parents around an individual

student's performance (e.g., student reports, online access to

student assignments and attendance).

e.

f. Links to curricular resources (e.g., lesson plans, state content or

performance standards, references to instructional materials).

g. Other features or tools (describe):

Access to the Student Data System

The following questions focus on the accessibility of student-level data in the district's

electronic data system(s) that drive instructional improvement (the systems identified in

Question 3).

What description below best describes the access that school principals or specialists (i.e.,

nonclassroom teachers) have to student-level data through the district's electronic information

system(s) that drive instructional improvement? (Mark one answer.)

9.

Principals or specialists have access to all the data on students in their school contained in the

district information system(s).

Principals or specialists have access to most of the data on students in their school contained in the

district information system(s).

Principals or specialists have access to a limited set of the data on students in their school

contained in the district information system(s) (e.g., achievement data, attendance data).

No electronic student-level data is available to principals or specialists.

25455

n=417

29.9 62.5 47.2

(4.32) (4.59) (4.66)

4.7 85.7 70.8

(2.01) (3.42) (4.31)

24.0 69.0 49.0

(4.01) (4.37) (4.68)

47.9 40.7 36.1

(4.69) (4.57) (4.40)

21.6 68.8 68.2

(3.82) (4.33) (4.38)

33.0 56.3 57.4

(4.39) (4.67) (4.65)

0.2 2.4 2.4

(0.17) (1.62) (1.62)

72.3 (4.20)

19.8 (3.69)

6.7 (2.37)

1.2 (1.19)

n=410

n=6
NTA District Survey 2007-08 Page 9 of 20

ID No.

What access to student-level data do individual classroom teachers have through the district's

electronic information system(s) that drive instructional improvement? (Mark one answer for

each row.)

10.

a.

b.

c. To all the data on students in the school.

To some of the data on students in their classroom (e.g., their most

recent standardized test score, parent contact information).

No Yes

Don't

Know

To some of the data on students in the school (e.g., students at the

same grade level or in classes in the same subject area).

d.

To all the data on students in their classroom.

Do the majority of classroom teachers have access to the district's electronic data system(s)

that drive instructional improvement in any of the following locations? (Mark one answer for

each row.)

11.

Internet access at their home.

In their own classroom or office.

Classroom teachers have access:

a.

b.

c.

No Yes

Don't

Teacher options to access data include: Know

Somewhere else in the school (i.e., not the teacher's classroom or office).

Data Quality

The reliability and accuracy of data in the student data system(s) that drive instructional

improvement is a common concern among districts attempting to establish data-driven decision

making practices. The next set of questions focus on this issue.

Has your district disseminated data collection guidelines and recommended data information

management and security practices to schools in your district? (Mark one answer.)

12.

No, schools have not received such guidelines.

No, but the state has provided guidelines and recommended data practices to schools.

Yes

Does your district have one or more staff members who are responsible for receiving and

preparing data files from outside sources, such as the state and test publishers, to load into the

student data system? (Mark one answer.)

13.

No such staff available.

No, because we use an outside source (e.g., the regional center, vendor) for this task.

Yes

25455

27.2 51.6 21.2

(4.27) (4.77) (3.77)

4.5 53.0 42.6

(1.79) (4.83) (4.79)

48.6 47.5 3.9

(4.90) (4.89) (1.82)

22.1 52.4 25.5

(4.00) (4.89) (4.31)

n=385-409

n=395-413

4.0 46.6 49.4

(1.72) (4.72) (4.74)

5.6 55.3 39.1

(2.18) (4.80) (4.70)

16.0 48.3 35.7

 (3.46) (4.80) (4.60)

28.9 (4.50)

21.3 (4.17)

49.8 (4.69)

n=407

n=413

10.8 (3.02)

11.3 (3.03)

77.9 (3.99)
NTA District Survey 2007-08 Page 10 of 20

ID No.

By your estimate, what percentage of the data captured by the district's student data system(s)

that drive instructional improvement (the systems identified in Question 3) is accurate? (Mark

one answer.)

14.

Less than 50% is accurate.

50-75% is accurate.

76-90% is accurate.

Greater than 90% is accurate.

15. What data elements do you feel have the biggest accuracy problems? (Specify below.)

a. Student test scores on district-required

assessments (benchmark, diagnostic).

For which of these student-level data elements are staff responsible for entering directly into

the student data system(s) (e.g., via online input/data entry, scanning a form)? Data entered

through "other method" would include staff other than those employed by the district, such as a

contractor or state staff who prepare the data for the district (e.g., a test publisher, state

generated data set). (Mark all that apply for each row.)

16.

Data Entered By:

Type of Data

Student demographics (e.g., campus of

enrollment, grade level, gender, ethnicity, ELL

status, economically disadvantaged status,

migrant status).

b.

c.

d.

e.

f.

g.

h.

Student course enrollment histories (e.g.,

course completion information).

Student behavior data (e.g., counselor

reports, referrals, discipline).

i.

j.

k. Student graduation data.

Student special education information.

Student test scores on school-required

assessments (e.g., end of unit test, diagnostic

test).

Student participation in educational programs

(i.e., ELL program, Title I, gifted and talented,

after school learning programs, tutoring).

Student attendance (e.g., daily, tardies).

Student grades.

Data

Entered

Through

Other

Method

District

staff

Classroom

teachers

Principal

& nonclassroom

school

staff

Data Not

in

System

Student dropout data.

25455

n=411

4.2 (2.00)

5.9 (2.30)

24.8 (4.04)

65.1 (4.51)

n=417

23.6 26.2 25.4 31.6 13.1

(4.09) (4.23) (4.03) (4.19) (2.83)

28.1 13.3 54.5 13.9 4.8

(4.25) (3.00) (4.65) (3.00) (1.72)

1.7 13.4 93.1 5.8 0.3

(1.19) (2.99) (2.31) (1.75) (0.18)

5.0 70.2 9.5 26.0 4.2

(2.26) (4.37) (2.77) (4.10) (1.99)

0.0 70.4 1.9 49.9 1.3

(0.01) (4.37) (1.19) (4.69) (1.16)

1.8 55.0 21.3 61.2 3.2

(1.20) (4.67) (3.62) (4.59) (1.66)

7.6 52.9 7.8 53.0 1.2

(2.73) (4.68) (2.33) (4.68) (1.16)

0.0 65.5 70.9 14.2 1.2

(0.00) (4.44) (4.31) (3.17) (1.16)

7.5 85.9 17.3 10.5 1.2

(2.73) (3.42) (3.46) (2.39) (1.16)

4.5 77.2 1.9 33.5 1.4

(2.00) (3.92) (1.20) (4.27) (1.16)

5.8 78.5 1.0 34.6 1.3

(2.29) (3.82) (0.38) (4.39) (1.16)
NTA District Survey 2007-08 Page 11 of 20

ID No.

a. Student test scores on district-required

assessments (benchmark, diagnostic).

For each of these student-level data elements, who has the ability to modify data if the need

arises once it has been entered into the district's electronic information system(s)? (Mark all

that apply for each row.)

17.

Data Can be Modified By:

Type of Data

Student demographics (e.g., campus of

enrollment, grade level, gender, ethnicity, ELL

status, economically disadvantaged status,

migrant status).

b.

c.

d.

e.

Student test scores on school-required

assessments (e.g., end of unit test, diagnostic

test).

Student grades.

Data

Cannot

be

Modified

District

staff

Classroom

teachers

Principal

& nonclassroom

school

staff

Data Not

in

System

Student special education information.

f. Student participation in educational programs

(i.e., ELL program, Title I, gifted and talented,

after school learning programs, tutoring).

g. Student attendance (e.g., daily, tardies).

h. Student behavior data (e.g., counselor

reports, referrals, discipline).

The remainder of the survey focuses on how your district's student data system(s) and tools

are being used to enhance educational decision making in your district. The questions

refer to the student data systems that are driving instructional improvement in your

district (the systems identified in Question 3). The staff member who has primary

responsibility for leading the district's data-driven instructional improvement activities may

be the best individual to complete Sections C and D of the survey.

25455

n=416-417

20.6 40.0 15.2 45.4 5.5

(3.82) (4.64) (3.44) (4.60) (1.73)

26.6 40.8 34.8 32.1 2.2

(4.18) (4.57) (4.48) (4.38) (1.20)

1.7 68.2 52.9 38.7 0.1

(1.19) (4.38) (4.69) (4.53) (0.05)

0.0 73.1 2.5 57.3 2.4

(0.00) (4.30) (1.23) (4.66) (1.62)

1.5 60.1 15.1 66.1 2.5

(1.18) (4.62) (3.18) (4.51) (1.63)

7.4 56.9 3.6 58.6 2.4

(2.72) (4.67) (1.29) (4.66) (1.62)

0.0 84.6 28.1 39.5 1.2

(0.00) (3.55) (4.04) (4.49) (1.16)

7.4 84.2 7.5 30.9 1.2

(2.72) (3.66) (2.33) (4.04) (1.16)
NTA District Survey 2007-08 Page 12 of 20

ID No.

C. District Use of Data

This section asks about your district's goals for data-driven decision making and how district

staff use the student data system(s) to support decisions intended to improve instruction. By

"data-driven decision making" we mean the integration and analysis of data and information

from various data systems to support decisions intended to improve teaching and learning at

the school and classroom levels.

This question focuses on how often the student data system(s) that drive instructional

improvement are actually used to support instructional decision making in your district. How

often do staff in the district office (non school-based staff) perform the following activities

related to student data using an electronic data system(s)? (Mark your best estimate for

each row.)

18.

2 to 4

Times a

Year

Monthly

or More

Annually Often

a. Analyze student achievement by grade-level, district wide

or by school.

Use data in the districts' electronic student data system(s) to:

b. Analyze student achievement data over time (i.e., to identify

trends).

Track other measures of student progress (e.g., benchmark

and diagnostic tests).

c.

Never

d. Examine achievement gaps between groups of students

(e.g., NCLB subgroups).

e. Track school performance (e.g., to estimate AYP for

schools).

f. Track graduation rates by school.

g. Track student readiness for promotion or graduation (e.g.,

percent on track to graduate or advance).

Inform student placement in courses or special programs or

support services (e.g., remedial math, gifted program,

tutoring).

h.

i. Inform teachers about individual student's instructional

needs in terms of specific skills or content.

j. Monitor student attendance.

k. Examine district or school climate data (e.g., student

perceptions, satisfaction levels of staff and parents).

l. Inform parents about student progress (e.g., test scores on

district test, areas of strengths and weaknesses,

satisfactory course completion).

25455

n=409-415

5.9 24.9 44.4 24.8

(2.30) (4.17) (4.67) (3.85)

7.7 38.0 43.1 11.3

(2.58) (4.60) (4.68) (2.64)

12.7 13.9 50.6 22.9

(3.28) (3.17) (4.69) (3.84)

6.8 43.4 38.9 10.9

(2.33) (4.68) (4.60) (2.39)

4.2 46.2 40.2 9.4

(1.73) (4.71) (4.59) (2.61)

10.4 68.3 18.2 3.0

(2.96) (4.34) (3.60) (1.27)

16.9 33.2 36.7 13.2

(3.72) (4.43) (4.49) (3.19)

15.6 26.8 39.1 18.4

(3.57) (4.12) (4.55) (3.70)

18.7 18.5 32.7 30.0

(3.80) (3.71) (4.29) (4.33)

5.7 2.7 15.5 76.1

(2.30) (1.25) (3.46) (4.04)

30.4 43.8 19.7 6.0

(4.43) (4.58) (3.82) (2.30)

11.9 13.7 42.9 31.5

(3.15) (3.30) (4.65) (4.35)
NTA District Survey 2007-08 Page 13 of 20

ID No.

How often are electronic student data systems that drive instructional improvement used by

staff in the district office to discuss or make decisions in the following areas? (Mark your

best estimate for each row.)

19.

2 to 4

Times a

Year

Monthly

or More

Annually Often

a. Guide curricular changes or curriculum development (e.g.,

align curriculum with content standards).

Use data in the districts' electronic student data system(s) to:

b. Develop benchmark assessments aligned with curriculum.

Evaluate teacher performance (e.g., assessing classroom

performance, evaluating teachers' instructional practices).

c.

Never

d. Evaluate principal performance.

e. Inform instructional practice (e.g., tailoring instruction to

meet student needs, managing instructional pacing).

f. Identify promising instructional programs (e.g., measuring

program effectiveness).

g. Inform professional development offerings for school staff to

enhance instructional quality.

Target individual teachers for specific professional

development.

h.

i. Track teacher qualifications (e.g., to determine highly

qualified status).

j. Inform resource allocation to improve instruction (e.g., which

schools/students receive which programs, which staff work

in which school/with which students).

k. Meet accountability reporting requirements.

l. Use data for other purposes (specify):

REMINDER: There may have been more than one respondent needed to complete this survey.

Please remember to review and complete Question 2 of the survey.

25455

n=410-414

13.2 29.4 40.8 16.6

(3.16) (4.21) (4.65) (3.46)

18.1 35.4 36.3 10.2

(3.52) (4.56) (4.56) (2.63)

34.6 23.9 33.4 8.0

(4.45) (4.02) (4.48) (2.34)

34.7 43.5 21.0 0.8

(4.51) (4.65) (3.91) (0.30)

20.3 18.1 44.0 17.6

(3.83) (3.60) (4.68) (3.34)

24.0 38.1 27.1 10.9

(4.11) (4.59) (4.13) (2.81)

15.9 20.4 39.6 24.0

(3.46) (3.82) (4.55) (4.09)

30.2 30.1 33.9 5.8

(4.17) (4.45) (4.52) (1.77)

20.3 45.2 25.5 8.9

(3.72) (4.68) (4.17) (2.57)

21.6 46.7 20.6 11.0

(3.75) (4.70) (3.63) (2.98)

7.6 42.1 29.5 20.9

(2.56) (4.66) (4.27) (3.74)

9.3 46.8 20.4 23.5

(0.00) (0.00) (0.00) (0.00) (n=12)
NTA District Survey 2007-08 Page 14 of 20

ID No.

D. Capacity-Building for Data Use

Previous sections asked about the type of data in your district's student data systems. This

section asks about your district's efforts to build capacity at the district- and school-levels to

support using data to improve instruction.

Indicate the emphasis your district has put on each strategy over the past 2 years (2005-06 to

2006-07) to promote data-driven decision making. (Mark one response for each row.)

20.

Some

Emphasis

Major

Emphasis

No

Emphasis

a. How to design/acquire an information management system to

support data analysis (that generates timely data and makes

useful data accessible to staff at all levels of the system-user

friendly system).

District Strategies

b. Building district staff capacity for data analysis and interpretation.

c. Establishing a process for continuous improvement (e.g.,

developing measurable goals, measuring progress, reflection,

making refinements).

d. Establishing a culture of data use at the school level (e.g., explicit

norms and expectations regarding data use, creating a safe

climate for data use, mutual accountability among staff).

e. Designing professional development and capacity-building

activities for schools' data-driven decision making (e.g.,

school-level professional development, modeling data use,

providing time to discuss data, providing tools to interpret and act

on data).

25455

n=415-416

9.4 44.6 46.0

(2.77) (4.67) (4.66)

9.5 47.3 43.1

(2.93) (4.69) (4.60)

4.0 38.0 58.0

(2.01) (4.59) (4.67)

4.5 45.9 49.7

(2.00) (4.69) (4.68)

5.9 41.1 53.0

(2.29) (4.60) (4.68)
NTA District Survey 2007-08 Page 15 of 20

ID No.

Does your district provide or provide support for any of the following training (e.g., data retreats,

workshops, data coaches) to increase school-level capacity in data-driven decision making to

improve instruction? If yes, what proportion of schools are involved or have been involved as of

the current school year (2007-08)? (Mark your best estimate for each row.)

21.

More

than half

of the

schools,

but not

all

All of

the

schools

a. Training school staff on the basic functions of the data

system (e.g., accessing and downloading data, data

queries).

District-supported training to increase school-level

capacity includes:

Proportion of Schools

Involved

Half or

less

than

half of

the

schools

District

Has Not

Provided

This

Training

b. Training school staff on data entry to improve data

accuracy.

c. Training school staff on data management and security.

d. Training principals or other building administrators on using

the data system to analyze student achievement.

e. Training principals or other building administrators on using

data to change instructional practice (e.g., tools for

translating data into practice).

f. Training principals an other building administrators on how

to provide leadership for data-driven decision making

practices in their school (e.g., modeling data use, leading

data discussions).

g. Training teachers on using the data system to analyze

student achievement.

h. Training teachers on using data to change instructional

practice (e.g., tools for translating data into practice,

collaborating with colleagues to discuss data).

25455

n=412-416

9.9 14.6 8.8 66.6

(2.94) (3.54) (2.58) (4.51)

16.1 12.2 8.4 63.3

(3.67) (3.27) (2.57) (4.61)

23.7 11.7 8.3 56.2

(4.17) (3.26) (2.57) (4.69)

8.8 8.8 9.0 73.4

(2.77) (2.77) (2.77) (4.26)

14.2 9.8 12.0 64.0

(3.43) (2.94) (3.13) (4.58)

17.9 12.9 9.3 59.9

(3.78) (3.28) (2.59) (4.64)

18.6 14.2 14.1 53.1

(3.79) (3.30) (3.01) (4.68)

18.1 15.0 13.9 53.0

(3.79) (3.32) (3.16) (4.69)
NTA District Survey 2007-08 Page 16 of 20

ID No.

In addition to training described above, does your district do any of the following activities

intended to increase school-level capacity in data-driven decision making to improve

instruction? If yes, what proportion of schools are involved or have been involved as of the

current school year (2007-08)? (Mark your best estimate for each row.)

22.

More

than half

of the

schools,

but not

all

All of

the

schools

District activities to increase school-level capacity

include:

Proportion of Schools

Involved

Half or

less

than

half of

the

schools

District

Has Not

Done

This

Activity

a. Making technical experts (in systems, networks, databases)

available to schools to support system use.

b. Providing models to schools to illustrate how to use data in

allocating resources and designing school improvement

activities (e.g., school improvement template, providing

assistance in analyzing and revising the school budget).

c. Making data analysis experts available to school staff such

as data coaches.

e. Providing an online database of lesson plans and planning

resources linked to academic standards and assessment

results.

f. Providing a web-accessible library of diagnostic or

benchmark assessments (for downloading) linked to

academic standards.

g. Providing teachers research-based guidance on

differentiating instruction on the basis of student

assessment data.

PROVIDING RESOURCES / ASSISTANCE TO SCHOOLS

d. Using data to identify professional development activities for

schools identified for improvement (e.g., to address school

improvement needs).

Question 22 continues on next page.

25455

n=410-415

19.6 10.1 5.7 64.6

(3.90) (2.95) (2.04) (4.56)

42.5 5.5 9.4 42.6

(4.69) (2.04) (2.60) (4.59)

50.2 6.2 11.4 32.1

(4.63) (2.30) (2.98) (4.06)

23.8 16.9 9.4 50.0

(4.10) (3.59) (2.77) (4.70)

39.6 13.2 6.0 41.2

(4.58) (3.29) (2.30) (4.61)

53.2 5.7 6.8 34.3

(4.67) (2.05) (2.32) (4.41)

26.4 13.0 10.0 50.6

(4.25) (3.16) (2.60) (4.70)
NTA District Survey 2007-08 Page 17 of 20

ID No.

j. Providing teachers with processes or tools to effectively

utilize data for instructional purposes (e.g., template for how

to talk about data with colleagues).

k. Requiring all or particular schools to follow specific

data-driven decision making practices in their school

improvement plans (e.g., identifying targets, monitoring their

data).

l. Requiring "data conferences" between individual principals

and their supervisors.

n. Tracking teacher use of the data system.

m. Following up to determine if schools have implemented

instructional changes prescribed as a result of data analysis

activities.

IMPLEMENTING POLICIES / PRACTICES

h. Requiring instructional coaches to explicitly incorporate data

use and train teachers in data use as part of their job.

Paying for incentives for teachers to use or obtain training in

data-driven decision making (e.g., paying for dedicated time

for school staff to review data).

i.

22. (Continued from previous page)

District activities to increase school-level capacity

include:

District

Has Not

Done

This

Activity

Half or

less

than

half of

the

schools

More

than half

of the

schools,

but not

all

Proportion of Schools

Involved

Half or

less

than

half of

the

schools

More

than half

of the

schools,

but not

all

All of

the

schools

For how many years has your district been actively engaged in helping schools to use data to

improve instruction? (Mark one response for each row.)

23.

a. Providing professional development for teachers

and principals on the use of data to improve

instructional practices.

Number of Years of District Support

District support activities to help schools to use data:

b.

c.

Providing resources (such as models, consultants)

to schools to support the use of data to inform

instruction.

Implementing policies and requirements to use data

or providing incentives for data use.

6 or

more

years

3 to 5

years

1 to 2

years

Planning,

but have

not started

No

Plans in

This

Area

25455

55.9 7.0 11.1 26.1

(4.44) (2.07) (2.61) (3.85)

70.1 8.8 3.0 18.1

(4.13) (2.57) (1.25) (3.48)

35.1 15.8 9.2 40.0

(4.55) (3.45) (2.58) (4.54)

31.3 11.1 6.6 51.0

(4.50) (2.81) (2.31) (4.69)

52.1 6.0 5.8 36.2

(4.60) (2.05) (2.05) (4.35)

40.2 13.6 7.3 38.8

(4.65) (3.29) (2.09) (4.50)

69.8 5.8 3.6 20.8

(4.06) (2.05) (1.29) (3.62)

n=414-415

4.5 17.0 29.9 27.3 21.4

(2.01) (3.77) (4.33) (4.11) (3.62)

9.0 17.8 25.2 28.2 19.8

(2.76) (3.71) (4.10) (4.12) (3.61)

19.4 31.0 17.0 17.0 15.6

(3.71) (4.46) (3.46) (3.33) (3.32)
NTA District Survey 2007-08 Page 18 of 20

ID No.

In general, how much does your district need examples of good practice in the following

areas? (Mark one response for each row.)

24.

Some

Need

Great

Need

Little Need

(we know

how to do

this)

a. Using assessment data to identify gaps in student achievement (e.g.,

standards that individual students or groups of students don't meet).

Areas of need include:

b. Adapting instructional activities to meet students' individual needs (e.g.,

modifying lesson plans to teach students at different ability levels).

c. Developing curriculum-embedded formative assessments (e.g.,

designing assessments to use with instruction).

d. Examining student data to identify which practices work best for which

students (e.g., comparing the performance of students receiving

instructional programs).

e. Collaborating and sharing ideas with colleagues regarding data inquiry

and analysis issues (e.g., group facilitation techniques).

f. Communicating with parents about student progress.

g. Structuring the district organization and practices to support

data-driven decision making.

To what extent are the following typical problems current barriers to the expanded use of

data-driven decision making in your district? (Mark one response for each row.)

25.

Minor

Barrier

Not a

Barrier

a. Lack of sufficient hardware (servers, computers, peripheral devices, etc.).

Current barriers in our district include:

b. Out-of-date hardware.

c. Internet connections that are not fast or reliable enough.

d. Unreliable or inaccurate data in the system.

e. Data stored in forms that are difficult to access, manage, and interpret.

f. Information located in multiple disparate data bases that make it difficult to

link data for analyses (i.e., lack of interoperability).

g. Lack of district-wide unique student identification numbers that are

consistent from year-to-year.

Major

Barrier

DATA SYSTEM AND TECHNOLOGY RESOURCES

h. Lack of district-wide unique teacher identification numbers that are

consistent from year-to-year.

i. Inability to provide adequate safeguards and security for the data.

j. Lack of funding to expand or improve the student data system.

k. Lack of trained technical staff available for product and service acquisition,

installation, or equipment maintenance.

Question 25 continues on next page.

25455

n=414

28.9 50.1 21.0

(4.12) (4.69) (3.91)

16.2 49.2 34.7

(3.45) (4.69) (4.49)

18.5 48.1 33.4

(3.59) (4.68) (4.48)

16.6 46.7 36.7

(3.46) (4.65) (4.59)

19.9 54.3 25.8

(3.61) (4.67) (4.18)

36.8 49.3 13.9

(4.53) (4.69) (3.29)

19.6 58.9 21.5

(3.60) (4.61) (3.91)

n=413-416

52.9 37.6 9.4

(4.68) (4.56) (2.58)

(4.70) (4.55) (2.98)

(4.39) (4.18) (2.04)

(3.75) (3.37) (2.01)

(4.69) (4.05) (4.07)

(4.55) (4.05) (4.53)

(2.03) (1.69) (1.17)

(3.46) (3.16) (1.64)

(4.46) (4.46) (0.34)

(4.01) (4.48) (2.61)

(4.26) (4.68) (4.00)

50.6 37.8 11.6

67.6 26.6 5.8

73.8 21.4 4.8

47.9 29.2 22.9

35.8 28.4 35.8

94.5 4.2 1.4

83.1 13.8 3.1

68.2 30.9 0.8

23.8 34.5 41.7

30.3 46.8 22.9
NTA District Survey 2007-08 Page 19 of 20

ID No.

25. (Continued from previous page)

Minor

Barrier

Not a

Barrier

l. Lack of resources to train school staff on how to use data to support

instructional improvement (e.g., providing data analysis specialists,

professional development funds).

Current barriers in our district include:

m. Lack of teacher preparation on how to use data for instructional decision

making (e.g., data interpretation skills).

n. Lack of technical skills of school staff to access or utilize electronic data

systems (e.g., technical proficiencies).

o. Lack of building administrator preparation on how to use data for

data-driven decision making.

Major

Barrier

LOGISTICAL / OTHER BARRIERS

p. Lack of time for school staff to conduct data-driven decision making

activities (e.g., to reflect on or use data, for teacher collaboration).

q. Lack of a clear vision or strategic plan for data-driven decision making (e.g.,

as part of a systematic approach to continuous improvement).

r. Lack of district leadership support for data-driven decision making (e.g.,

explicit norms and expectations regarding data use).

s. Lack of communication or sharing of data across departments within the

district.

t. Policies that preclude direct access by school staff to data systems or

individual student-level data.

u. Lack of incentives for data use by district or school staff.

REMINDER: There may have been more than one respondent needed to complete this survey.

Please remember to review and complete Question 2 of the survey.

25455

18.9 45.8 35.2

15.7 55.6 28.6

10.8 69.5 19.7

30.7 53.5 15.9

8.1 41.2 50.7

35.9 49.5 14.6

60.9 30.0 9.1

33.8 56.1 10.1

62.4 33.4 4.2

36.7 49.8 13.4

(3.60) (4.68) (4.53)

(3.45) (4.63) (4.12)

(2.79) (4.13) (3.49)

(4.42) (4.69) (3.33)

(2.55) (4.63) (4.69)

(4.49) (4.69) (3.31)

(4.57) (4.32) (2.58)

(4.44) (4.63) (2.60)

(4.57) (4.48) (1.69)

(4.49) (4.68) (3.01)
NTA District Survey 2007-08 Page 20 of 20

ID No.

Please feel free to note any special circumstances in your district or other comments

you might have:

THANK YOU!

If you have any questions about this survey, please contact Angeline Reyes at

nta-survey@sri.com or 650-859-5927. Please use the enclosed envelope to return the

completed survey to SRI International.

All study participants will be notified of the availability of the final report once it is

completed. Thank you again for your time.

25455