Traveling

Through

Time


The Forum Guide to 

Longitudinal Data Systems 

Advanced LDS Usage 

National Cooperative Education Statistics System
 

The National Center for Education Statistics established the National Cooperative Education Statistics System 

(Cooperative System) to assist in producing and maintaining comparable and uniform information and data on early 

childhood education and elementary and secondary education. These data are intended to be useful for policymaking at 

the federal, state, and local levels. 

The National Forum on Education Statistics (the Forum), among other activities, proposes principles of good practice to 

assist state and local education agencies in meeting this purpose. The Cooperative System and the Forum are supported 

in these endeavors by resources from the National Center for Education Statistics (NCES). 

Publications of the Forum do not undergo the formal review required for products of NCES. The information and 

opinions published here are those of the Forum and do not necessarily represent the policy or views of the U.S. 

Department of Education or the National Center for Education Statistics. 

July 2011 

This publication and other publications of the Forum may be found at the NCES websites. 

The NCES World Wide Web Home Page is http://nces.ed.gov 

The NCES World Wide Web Electronic Catalog is http://nces.ed.gov/pubsearch 

The Forum World Wide Web Home Page is http://nces.ed.gov/forum 

This publication was prepared in part under contract number ED-06-CO-0056 with the Council of Chief State School 

Officers. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. 

Government. 

Suggested Citation: 

National Forum on Education Statistics. (2011). Traveling Through Time: The Forum Guide to Longitudinal Data Systems. 

Book Four of Four: Advanced LDS Usage (NFES 2011–802). Washington, DC: National Center for Education Statistics, 

Institute of Education Sciences, U.S. Department of Education. 

For ordering information on this report, write: 

ED Pubs 

U.S. Department of Education 

P.O. Box 22207 

Alexandria, VA 22304 

Or call toll free 1–877–4ED–PUBS; or order online at http://www.edpubs.gov 

Technical Contact: 

Tate Gould 

202–219–7080 

tate.gould@ed.gov 

ii Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
The longitudinal Data 

Systems Task Force 

A volunteer task force of the National Forum on Education Statistics (the Forum) 

produced this document. It was developed through the National Cooperative Education 

Statistics System and funded by the National Center for Education Statistics (NCES) of 

the U.S. Department of Education. 

Chair 

Bruce E. Dacey 

Delaware Department of Education 

Dover, Delaware 

Task Force Members 

Nancy Burke Nancy J. Smith 

Century Elementary School DataSmith Solutions, LLC 

Grafton Public School District Vienna, Virginia 

Grafton, North Dakota 

Steve Smith 

Corey Chatis Cambridge Public School District 

(formerly of the Tennessee Cambridge, Massachusetts 

Department of Education) 

Chatis Consulting Raymond Yeagley 

Seattle, Washington Northwest Evaluation Association 

Lake Oswego, Oregon 

Laurie Collins 

(formerly of the SIF Association) 

Mizuni, Inc. 

Addison, Texas 

Patrick Sherrill 

Office of Planning, Evaluation, 

and Policy Development 

U.S. Department of Education 

Washington, DC 

Consulting Author 

Anthony Garofano 

Quality Information Partners 

Fairfax, Virginia 

Project Officer 

Tate Gould 

National Center for Education Statistics 

Washington, DC 

Book Four of Four:Advanced LDS Usage iii 
 

acknowledgments
 

The members of the Longitudinal Data Systems Task Force of the National Forum on 

Education Statistics would like to thank everyone who reviewed drafts of this document or 

otherwise contributed to its development. This includes the Forum Steering Committee and 

Technology (TECH) Committee and the members of the National Forum on Education 

Statistics. The Task Force wishes to specifically acknowledge the contributions of 

Lee Hoffman 

National Center for Education Statistics 

Ghedam Bairu 

National Center for Education Statistics 

Maureen Wentworth 

Council of Chief State School Officers 

Beth Juillerat and Marsha Ward 

Ohio Department of Education 

Susan VanGorden 

Lakota Local School District, Ohio 

Mark Russell 

formerly of the Idaho State Department of Education 

Khaled Falah and Tom Cosgrove 

formerly of the Washington, DC Office of the State Superintendent of Education 

Neal Gibson 

Arkansas Department of Education 

Mwarumba Mwavita 

Oklahoma State University and Western Heights Public Schools, Oklahoma 

Joe Kitchens 

Western Heights Public Schools, Oklahoma 

Mickey Garrison 

Oregon Department of Education, Oregon 

Jim Campbell 

AEM Corporation (formerly of the SIF Association) 

Frances Erlebacher provided overall editing and the Creative Shop formatted and 

designed this publication. 

iv Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

Foreword
 

The National Forum on Education Statistics (the Forum) is pleased to present Traveling 

Through Time: The Forum Guide to Longitudinal Data Systems. This document, Book Four 

of Four: Advanced LDS Usage, is the fourth and final installment of this Forum series of 

guides on longitudinal data systems (LDS). One goal of the Forum is to improve the 

quality of education data gathered for use by policymakers and program decisionmakers. 

An approach to furthering this goal has been to pool the collective experiences of Forum 

members to produce “best practice” guides in areas of high interest to those who collect, 

maintain, and use data about elementary and secondary education. Developing LDSs 

is one of those high-interest areas. These systems hold promise for enhancing both the 

way education agencies use data to serve students and the way they do business, from the 

policy level to the school office and into the classroom. 

LDSs are increasingly becoming the state of the art in education data. These systems 

move us from relying on blunt, aggregate, snapshot student data; to detailed and timely, 

student-level data that reflect the student’s entire academic history. An LDS makes it 

possible to not only monitor the success of individual students, but also to identify trends 

in those students’ education records. Freeing educators from guesswork and lessening the 

burden of painstaking data analysis, these systems provide powerful and timely insight 

about students and allow educators to tailor instruction to better meet individual needs. 

An LDS can reveal with great clarity what effects our policies, programs, and decisions have 

on schools. These systems allow agencies to track students across institutions to facilitate 

appropriate course placement and to determine who has transferred and who has dropped 

out. Longitudinal data systems also offer a new level of sophistication at the business 

level that can streamline operations; improve data quality; and free up valuable resources 

previously allocated to inefficient data entry, maintenance, and reporting practices. 

For these and others reasons, states should continue to introduce, develop, and 

expand their LDSs. The Traveling Through Time: The Forum Guide to Longitudinal Data 

Systems series is intended to help state and local education agencies meet the many 

challenges involved in developing robust systems, populating them with quality data, 

and using this new information to improve the education system. The series introduces 

important topics, offers best practices when possible, and directs the reader to additional 

resources related to the LDS development process. In sum, it is intended to help agencies 

establish LDSs that will have lasting, far-reaching impact on the education system and on 

students’ lives. For a description of the entire guide series, see appendix A. 

Book Four of Four:Advanced LDS Usage 

This fourth book in the guide series focuses on issues important to the effective use of 

longitudinal data. 

 The introduction explains the purpose of book four, as well as the format and 

conventions used throughout the series. 

 Chapter 1 takes a historical look at data use, comparing what was done in the past 

with what is possible today thanks to detailed longitudinal data. 

 Chapter 2 describes several preconditions to effective data use. 

Book Four of Four:Advanced LDS Usage v 
 

 

 

 Chapter 3 summarizes the many uses of longitudinal data, and the types of users 

who can leverage the information. 

 Chapter 4 offers tips on implementing effective training and professional 

development programs on the use of longitudinal data. 

 Chapter 5 discusses the road ahead for LDS development and use. 

The appendices include an overview of the four books in this series, references, 

additional resources, and other relevant Forum and NCES resources. 

The National Forum on Education Statistics 

The work of the Forum is a key aspect of the National Cooperative Education Statistics 

System (the Cooperative System). The Cooperative System was established to produce 

and maintain, with the cooperation of the states, comparable and uniform educational 

information and data that are useful for policymaking at the federal, state, and local levels. 

To assist in meeting this goal, the National Center for Education Statistics (NCES), within 

the U.S. Department of Education, established the National Forum on Education Statistics 

(the Forum) to improve the collection, reporting, and use of elementary and secondary 

education statistics. The Forum deals with issues in education data policy, sponsors 

innovations in data collection and reporting, and provides technical assistance to improve 

state and local data systems. 

Development of Forum Products 

Members of the Forum establish task forces to develop best-practice guides in datarelated areas of interest to federal, state, and local education agencies. They are assisted 

in this work by NCES, but the content comes from the collective experience of the state 

and school district task force members who review all products iteratively throughout 

the development process. Documents prepared, reviewed, and approved by task force 

members undergo a formal public review. This public review consists of focus groups with 

representatives of the product’s intended audience, review sessions at relevant regional 

or national conferences, or technical reviews by acknowledged experts in the field. In 

addition, all draft documents are posted on the Forum website prior to publication so 

that any interested individuals or organizations can provide feedback. After the task force 

oversees the integration of public review comments and reviews the document a final time, 

publications are subject to examination by members of the Forum standing committee 

sponsoring the project. Finally, the entire Forum (approximately 120 members) reviews and 

formally votes to approve all documents prior to publication. NCES provides final review 

and approval prior to publication. 

vi Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Contents
 

The Longitudinal Data Systems Task Force ............................... iii
 

Acknowledgments .................................................. iv
 

Foreword .......................................................... v
 

Introduction......................................................... 1
 

Chapter 1. Data Use: Where Has It Been, 


Where Is It Going? .................................................. 5
 

A Case Study: From Inches to YARDS .................................. 11
 

Chapter 2. Prerequisites for Effective Data Use.................. 15
 

A Case Study: Soaring to Western Heights ............................... 20
 

Chapter 3. What Do Stakeholders Get? 


LDS Data Uses and Users ......................................... 25
 

A Case Study: The Buzz on Arkansas’s Data Visualization Tool............... 36
 

Chapter 4. Professional Development and Training 


for Effective Use of LDS Data ..................................... 39
 

A Case Study: Oregon’s Trail to Data Use................................ 45
 

Chapter 5. Conclusion............................................. 47
 

Appendices ......................................................... 49
 

A. Overview of the LDS Guide Series ................................... 49
 

B. References ...................................................... 51
 

C. Additional Resources.............................................. 53
 

D. Forum and Other NCES Resources .................................. 63
 

Book Four of Four:Advanced LDS Usage vii 

 

introduction 

T he three preceding books of this series covered the groundwork for 

successful longitudinal data systems (LDS), creating a foundation of good 

data governance; envisioning the desired system with broad stakeholder involvement; 

developing a robust and sustainable system that collects and maintains valuable new data; 

ensuring data quality by training staff, enforcing the data governance process, and adhering 

to data standards; and securing the data and protecting privacy. But, as with any useful 

tool, building an LDS is not enough: the system and its data must be put to good use. The 

vast new inventory of longitudinal, student-level data education agencies will amass should 

be used frequently and effectively to help policymakers determine what works and ensure 

investment in the right educational inputs; help educators target instruction to improve 

student outcomes; give students and parents access to valuable, real-time information; 

and streamline agency and school operations. This book focuses on the ways the data can 

be used by stakeholders, as well as the tools and training that will allow users to access 

and turn student-level longitudinal data into actionable information at all levels of the 

education system. 

Figure 1 lays out the major issues discussed in each of the four books in this series. 

For more information on the purpose, format, and intended audience groups of this guide 

series, see Book One: What is an LDS? 

Book Four of Four:Advanced LDS Usage 1 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Book III: Effectively 

Figure 1. Summary of the Forum Guide to LDS series 

Book I:What Is an LDS?
 

 Understanding what an LDS is (and is not) 

 Appreciating the organizational steps needed to 

institute and effectively use an LDS 

 Identifying the technical features and 

capabilities of an effective LDS and the 

additional features that can enhance the 

system’s utility 

 Recognizing the benefits of an LDS 

Managing LDS Data 

 Defining governance structure 

 Defining roles and responsibilities 

 Collaborating to improve data quality and 

streamline operations 

 Managing changes to the system 

 Training staff to ensure data quality 

 Auditing/validating data at all levels 

 Establishing/following data standards 

 Securing data to protect privacy 

 Providing users access to key data 

Book II: Planning and 

Developing an LDS 

 Engaging stakeholders 

 Describing the current system 

 Envisioning the desired system 

 Defining needs, including data and functionality 

 Gaining buy-in and funding 

 Building relationships 

 Writing an RFP 

 Building or buying a system or components 

 Transferring knowledge (e.g., from developers 

to staff) 

 Defining and measuring success 

 Refining the system 

Book IV:Advanced 

LDS Usage 

 Collecting, storing, and delivering key data 

 Developing useful reports to fulfill common 

data requests and needs 

 Developing user-friendly data tools to facilitate 

access and analysis 

 Training users to utilize the technology 

 Building awareness, understanding, and 

analytical capacity 

2 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Conventions 

Throughout this series, important terms and topics will be highlighted in sidebars. Notable 

subject matter will be easily identified by the following icons: 

Definitions LDS lore Key points

(fictional tales) 

Bright ideas Tips District 

difference 

Book Four of Four:Advanced LDS Usage anced LDS Usage 3 

Data
use:
Where
has
it
Been,


Where
iS
it
going?


Chapter 1


While
education
agencies
have
long
been
collecting
large
quantities
of
data,


these
usually
aggregated,
group­level
data
have
mostly
been
used
to
meet


state
and
federal
reporting
requirements
for
funding
and
accountability
purposes,
and
to


periodically
provide
blurry,
high­level
glimpses
of
the
reality
on
the
ground.
Data
use
to


improve
education
services
at
both
the
policymaking
and
classroom
levels,
on
the
other


hand,
has
been
rare.


Data
Use:
More
than
just
IT


I

f you implement it, they may come...but will they like what they find? As stressed 

earlier in this guide series, an LDS should not simply be an IT project, and data 

use is the reason why.While technical staff will develop the system, an LDS should 

be designed by and for the end users if it is to meet their needs. 

The IT staff can translate the business needs into a system that includes 

all the specified data elements.They can follow the defined business rules 

as requested and validate the data; and, in the end, they can create a system 

that “works” in a technical sense. But ultimately, the end user may find the 

system lacking if it is merely a collection of data and government reports.And, 

without useful tools and stakeholders who understand how to use these data 

in a meaningful way, the LDS will likely be under­utilized and fall short of its 

transformative potential. In a sense, it could end up being just an IT project after 

all.To avoid this, the system’s design and development should be focused on using 

data not only to improve operations, but to ensure greater student success. 

Book Four of Four:Advanced LDS Usage 5 
With a sophisticated 

LDS, data become a 

universal resource, 

not just a local chore. 

Many factors have contributed to this reality. Educators have lacked data that 

were timely and granular enough to be very useful at the classroom and student levels. 

High-level snapshot data were often available, but educators and researchers did not 

have access to the detailed longitudinal data needed for deeper and more meaningful 

analysis. Motivated educators hungry for data found accessing and manipulating the 

information difficult as it required collecting data from disparate sources and points 

in time, then painstakingly analyzing them by hand or through other time-consuming 

means. In general, educators have not been taught how to use data effectively (during 

pre- or in-service teacher preparation programs), largely because institutional cultures 

simply have not valued and encouraged data use (Wayman 2005). Education agencies 

have made laudable advancements in recent years, developing LDSs with many or all of 

the major components and functionalities. Yet these systems have often failed to lead to 

the effective use of the new student-level, longitudinal data; and, thus, to substantially 

benefit the full range of potential stakeholders. 

But the landscape is quickly changing. At both the state and local levels, carefully 

planned, skillfully developed LDSs—in combination with user-friendly tools and strong 

cultures of data use—are moving the education community into an era rife with objective 

inquiry. Agencies are beginning to transcend the historical purposes of data collection, 

empowering stakeholders throughout the education community by expanding their use 

of data to improve their work and student outcomes. They are increasingly using data 

to inform decisions about everything from policymaking and resource allocation, to 

professional development programming and targeting, to instructional planning and 

intervention at the classroom and student levels. As increasingly sophisticated LDSs are 

implemented and expanded, and greater use of data is fostered among stakeholders, major 

changes are occurring. 

Data: From Local Burden to Universal Resource 

Historically, many local education staff might have summed up their relationship with 

data as “we make them, they use them.” That is, school and district staff collected and 

reported data to comply with state and federal requirements and receive funding, but rarely 

used them to enhance their own work. Making data locally available in a useful and timely 

manner was difficult due to technological challenges, and often was not a priority. With 

the advent of robust and accessible LDSs, however, the local data burden is increasingly 

leading to greater reward.* When useful data are made available to local stakeholders, 

the dynamic shifts to “we make them, everybody uses them.” From the local perspective, 

this is a fundamental repurposing of education data from an administrative requirement 

to a valuable resource—an informational asset that empowers stakeholders at all levels to 

improve students’ educational outcomes. Indeed, data use is growing among educators and, 

already, many administrators and teachers no longer make significant decisions without 

objective, high quality facts in front of them. 

*Compounding this change is the decrease in burden made possible by interoperability solutions that reduce data entry 

errors and eliminate the need for redundant data entry into multiple systems. 

6 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Policymaking and Administration: From Guesswork to 

Evidence-Based Decisionmaking 

Historical use of aggregate data to gauge performance, evaluate success, and plan for 

improvement is slowly but surely giving way to the use of timely, detailed data that offer 

substantially greater precision and reliability. Armed with more granular, accurate facts 

about what is happening on the ground, policymakers and school leaders can better assess 

what is working and what is not. In effect, this enables them to allocate resources more 

efficiently and effectively. For instance, decisionmakers can more objectively and reliably 

gauge the real impact of policies and programs on student outcomes. And, by linking 

and aggregating longitudinal, student-level data to various educational inputs, leaders can 

determine which schools are most effectively teaching which subjects to which types of 

students (for example, those who have struggled academically with particular concepts and 

skills, etc.). Professional development needs are also more easily identified and targeted 

to ensure all educators are well-qualified. In effect, leaders can trade hunches for facts, 

supposition for the scientific method. 

Teaching: From Intuition, Isolation, and Rigidity to 

Objectivity, Collaboration, and Fluidity 

Teaching has traditionally been a private matter, largely dependent upon the instructor’s 

intuition and skills. Today, with detailed, relevant, and timely student-level data accessible 

through user-friendly tools and reports, as well as social networking processes and tools 

(online bulletin boards, professional learning communities, and other resources), teaching 

is quickly becoming a more objective and open endeavor. The reality is, no teacher can 

discern the patterns in every student’s performance and behavior, much less figure out 

how to meet individual needs most effectively—especially when they are responsible for 

a large number of pupils. Better data and analytical tools can clarify reality, simplify the 

information teachers have to process, reveal patterns in performance and the effects of 

their own actions, and remove biases from interpretation. And with more information 

available, more and more educators can collaborate to learn from the data, reflect on 

experiences, and share effective practices and instructional materials as evidenced by the 

data. Reliance on infrequent, summative tests is fading in favor of frequent, interim and 

formative assessments that measure growth, pinpoint deficiencies, and help teachers adjust 

instruction throughout the school year (see the “Balanced Assessment System” section in 

chapter 3 for more information). With the new insights revealed by the data, educators are 

better able to assess their methodology, differentiate instruction for groups and individual 

students, monitor student progress throughout the year, and continually make adjustments 

to better meet students’ needs. 

Book Four of Four:Advanced LDS Usage 7 
Teaching in this century 

T

he school year had just begun at Century Elementary School in Grafton, North Dakota. 

Children in this K–4 building had already taken assessments in math and language arts. 

Results were quickly distributed to staff and, based on the findings, pupils were placed in 

core and tier instruction groups with similarly scoring peers. 

Throughout the year, the students’ performance is continually and individually monitored for 

progress.Teachers meet on a regular basis with the school’s response to intervention (RTI) 

coach, guidance counselor, and principal to adapt instruction to students’ advancements and 

changing needs. 

“Teachers today must collaborate and work as a team using timely data,” says the principal. 

“School now follows a fluid model.” 

Data Sharing: From Partitions to Partnerships 

Data sharing among institutions was limited in the past and continues to pose a host of 

logistical challenges. Today, data sharing is a growing reality among schools as well as 

districts. In addition, K–12 education agencies share data with early childhood and 

postsecondary education, and other agencies such as labor and social services. Unique 

identifiers and improved communication among and across levels of the education system 

are providing a clearer picture of where students are, how they are progressing, and what 

their long-term outcomes are; as well as a detailed view of how their experiences relate to 

their success. For instance, what effects might early childhood education have on 

performance in elementary school? Elementary years on middle school? Middle on high 

school? High school on postsecondary education and workforce success? And for students 

who go on to be teachers, what effects might pre-service training have on their future pupils’ 

performance? Data sharing throughout the P–20 system can help provide the answers to 

these important questions. 

Asking the data: Then and now 

Longitudinal, student-level data help put reality under a microscope in order to understand 

the details of what is going on. This allows much more precise analysis of the effects of 

policymakers’ and educators’ actions, as well as other factors that contribute to educational 

success or failure. (For the types of questions these data can answer, see chapter 5 of Book 

One: What is an LDS?) 

8 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Table 1 illustrates the transformation enabled by student-level longitudinal data. 

The before-and-after presentation compares the types of questions traditional, aggregate 

snapshot data can answer about the education system (questions without an LDS); to the 

more detailed information available with an LDS. As the table shows, the data offer a 

better understanding of what is actually happening in schools. The questions in this table, 

taken directly from Information Won’t Be Used If No One Can See It (Dougherty 2008), are 

policy-level questions that rely on summative assessment data. More detailed questions can 

be answered with more detailed data, such as formative assessment data, input data, and 

other information. 

Table 1. Policymaker questions: Before and after development of a sophisticated LDS 

Book Four of Four:Advanced LDS Usage 

Data use Questions without an LDS Questions with an LDS 

Analyze  How many students were proficient in 8th  Disaggregated by prior achievement: How 

assessment grade? many students who were proficient in 8th 

results grade stayed proficient in 10th grade? 

 How many students scored below basic in 4th  What percentage of below-basic 4th graders 

grade? normally reach proficiency by 8th grade? 

 What percentage of this year’s high school  How is completing an AP course and passing 

seniors took at least one Advanced Placement the AP exam related to students’ 8th grade 

(AP) course and passed the corresponding AP achievement levels? 

exam? 

 Which schools and districts have the highest  Which schools and districts do the best job 

test scores within their demographic category? of producing growth among academically 

advanced students? Are these different from 

the schools and districts that do the best job 

of helping students who are academically 

behind catch up? 

Analyze  How many students graduated this year?  What percentage of students who completed 

graduation 8th grade four years ago graduated on time 

rates this year, taking into account transfers and 

dropouts? 

 How does the number of 12th graders in  What causes these differences, taking into 

each high school compare to the number account transfers, dropouts, and students held 

of 9th graders three years earlier? back a grade? 

 Does the number of graduates relative to 8th  How are students’ graduation, dropout, 

graders in this district appear to be declining? and transfer rates related to academic 

Were 8th grade test scores increasing or performance levels in 8th grade; and how are 

declining four years ago? those relationships changing over time? 

9 
Analyze course  What percentage of students in each grade  What percentage of students receiving course 

completion 

data 

received course credit for algebra II? credit for algebra II met college readiness 

benchmarks on the ACT* or earned a 

corresponding score on the SAT*? 

 What percentage of students who completed 

algebra II also demonstrated proficiency on an 

algebra II end-of-course exam? 

 What percentage of high school juniors and 

seniors took algebra II or courses beyond 

algebra II in the past two years? What 

percentage of students in the state’s colleges 

 How many of last year’s graduates who 

completed mathematics through at least 

algebra II in high school and entered college 

this year needed remediation in mathematics? 

needed remediation in mathematics this year? 

 What were 8th grade proficiency rates for  How do the answers to the questions above 

math two, three, and four years ago? vary for students based on their mathematics 

performance level in 8th grade? 

Compare K–12  What are the high school graduation trends in  What percentage of the students from this 

performance to 

postsecondary 

this district? What are the college enrollment 

trends in the region and state? 

district enroll in higher education within two 

years of high school graduation? 

success  What are the trends in student grades, majors,  What are the trends in college grades, majors, 

and graduation in the two- and four-year and graduation for students from this district; 

colleges in the region and state? and how are they related to those students’ 8th 

 What are the trends in the academic grade achievement levels, high school courseachievement levels and course-taking patterns taking patterns, and high school academic 

of this district’s high school students? achievement? 

Compare 

elementary 

and middle 

 What are the achievement trends in this 

district’s elementary, middle, and high 

schools? 

 How do students from this elementary school 

perform after they move on to middle school? 

How do these middle school students perform 

school 

performance 

on state assessments and college readiness 

tests in high school? 

to success in  What are the course-taking patterns in this  How many of the students from this middle school 

high school district’s high schools? take rigorous courses in specific subjects in high 

school, and how do they do in those courses? 

 What percentage of students are performing at  How does the performance of this school’s 

specific achievement levels in the middle and students in the next level of education relate to 

high schools that students leaving this school their academic achievement level when they 

normally attend? leave this school? 

SOURCE: Dougherty, C. (2008) Information Won’t Be Used If No One Can See It. 

Reprinted with permission of the Data Quality Campaign.

 *American College Testing (ACT), Scholastic Assessment Test (SAT) 

10 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
A
Case
Study:

From
Inches
to
 Part
I:
The
struggle
to
expand
data
use


YARDs
 T

he Cambridge Public Schools in Massachusetts had been 

working on its districtwide LDS for some time. Several years 

of student data were housed within the district’s local data 

warehouse—demographics, enrollment, attendance, discipline, 

classroom grades, state and local assessment results, etc. But 

despite this rich collection of data, use of the information was 

limited. In fact, only a few staff members in the entire district were capable of accessing the data to track down 

information, conduct analyses, and produce reports. And, aside from fulfilling reporting requirements, the district 

had used the data for only two purposes. 

First, they produced annual reports that compared student outcomes disaggregated by demographics. This 

established benchmarks to help the community see how well the school system was achieving its goals. Then, 

the district provided teachers with historical data snapshots of each student they would have the following 

year—a resource educators found very helpful in preparing for the new school year. 

Despite these two very productive data uses, district leaders knew that their inability to provide broader access to 

the system was a significant missed opportunity. They needed to provide administrators, teachers, researchers, 

parents, students, the public, and city programs and services with greater access to these valuable data. The 

district had a history with commercial technology solutions, but its experience suggested that, rooted in the 

business world, these products were inadequate to meet the unique needs of the education community. In spite 

of this, the district again turned its sights outward for help. 

Over the next year or so, the district tried to determine how they could more effectively use their data. They held 

meetings with various stakeholders and came up with a list of five main indicators of student success or failure that 

they wanted to track. They also knew they wanted to follow student cohorts and explore the factors related to their 

performance, and to extend analytical functionalities down to the classroom level. Armed with a basic idea of what 

they wanted, the district released a request for information (RFI) to see what solutions were available. They received 

over a dozen responses. After a few presentations, the staff culled out their favorite functionalities from the vendors’ 

recommendations and crafted a detailed request for proposals (RFP) to find the right solution. 

After hearing from the few respondents who said they could meet the requirements, the district chose a start­up 

company whose suite of technology solutions was designed to make education data more useful and accessible to 

stakeholders. The vendor promised it had solved many of the problems that had plagued the education data field 

for years, and was capable of delivering within a short time frame a flexible solution that would meet the district’s 

current requirements yet accommodate future needs. 

Confident in their choice, district leaders chose a number of modules from the vendor’s suite that would provide a 

wide range of functionalities: 

 a data warehousing solution would integrate and store data from various sources (e.g., state and 

local assessment data, data from other city agencies and services) and provide role­based access and 

extensive reporting and analysis capabilities down to the classroom level; 

 an online testing platform with reporting capabilities would allow educators to easily craft online 

assessments tied to academic standards, and to then quickly pull up detailed analyses of the results; 

 an instructional management system would put a host of resources at users’ disposal to help educators 

create lesson plans and assign work to students; and 

Book Four of Four:Advanced LDS Usage 11 
 a social networking tool would connect students, parents, teachers, principals, and district administrators 

in a secure environment. With a look and feel similar to leading modern social media and networking 

tools, the application’s familiarity would hopefully make the tool second nature to many users. 

These modules would be rolled out individually and accessible through a single, user­friendly interface where 

users would be able to view longitudinal data, gauge progress towards education goals, access testing and 

instructional resources, and easily communicate with one another. 

Now it was time to put the vendor and its solution to the test. The district moved forward with cautious optimism. 

Part
II:
Rollout,
training,
and
a
look
to
the
future


Rollout of the Youth and Resource Development system (YARDs), as the district’s system was named, was planned 

in stages, with broad expansion of access to longitudinal data as priority number one. A speedy timeline was 

set for the initial phase and the vendor was able to quickly integrate ten years worth of longitudinal student data 

from various sources, correcting many data quality issues in the process. Within phase one, teachers, principals, 

and district administrators were also given access to customized dashboards to provide access to this wealth of 

information: 

 District administrators received easy access to up­to­date information on districtwide operations and 

performance data, drillable down to the school, classroom, and student levels. 

 School principals were given school and classroom overview data that can be disaggregated down to the 

classroom and student levels. 

 Teachers gained easy access to classroom­ and student­level information such as demographics, 

attendance, assessment performance, participation in programs offered by the district or other city 

agencies, and alerts about struggling students. Teachers can access these data during the summer to 

learn about the students they will soon have in class. Whereas previously teachers needed to request 

most data from the school office, the new software put even a wide variety of historical data at their 

fingertips. 

Through these dashboards, data coaches and other staff were able to conduct analyses at the aggregate level 

to help teachers and administrators monitor student outcomes, identify trends in cohorts, gauge the effects of 

programs, and determine how to boost learning and improve performance. School principals and administrators 

were able to generate and publish reports online to share with specific audiences; for example, reports on 

grade­level assessments might be shared and used to collaborate with teachers. In the next phase, teachers will 

use electronic grade books and report cards; and educators and students will gain access to goal planning and 

progress monitoring tools towards grades, graduation, higher education objectives, etc. 

Training


Professional development efforts began before the system was in place. But rather than introduce the technology 

as the main issue of training, the district decided to address the broader issue of data analysis and introduced 

the technology as a part of the bigger picture—the tool rather than the focal point. At the high school level, 

training focused on administrators and teachers; for K–8, data and curriculum coaches were trained to work with 

teachers only. When the system is fully operational, staff will have time built into their schedules for professional 

development so they may learn to use the software and conduct their own data explorations and analyses. 

12 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
The
future


The district is very excited about the YARDs initiative. While implementation has suffered some setbacks, mostly 

related to difficulties defining a districtwide vision for data uses, the district has been encouraged by its success 

so far and continues to actively pursue its goals for the new system. Leaders envision extensive use of data with 

the help of their new tools. As new modules are introduced, they hope to slowly incorporate the technology more 

and more into the school culture. Their ultimate goal is a significant transformation—a new environment where 

students, parents, teachers, and administrators can easily and intuitively access data, instructional materials, and 

assessments; communicate with ease; and collaborate using data to improve education. 

In addition, district leaders hope to expand data sharing and use well beyond school walls. The ultimate goal is 

for YARDs to become a citywide resource that connects a host of city­run organizations. This would enable broad 

collaboration towards improved city programs and services, and better ensure the academic success and overall 

well­being of all of Cambridge’s students. 

Book Four of Four:Advanced LDS Usage 13 

 

 

 

 

 

Prerequisites for effective 

Data use 

Chapter 2

 F or an LDS to reach its potential, certain conditions of high quality data use 

must be met. These prerequisites fall into four basic categories: data, access, 

understanding, and support. Specifically, to exploit the information effectively, users must 

have easy access to, and an understanding of, high quality data; as well as strong leadership 

and organizational support. The more effectively agencies achieve these ideals, the more 

successfully the system will be utilized. Without effective realization of these conditions, 

the system is likely to fail in various predictable ways. 

To exploit the information effectively, users must have 

easy access to, and an understanding of, high quality 

data; as well as strong leadership and organizational 

support. 

To fully benefit from the LDS, stakeholders should have 

 high quality data that meet a broad range of user needs and are accurate, 

comparable and trustworthy; 

 easy access to key information (e.g., performance and evaluation indicators) 

through a web portal, reports, and analytical and querying tools; 

 an understanding of what data are available to them, how to use the tools to 

access and analyze the information they need, and how to make sense of the facts 

presented or interpret the results of their own analyses; and 

 support from strong leadership, colleagues, and peers who value, model, and 

facilitate effective data use. 

Figure 2 illustrates these requirements. 

Book Four of Four:Advanced LDS Usage 15 
Figure 2. Data use framework 

Prerequisites for effective LDS use 

QUALITY 

DATA 

 Stakeholder engagement 

 End user’s needed data 

 Strong data governance 

 Adherence to common data standards 

 Linkages within and beyond K–12 

ACCESS 

 Data warehouse (or linked data repositories) 

 Web portal 

 Reports (standardized & customizable) 

 Analytical and querying tools 

UNDERSTANDING

 Awareness of available data 

 Training (on use of technology) 

 Professional development (on data use 

and intepretation) 

SUPPORT 

 Leadership support 

 Organizational suport (e.g., small groups, 

support staff) 

 Time reserved for analysis 

Quality Data 

In Book Two: Planning and Developing an LDS, the importance of defining and meeting 

stakeholder information and analytical needs was discussed. And in Book Three: Effectively 

Managing LDS Data, data quality was addressed in terms of ensuring accuracy, timeliness, 

validity, and utility. This foundation of high-value, high-quality data provides the raw 

materials for effective LDS use. 

16 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

 

 

 

 

 Stakeholder engagement to envision ideal and gain buy-in 

System success begins with stakeholder interest, established through early 

and continual involvement. End users should be involved in the design and 

refinement stages, including the selection of data items that will be collected and 

made available through the system. 

 LDS populated with valuable, needed data 

The system should be designed to include all data stakeholders need to answer 

their questions, including those needed for reporting requirements. This detailed 

information, including historical data, must be linked over time and cover areas 

such as student, staff, institutional, curricular, achievement and assessment 

(formative and summative), programmatic, and financial information. The 

system should be designed to answer known information needs, yet be flexible 

enough to accommodate newly identified requirements and new data items as 

needed. 

 Quality data ensured through a strong data governance 

process 

Decisions based on bad data are likely to be deficient. Therefore, a strong 

governance process and staff training program should be implemented to ensure 

data quality and utility. 

 Adherence to common standards 

Adherence to common data standards facilitates data quality, interoperability, 

and comparability across systems. Agencies should follow widely accepted data 

standards including definitions, code sets, technical specifications, and other 

guidelines. 

 Integration of K–12 agency data and linkages to outside 

data sets 

Ideally, K–12 data systems should be linked or integrated with data from early 

childhood education, postsecondary, the workforce, neighboring states, and 

other agencies as determined by the state or district. This broader integration 

allows a more complete picture of students’ experiences throughout their 

education and professional lives. 

Access to the Information 

Beyond the crucial foundation of quality data, users must have the tools to access the 

information they need and are authorized to see. Without easy access, the information 

may require tedious work to find and decipher, making data use less likely. 

 Data warehouse (or linked repositories) 

Agency data should be consolidated in a central data warehouse (or linked across 

multiple source files) to allow users to easily yet securely access and analyze key 

information from different areas of the education system. Data from separate 

systems should be transferred frequently to ensure they are current. 

Book Four of Four:Advanced LDS Usage 17 
 

 

 

 

 

 

 Web portal 

Data should be available via a user-friendly web portal that provides secure 

access to the agency’s reporting and querying tool. This single point of access can 

provide easy access to longitudinal reports and analytical tools from a wide range 

of sources. 

 Useful and timely reports 

Agencies should create pre-packaged, high-quality reports to answer frequently 

asked questions or provide key information. These reports should clearly and 

concisely communicate important information and give users context to ensure 

accurate and appropriate interpretation. They should also be timely to allow 

stakeholders to take action in the present rather than simply reflect on the past. 

 Analytical and querying tools 

Easy-to-use reporting and analysis tools built “on top of” the data repository(ies) 

will help stakeholders quickly access and analyze key data. These tools may 

include statistical and visual analysis tools that help identify trends and 

relationships among variables; and may be consolidated in, or accessible from, 

user dashboards to help educators and other users explore key information in real 

time. 

Capacity to Use and Understand the Data 

If users do not understand what data are available and how to appropriately analyze and 

interpret them, the information will not be used or, worse, may be misused. Therefore 

users must be trained in data analysis and its predicated technology, and must understand 

the value and limitations of the data at their disposal. 

 Effective dissemination 

Access, reports, and tools are not enough. Potential users must know the 

information is available and understand how to use it effectively. Agencies 

should thus communicate what data assets are, or will be, available. 

 Training and professional development 

Stakeholders should be trained to effectively use the available tools to access, 

query, and analyze the information. This professional development, both preand in-service, should also teach how to appropriately interpret and use the data. 

 Staffing capacity 

Agencies must be able to understand and analyze their data. In-house analysts 

who understand the data and can use them to answer questions and build ad-hoc 

reports can be very beneficial. Alternatively, or additionally, agencies may look 

to outside researchers (e.g. from universities) for analytical support. 

18 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

 

 

Support from Leadership and Peers 

All the investment in training and technological tools will be wasted if staff are not given 

the time to use the available resources and apply what they have learned to improve 

educational processes and student outcomes. 

 Leadership support for data use 

Leadership support and a strong culture of data use to improve learning are 

critical to effective data use. Educators need help to successfully decipher the 

data and apply the insight they gain to instructional planning and practices. 

This help often comes from principals, but may also come from others such as 

department lead teachers, in-house data coaches or hired consultants. 

(Office of Planning, Evaluation, and Policy Development 2009) 

 Organizational support for data use 

Educators should collaborate to learn from their colleagues, consider their 

experiences, share best practices, reflect on the data, and formulate strategies. 

This requires flexibility in the pace of the curriculum and openness to teacher 

innovation. If teachers are to use data to help their students, they must be 

able to adjust their strategies when necessary. If the curricular schedule is 

inflexible, teachers will be unable to respond to the data effectively. Additionally, 

standardized frameworks, procedures, and tools to guide data use can help 

educators leap from raw assessment and other data to instructional changes. Tools 

for this purpose may include data system links to instructional materials and 

model lesson plans, as well as to instructional frameworks and curriculum guides. 

(Rennie Center for Education Research and Policy 2006) 

 Time 

If educators have access to masses of high-quality data and state of the art 

technology, but are not afforded time to review reports or interpret key data, the 

information will probably be ignored. Education leaders at every level should 

build time for data use into administrators,’ teachers,’ and other staff’s regular 

schedules. This may require some creative scheduling and perhaps some extra 

staffing, but it is essential. 

Quality,Access, Understanding, and Support 

Once these four prerequisites of data use are met, the stakeholders must then take the 

final step and use the tools and the new data to inform their work. Identify what works 

and what does not, adapt to meet the challenges the data have revealed, and increase 

operational efficiency. 

Book Four of Four:Advanced LDS Usage 19 
20 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

A Case Study: 

Soaring to Early in the Oklahoma morning, the superintendent of Western 

Western Heights Heights (WH) Public School District sits down at the computer 

and logs onto his personalized dashboard. There, he peruses a host 

of data on the district’s schools. Noticing a new dropout at the high 

school, he drills down to look at his record, checking the former 

student’s expected graduation date and test scores; and exploring 

demographic, economic, and other data to try to discover what may 

have contributed to the loss. Meanwhile, teachers and staff take 

roll and enter attendance records into the local data system, automatic notifications are sent out, and attendance 

secretaries at each school contact the parents of absentees. Later that day, several teachers meet to discuss results 

of a recent formative assessment and to devise a joint plan to meet their students’ needs. They view fresh data 

on their students’ performance, seeing which of them has the most success with which areas of the curriculum, 

sharing effective strategies, and using the district’s Professional Learning Community (PLC) website and curriculum 

management system to find helpful instructional materials. While on the PLC website, which links to the district’s 

many other teacher groups, they post a question to their colleagues. That evening, a state university statistician who 

is also the district’s school improvement director visits the site and reads the question. He begins to gather data 

from the LDS to try to find the answer.

Under strong leadership from a superintendent who placed high value on data use long before the issue was 

prominent on the national or state education agenda, Western Heights has created a robust local LDS and paired 

it with extensive access, sophisticated analytical capabilities, and a strong culture of data use. The combination is 

a recipe for very effective, data-informed decisionmaking that has gained national attention. 

Western Heights dashboards

Administrator dashboard: Administrators have access to a host of school-level summary data, including 

the number of suspended students, meal status, recent enrollment and transfer counts, and dropouts; 

a trend-based analysis tool that shows performance on the state annual summative assessments; and 

an NCLB validation requirement report, that shows the percentages of students tested in each school by 

subgroup and by teacher.

Teacher dashboard: Teachers are given alerts when their students are falling behind or having trouble 

with particular areas of the curriculum (e.g., which standards they are struggling to master), class 

summary reports on test results by academic standards, reports that show student test scores and 

attendance data, trend data on students’ performance by academic standards, and more.

Parent dashboard: Parents can easily access data on their children, including attendance, schedule, 

meal account, report cards, and standards-based reports on student performance.

System: All the pieces that help make the district a model data user

In building its district-level LDS, Western Heights decided not to rely on any single vendor. Instead, the district 

pieced together a collection of interoperable products—all Schools Interoperability Framework-compliant 

(http://www.sifinfo.org/us/index.asp), “best-of-breed” technologies. The district’s data infrastructure is made up 

of a dozen or so separate but interoperable systems, including a student information system, a student ID system, 

a data warehouse, food services and transportation systems, and others. Each system has clear authority over 

certain data items and automatic updating that ensures the data are current and consistent districtwide. 
Book Four of Four: Advanced LDS Usage 21

The pros and cons of perceptions data

Western Heights collects perceptional data—survey data measuring perceptions or “feelings” about the 

learning environment—from teachers, students, and parents to inform and evaluate policy decisions. 

While helpful in many ways, these data can be problematic.

In the first place, they are difficult to collect. Due to limited computer access in the community, 

electronic surveys were impractical. The district therefore used paper surveys during enrollment and 

parent–teacher meetings, which led to data-entry problems. Frequent surveying lowered response rates, 

so the district decided to conduct surveys only when they had a clearly defined purpose. For example, a 

survey was conducted to gauge opinions about the restructuring of academic scheduling. Surveys were 

also used in some cases to try to understand why student performance had dropped.

In addition, the district found the data limited in terms of application. Overall, staff found that while 

perceptions data were useful for informing decisions in the present, they were not well-suited for 

longitudinal applications. Subjective changes in opinions or perceptions, the district realized, do not 

represent change in the way that objective data such as assessment scores do and, thus, do not track 

well over time.

Beyond this foundation, the district’s system and approach include

 Formative assessments: A number of formative assessments are used to gauge student performance 

throughout the school year.

 Standards mapping: Tools map a host of state assessments to a common set of academic standards.

 Dashboards: Dashboards provide easy access to detailed historical student data, including educational 

inputs (e.g., teachers, schools attended, program participation, etc.) and outcomes (multiple measures 

including assessments mapped to standards), and an array of analysis tools. Administrators, teachers, 

and parents have dashboards with tailored access and functionalities.

 Cohort-tracking system: Recent and historical data use identifiers to follow students and cohorts, and 

to establish appropriate graduation expectations. Three levels of cohorts are followed: local, state, and 

federal. This gives the district a fairer way to assess value added and accurately assign responsibility 

for successes or failures. For instance, if a student comes to the district in 10th grade with an expected 

graduation year of 2011, but actually graduates in 2010, the district can rightfully take credit for that 

expedited success. However, if the student was held back in an earlier grade while in another state or 

district, the federal and/or state cohort might be 2011 but the student can be placed with the local 2012 

cohort. If the district does its job, the student will graduate in 2012 and the district will not be blamed for 

falling short of the state- or federally expected graduation year.

 Family information tracking: The district assigns family IDs during enrollment rather than assigning 

a different ID for each child. These are used for various purposes, such as allowing parents to access 

information on all their children districtwide, with a single user ID and password. Family IDs can also help 

ensure all students eligible for free and reduced-price lunch receive program benefits.

 Growth model: A sophisticated and publicly accessible growth model is being developed to measure and 

display growth and value added. The model relies on the district’s common classification of academic 

standards.
 Automatic
reporting: The system allows the district to produce automated state and federal reports in 

minutes. 

 Professional
learning
community
(PLC)
network: A districtwide community of teacher PLCs collaborates 

to improve instruction and student achievement, supported by a website that facilitates communication 

across the district. 

 Research
support: A university researcher works with the district, providing data analysis and 

instructional improvement support. 

 Merit
pay: A system for creating merit pay is being developed, based on a host of teacher and student 

data. This evolving project has been widely supported by stakeholders, including teachers and the 

teachers union, who view it as a fair approach. 

What
is
the
district
doing
with
its
LDS?


Digging
deep
with
outside
assistance: To better understand the roots of its challenges and improve its students’ 

outcomes, the district brought in an outside statistical consultant to analyze its data. Administrators wanted to 

build predictive models that could use scores from two annual norm­referenced tests to forecast how students 

would perform on the state’s high­stakes, year­end test. The consultant used the district’s norm­referenced 

and state criterion test data from a previous school year to create models for each grade level and subject. After 

demonstrating the model’s strong predictive value, he applied the formulas to the current school year and created 

reports that showed teachers where their students were struggling. This allowed educators to focus on problem 

areas and better prepare students for the year­end assessment. Similar models are now being developed with 

more frequent benchmark achievement data; this will help the district predict end­of­year assessment performance 

as well as show academic growth. Additionally, these models will help assess the benchmark tests themselves to 

determine if they are rigorous enough, and if instructional practices are adequately preparing students. 

Beyond the predictive models, the consultant worked closely with the district to observe how staff used the data 

and software applications. He then analyzed and presented findings, worked with teacher groups, and even held 

one­on­one training sessions to help educators determine what to do with the available data. This new insight 

encouraged teachers to work more openly and collaborate more effectively to meet student needs. 

Teacher
collaboration: Western Heights teachers work together in professional learning communities (PLCs), 

teaming up to analyze data and devise strategic improvement plans. Some teachers are trained as “instructional 

leads,” learning to work with teachers and data to determine which strategies are working and which are not; and 

in which content areas students are struggling, both individually and as a group. They also use the data to identify 

which teachers have the most success with different students and areas of the curriculum. Students are then 

placed with teachers who possess particular strengths suited to their needs (see discussion below). Teachers also 

take advantage of the district’s curriculum management system to find lesson plans and resources, and to send 

students materials to help them as necessary. 

Collaboration does not end with individual PLCs. Teachers work together not only within subjects and grades, 

but also across grades to make sure students are ready for the following year. Teacher groups are linked by a 

central PLC website that allows PLC members across the district to communicate across grade levels; and the 

entire school district to ask questions, provide answers, and share instructional resources. The district’s statistical 

consultant also actively engages with the staff through this communication tool. 

22 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Four of Four: Advanced LDS Usage 23

An example of data analysis to inform policymaking

Leaders of Oklahoma’s Western Heights School District were determined to identify the factors related 

to poor student performance in their schools. They hired a research consultant to collect a range of 

student data and conduct a series of analyses examining the relationships between test performance 

and variables such as socioeconomic status (SES) and attendance. The consultant found that, contrary 

to expectations, SES had no strong relationship with student performance. Attendance, however, was 

strongly correlated with test scores. Based on these findings, the district published a report suggesting 

that students with less than a 90 percent attendance rate were more likely to do poorly on the state’s 

year-end test. Though no causal relationship was proven, the findings prompted the district to take action. 

Policies were implemented to increase the number of students who showed up to school. For example, 

an attendance secretary was added to each school and an attendance coordinator to each professional 

learning community (PLC). Parents of absentees received automatically generated notifications and staff 

worked to maximize the number of students who showed up to school. And the district’s superintendent 

even asked that the district start enforcing its school attendance laws more aggressively. 

Further analysis unearthed a more fundamental issue: attendance problems were highly correlated 

with mobility. In this district with a highly mobile student body—a 70 percent mobility rate—the most 

transient students, regardless of SES, were most likely to have the lowest attendance rates and the 

lowest assessment scores. Faced with this reality, the district is attempting to identify other factors that 

may contribute to the relationship between mobility and student success.

Matching teacher strengths to student needs: By identifying detailed information about student needs (the areas 

of the curriculum in which they are struggling as identified by online, standards-based testing) and teacher 

strengths (the areas of the curriculum in which they have produced the greatest student performance gains), 

the district moves students to different teachers throughout the school year to maximize learning. This is done 

in various ways to provide the best instruction available as evidenced by the data. For example, if a teacher has 

notable strength teaching a particular concept, others may bring their students to that teacher to learn that portion 

of the curriculum while they themselves focus on other content areas in which they have the most success. In 

another example, students having trouble with a particular skill, perhaps spelling or long division, might be placed 

with a teacher best equipped to teach this skill. Teacher–student pairing is also used in certain interventions such 

as after-school tutoring. This strategy has been very effective in helping students gain the skills they need to stay 

on track to success.

The future 

It took Western Heights years to establish its sophisticated system, and to change its culture to not only use but 

rely on data for decisionmaking. Today, the district continues to expand its data use capabilities; and to delve 

further into its data to better understand its challenges and find effective solutions.

What Do Stakeholders get? 

lDS Data uses and users 

Chapter 3 

T he benefits agency stakeholders will reap from the LDS depend on the effort

 invested in planning, designing, developing, populating, and managing 

a system that will help improve student outcomes. In the end, education agencies 

have to deliver on their promises and deliver an LDS that has practical value for users. 

Ideally, access to high-value data—with appropriate privacy protection—will be extended 

to state and federal program administrators, education researchers, district and school 

leaders, teachers, parents, students, the general public, and organizations outside of K–12 

education. 

Already, some administrators are using the data to more accurately and efficiently 

administer or comply with federal and state programs and requirements. Some are casually 

using single points of data to check the status of schools or students by viewing end-ofyear summative assessments, periodic formative assessments, schedules, attendance and 

discipline records, etc. Some are producing reports or using real-time dashboards to get 

frequent updates on key information and trends; and turning the information into changes 

in policy, curriculum, training, and classroom instruction to improve education outcomes. 

And some are conducting sophisticated analyses by combining detailed data integrated 

over time and across institutions, matching students to teachers and programs, measuring 

student growth, and gauging the value of various educational inputs. 

Don’t leave data use to IT 

Figuring out how to use the data should not be IT staff’s responsibility. 

Ask business staff and researchers. 

This chapter provides an overview of how some stakeholder groups might use an 

LDS equipped with key data and helpful tools. Building on earlier sections of this guide 

series, it reviews many of the benefits an LDS can provide. 

Book Four of Four:Advanced LDS Usage 25 
District
and
School
Leaders


Superintendents
and
other
district
administrators,
principals,
directors
and
other
school


staff,
school
board
members,
and
other
local­level
leaders
are
often
more
avid
data
users


than
other
stakeholders.
This
group
can
benefit
greatly
from
access
to
real­time,
detailed,


longitudinal
data.
These
users
may,
for
instance,
rely
heavily
on
both
aggregate
and


individual
student­
and
staff­level
data
to
guide
decisionmaking
about
financial
and
human


resource
allocation,
hiring,
professional
development,
staff
promotions
and
compensation,


facilities
maintenance,
disciplinary
policies,
student
remediation,
placement,
promotion,


program
implementation,
product
purchases,
and
much
more.
These
local
leaders
may
use


portals,
dashboards,
or
other
business
intelligence
tools
to
view
key
indicators
and
trends;


evaluate
schools,
principals,
teachers;
and
assess
the
impact
of
programs
and
policies
on


long­term
success
and
student
learning
between
marking
periods
or
from
year
to
year.


Depending
on
the
flexibility
of
the
tools
available
to
them,
administrators
may
be
able
to


highlight
information
for
themselves
and
their
staff
by
producing
their
own
reports,
charts,


graphs,
lists
and
more.
Using
techniques
like
the
value­added
assessment
models
discussed


later
in
this
chapter,
administrators
can
more
readily
identify
the
most
effective
teachers;


match
teachers
with
the
training
they
need
to
hone
their
skills;
promote
the
most
effective


educators
to
lead
positions;
facilitate
the
sharing
of
best
practices
among
staff;
inform


compensation
decisions;
and
determine
which
certifications,
trainings,
and
other
programs


are
worth
the
investment.


Data
use:
Culture
change!


Data use, or lack of use, is a cultural issue. Education agencies and schools must shift from reporting and collecting data for compliance to facilitating 

data use for education improvement. Leaders should ask: 

 What value is put on data use? Are data used only for operational and 

compliance reasons, or are they also used to inform decisionmaking? 

 What is envisioned for data use? Is there a clear plan to achieve this 

vision? 

 How is that vision implemented by leadership in individual schools? 

 What emphasis is put on data quality? 

 Is support available to help staff use data? 

 Do educators have time allotted specifically for data use and 

reflection? 

 Are the tools for accessing and analyzing data user­friendly? 

 What is the depth of available analyses—progress monitoring, 

benchmarking, prescriptive, etc.? 

 How is the information the data reveal applied? 

 How frequently are data collected about student learning, the 

educational environment, stakeholder perceptions, etc.? 

26 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
“(At) the moment, high school principals know virtually nothing 

about what becomes of their graduates. Most don’t even know 

whether their students make it to college at all. 

What data they have is anecdotal. ‘Once a graduate happened to drop 

by and tell us she was struggling with college writing,’ Linda Calvo, the 

principal of Arleta High School in Los Angeles, told us. ‘We changed our 

writing curriculum based on what she said. But her visit was a 

totally random occurrence.’ ” 

(Schramm
and
Zalesne
2009)


The
following
are
some
specific
questions
users
might
answer
with
LDS
data:


 How
well
are
a
school
or
district’s
former
students
faring
in
later
grades,


postsecondary
institutions,
or
the
workforce?
Based
on
the
data,
administrators


can
devise
evidence­based
plans
for
improving
student
outcomes.


 What
is
the
agency
doing
well
and
what
must
be
improved?
For
instance,
are


many
graduates
requiring
college­level
remediation
in
a
particular
subject?


 What
should
improvement
goals
be?
With
goals
set,
educators
can
use
the
data
to


monitor
progress
towards
those
targets.


 Which
schools
and
teachers
might
serve
as
models
for
those
having
less
success


preparing
students?


 Do
certain
teachers
have
more
success
with
students
of
similar
socioeconomic


status,
race/ethnicity,
sex,
prior
performance
level,
or
other
characteristic?


 How
well
do
local
curricula,
materials,
and
assessments
align
with
state
summative


tests?
How
can
they
be
better
aligned
to
ensure
students
succeed?


 What
was
the
impact
of
any
new
textbooks
or
other
instructional
resources?


 How
are
certain
student
characteristics
affecting
the
school
or
district’s
overall


performance
on
assessments
(English
language
learners,
transfers,
etc.)?


 What
are
the
local
rates
and
trends
in
attendance
and
mobility?
How
do
these


factors
impact
student
performance?


 Who
is
dropping
out
of
the
school
or
district?
What
signals
should
prompt
staff


to
intervene
with
struggling
students
and
prevent
future
failures?


 Are
school
or
district
educators
equitably
distributed
in
terms
of
qualifications?


Are
certain
geographic
areas,
subjects,
or
grades
disproportionately
serviced
by


the
least
effective
or
least
qualified
teachers?
Are
all
district
schools
adequately


staffed?


 What
skills
do
teachers
need
to
strengthen?
Based
on
past
outcomes,
which
types


of
professional
development
produce
the
greatest
results?


 What
is
the
value
added
by
individual
teachers,
teachers
with
certain
characteristics


(training,
certifications,
etc.),
grade­
or
subject­specific
instructional
strategies,
or


programs?
Do
these
effects
vary
by
student
characteristics
or
other
factors?


Book Four of Four:Advanced LDS Usage 27 
 

 

 

 

 Which programs are working and which are not? What weaknesses within 

otherwise successful programs might be improved? Does a specific program have 

a stronger impact on students with certain characteristics than on others? 

 What facilities maintenance is needed?* 

 How well do students in a specific school (or schools) for a number of years 

perform in comparison with children enrolled the same amount of time in the 

highest-performing comparable school(s)? (Dougherty 2002) 

 Are other comparable schools having greater success in particular subjects or 

content areas, or with students sharing certain characteristics? If so, what can be 

learned from them? 

Focusing the lens with better data 

Detailed, longitudinal data can provide many types of insights. Knowing that a school’s 3rd grade achievement gap has grown since the prior year is useful. But it is much 

more constructive to be able to account for changes in the school’s student population, 

and know whether the achievement gap in this year’s 3rd grade cohort of students actually 

closed relative to those same students’ scores during the prior year. 

Similarly, a teacher should know that the Latino students in her school lagged White 

students by 10 percentage points on this year’s state math assessment. But it would be 

even more useful for her to know whether a particular Latino student’s math scores were 

proficient before the student entered her class.And it would be even more valuable for 

her to know the student had difficulty in fractions and, specifically, that on the last interim 

assessment, the student repeatedly made the common mistake of adding the denominators 

together. Based on this information, that teacher can work with the student on this skill, or 

seek help from other teachers who have been the most success teaching this skill. 

Teachers 

Teachers could potentially reap significant benefits from access to detailed, longitudinal 

student data. These users on the front lines of the education system can use the data 

to monitor individual performance on assignments and assessments, as well as growth 

through the year; identify struggling students as quickly as possible; target extra help to 

students having difficulties; adjust lesson plans as needed; and set class-level improvement 

*Administrators can use LDSs to identify maintenance needs, compare the physical conditions and maintenance needs 

of buildings and utility systems across a district, and plan and take preventive action to ensure long-term facilities 

sustainability. 

28 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

 

 

 

 

 

 

 

 

goals. To ensure all students’ needs are met, instruction may be tailored for small groups 

or even individual students in a class. Teachers may access data in the form of reports 

produced by the state or district to provide key information on student- and classroomlevel performance. Or, they might use more high-tech solutions like analysis tools or 

personalized dashboards that present key, real-time information that can be drilled down 

to the student level. Teachers can also use the data to identify their own success and 

failures in the classroom, share best practices with colleagues, and seek better solutions 

based on the facts. Some specific questions and might be: 

 In what areas of the curriculum are students (the entire class, subgroups, or 

individuals) excelling or struggling? 

 Are students on target to meet proficiency or academic growth targets? Are any 

students showing signs that they may be heading in the wrong direction? At 

what point did these students start having trouble in a subject or area of the 

curriculum? 

 Are students with particular characteristics doing better than others? What might 

this say about teaching style or other factors? 

 Are other teachers having greater success teaching particular subjects or academic 

standards? If so, what can be learned from them? 

Parents and students 

Though the scope of an education agency’s LDS accessibility may not reach students and 

their parents, access to a student’s course schedules, attendance, disciplinary record, credits 

earned, summative and formative assessment scores, program enrollment, and other similar 

data can be very useful. Such access can serve many purposes, including setting goals, 

directing study, and preparing for meetings with teachers. These stakeholders can use the 

data to answer the following types of questions: 

 How is a student doing on assessments this year? Is the child on track to meeting 

the growth target or the proficiency standard? 

 In what areas of the curriculum is more study or extra help required? 

 What courses should the student take next year? 

 Based on grades and test scores, what colleges would be appropriate? 

 What grades need improvement to get into the first-choice college? 

 What programs might benefit the student? 

 How does the school compare to similar institutions in the district or state? 

Researchers 

Longitudinal data sets contain all the students across a school, district, or state, and many 

years of data for each, providing a vast source of detailed information. This should allow 

less dependence on aggregated data, surveys or studies that rely on samples of students 

that may or may not be representative of the larger population. Using more complete data 

will, in turn, allow researchers to derive results that are more reliable and representative 

of the broader education system. The availability of data should contribute greatly to our 

understanding of the education system (e.g., to evaluate the effects of programs, policies, 

curricula, teaching practices, as well as external factors and more). State education agencies 

may grant researchers access to data through various means, and in accordance with 

Book Four of Four:Advanced LDS Usage 29 
relevant state and federal privacy laws. These may differ, as states vary in their stance on 

providing personally identifiable data to researchers: some allow it under strict guidelines, 

while others require de-identification of all data before sharing with authorized researchers. 

Self-service 


research
 

While some states 

have trouble keeping 

up with data requests 

from researchers, others 

have solved this issue 

by creating a “selfservice” model. These 

states grant approved 

researchers access to 

a central data store 

containing pre-packaged 

data sets. 

Research partnerships, data-sharing agreements 

and memoranda of understanding 

When an education agency has collected potentially useful data, but lacks the inhouse staff necessary to analyze them, it may enter into an agreement with outside 

researchers to get its questions answered.The agreement may take the form of a research 

partnership, a data-sharing agreement, or a memorandum of understanding (MOU); the 

researchers may be independent research organizations, postsecondary research centers, 

the National Center for Analysis of Longitudinal Data for Education Research (http://www. 

caldercenter.org), etc.This can be a mutually beneficial arrangement. For instance, the 

Florida Department of Education provides authorized researchers with a list of subjects 

they want studied. In return for access to the data, the researchers conduct analyses to try 

to answer the provided questions and, at the same time, may also be allowed to explore 

their own questions, perhaps if they agree to share findings with the agency before they are 

published (Dougherty 2008). In addition to facilitating potentially enlightening research 

at minimal cost, education agencies may also benefit in other ways, such as having the 

data cleaned and converted into more useful formats by the researchers (National Research 

Council 2008). 

Some states require this type of agreement before access to data is granted.An MOU can 

be established between an education agency and a research organization or other party 

such as a postsecondary institution, another agency within the state, or a state education 

agency from a different state.This allows data sharing or access to protected data. MOUs 

generally articulate the agency’s interpretation of the Family Educational Rights and Privacy 

Act (FERPA) and other relevant privacy laws, establish ownership of the data, set the terms 

of data use, and establish a common set of goals for the data analyses. 

State-level decisionmakers 

Governors, chief state school officers, and legislators are some of the leaders at the state 

level who can use LDS data to learn about their state’s student population, including 

program eligibility, mobility, etc. They might also monitor student performance and 

outcomes, such as academic performance, dropout rate, National Governors Association 

(NGA) or four-year-cohort graduation rate,* etc.; apply more sophisticated methods of 

*The National Governors Association (NGA) graduation rate is a standard, four-year adjusted cohort graduation rate 

agreed upon by all 50 governors in 2005. 

30 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
accountability,
including
value­added
and
growth
models;
and
inform
decisionmaking


on
policies
and
programs,
resource
allocation,
and
initiatives
to
help
improve
their
state’s


education
system.
(DQC
2009)


Sample state data use websites 

School Improvement in Maryland: http://www.mdk12.org 

Wisconsin Information Network for Successful Schools (WINSS): http://www.dpi.state.wi.us/sig 

Indiana Accountability System for Academic Progress (ASAP): http://www.doe.state.in.us/asap 

Federal
government
and
state
education
agencies


A
successful
LDS
should
enable
more
efficient,
less
burdensome
data
reporting
by
schools


and
districts;
and
help
state
education
agencies
(SEA)
meet
federal
reporting
requirements


such
as
timely
and
accurate
submission
through
the
EDFacts
system.
State­level


decisionmakers
may
use
these
data
to
inform
decisionmaking
about
funding
and
other


issues,
including
the
improvement
of
state­funded
teacher
preparation
and
certification


programs,
dual
enrollment,
charter
school
policies,
and
more.
(DQC
2009
and
National


Research
Council
2009)


Planning
big,
starting
small


Although education agencies may have high expectations about revolutionizing data use, they likely will have to achieve their goals on a small budget.As such, 

they should identify the most critical issues for stakeholders and address these first. 

Achieving critical goals first helps garner support from skeptical stakeholders. 

Higher
education,
including
teacher
preparation
programs


Postsecondary
leaders
can
use
LDS
data
linked
to
K–12
to
prepare
for
their
incoming


students’
needs,
and
to
better
assess
applicants’
credentials
and
the
predictive
value
of


their
admissions
criteria
on
postsecondary
success.
They
can
also
use
the
information


to
ensure
the
necessary
staffing
resources
and
to
identify
the
most
effective
transition


strategies
(DQC
2009).
Furthermore,
if
teachers’
credentialing
programs
are
linked
to


student
data,
those
programs
can
better
assess
their
own
effectiveness
by
examining
the


educational
outcomes
of
their
graduates’
students.
Additionally,
these
institutions
and


programs
can
use
detailed
LDS
data
to
understand
the
geographic
distribution
of
critical


shortages
schools
face
in
hiring
teachers
of
mathematics,
science,
English
for
speakers
of


other
languages,
special
education,
etc.
They
can
then
use
this
insight
to
respond
more


strategically
to
this
problem,
advising
students
into
these
subjects
and
offering
field


experience
in
schools
facing
the
direst
shortages.


Book Four of Four:Advanced LDS Usage 31 
Industry, workforce agencies, and the workforce development 

system 

Industry can use longitudinal student data to learn more about tomorrow’s workers, 

perhaps considering a state’s educational outcomes when deciding where to establish 

businesses (EIMAC 2007). Workforce development programs can also assess their impact 

on graduates’ success in the labor market and identify areas that need improvement. 

Early childhood education 

Longitudinal data linked from K–12 to early childhood education (ECE) programs can 

show administrators how well they are preparing children for success in elementary school. 

In turn, the results can help them identify ways to improve their services so that students 

get the best start possible. (DQC 2009) 

Social service agencies 

Foster care, juvenile justice, health and human services, and other social service agencies 

that share data with education agencies can use the information to study how their 

services affect students’ educational outcomes (DQC 2009). For example, the connection 

of education and foster care data can help agencies better understand the needs of their 

clients; the relationship between their services and academic performance, such as the 

impact of student mobility across foster homes and schools on academic performance; and 

the ways they can improve their services to better meet children’s needs (Rennie Center for 

Education Research and Policy 2006). 

The public 

The public in general also stands to gain from more detailed and accurate data. Taxpayers 

can understand with greater precision where their money has gone, and how well students 

are performing as a result of the resource allocation decisions made by school and district 

officials. Homebuyers will also have better access to information on school quality in the 

community, a factor in property values and on the education their children receive. 

Balanced Assessment System 

Assessment results are essential to any LDS focused on improving the effectiveness of 

educational processes and supporting increased student learning. However, what constitutes 

high-quality, useful student assessment data is changing. For decades, educators have relied 

primarily on large-scale, summative assessments to judge student performance and to hold 

schools accountable. An increasingly large body of research suggests that, while these 

assessments estimate achievement for groups of students at a specific time, they cannot 

inform instructional planning for the individual student. 

More importantly, these studies suggest that, for a significant and positive impact 

on learning, a well-designed student assessment system should balance assessment types 

and purposes to include periodic summative assessment with frequent teacher-generated 

formative assessment, and ongoing self-assessment by students. This more balanced 

approach provides teachers with useful information for adjusting instruction to improve 

student learning, and helps students better prepare for future assessments. Furthermore, 

32 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
the integration of balanced assessment data with other kinds of LDS data can broaden the 

scope of questions that can be answered. For instance, stakeholders gain greater ability to 

assess the effect of instructional strategies and programs, return on investment, and the 

relationships between policy and learning. 

Formative assessments 

Formative assessments are relatively short tests that are given 

periodically throughout the school year “to monitor student progress 

during instruction.” 

Source: Rennie Center for Education Research and Policy (2006) 

Formative assessments are relatively short tests that are given frequently throughout the 

school year “to monitor student progress during instruction” (Rennie Center for Education 

Research and Policy 2006). These tests can be a powerful component of a balanced 

assessment system. While summative assessment data derived from annual tests may 

be sufficient for high-level decisionmakers, local-level educators and administrators will 

benefit from the more frequent formative assessment data to help monitor student learning 

throughout the school year, guide curricular and instructional planning, differentiate 

instruction for individuals and groups, and target interventions. 

However, education agencies must think carefully about their specific needs when 

they purchase or build a formative assessment system. For more information and tips on 

formative assessment solutions, see Data-Driven Teaching: Tools and Trends (Rennie Center 

for Education Research and Policy 2006). 

Measuring Progress in Different Ways: Growth 

Measures, Growth Models,Value Added 

Two common uses of longitudinal student- and staff-level data are growth models and 

value-added models. While both models focus on student academic growth, they approach 

this growth from different directions. Growth models consider past academic performance 

to look forward and set goals for future student growth; value-added models look back on 

progress that has been made in order to assess the effectiveness of particular educational 

inputs. Growth measures are the assessment tools used to feed these models with data 

about student growth, such as formative or interim assessments. Thus, growth measures 

feed value-added and growth models that calculate results in order to tell us about the past 

and help us plan for the future, respectively. 

Growth measures ≠ 

Growth models ≠ 

Value-added models 

Growth measures are frequent, short diagnostic assessment tools 

(e.g., formative and interim assessments) used to measure growth in 

academic performance. 

Book Four of Four:Advanced LDS Usage 33 
 

 

 

 

Growth models
 

Growth models are statistical models that measure academic growth 

over time to predict future performance and assign target goals that, 

if met, will bring students towards a proficiency standard over a 

period of time. 

Growth models measure academic growth over time. This measured rate is commonly used 

to establish performance benchmarks for points in the future (e.g., grade levels) against 

which to gauge student progress over time. 

This so called “growth to proficiency” method has been supported as a fairer 

approach to measuring performance and holding schools accountable than snapshot 

assessment scores and uniform proficiency thresholds that do not take into account prior 

performance and other important student characteristics (Barone 2009). Growth models 

are also respected as valuable tools to help teachers plan instruction, target students for 

intervention, and help them meet their performance goals. Proponents also have argued 

that this approach encourages educators to help all students improve, instead of focusing 

disproportionately on children on the cusp of proficiency at the expense of those far below 

or above the proficiency level in order to seek adequate yearly progress (AYP). Commonly 

cited limitations of these models include a lack of transparency about how results are 

calculated, and a narrow scope that may only focus on assessment results as measures of 

academic performance. 

Value-added models 

Value-added models are statistical models that use a range of data 

on student academic growth and other characteristics to estimate the 

impact of certain educational inputs. 

Value-added models analyze student assessments over time to estimate the relative effects 

of various educational inputs (schools, programs, curricula, teachers, etc.) on student 

learning (Wilson and Yeagley 2007). These models are commonly applied at the individual 

student or classroom levels. 

Value-added models may be used for a wide range of purposes, such as: 

 to identify the most effective teachers and help less successful ones improve their 

craft through collaboration, professional development, or other means; 

 to measure the effects of programs, curricula, or instructional strategies; 

 to evaluate the effects of principals and schools on student performance; 

 to group students and teachers based on teacher strengths and student needs or 

characteristics such as disabilities, English language learner, etc.; 

34 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 to assess the distribution of teaching talent and guide redistribution or 

recruitment efforts; 

 to inform teacher compensation and promotion decisions; 

 to identify the most successful schools and hold the least effective ones 

accountable; and 

 to identify the most and least effective teacher preparation programs, and ensure 

that all pre-service training programs are producing effective teachers. 

 Measuring the value added by teacher preparation programs 

A small number of states are using value-added models to evaluate teacher 

preparation programs. By measuring student growth through such a model, 

linking those student data to teachers, and tracking back to the institutions 

where the educators received their pre-service training, states and districts 

can identify the most effective teacher preparation programs. They can also 

work to improve and hold accountable the programs that produce the least 

effective teachers. 

A prerequisite to value-added assessment modeling is the linkage of individual 

student- and teacher-level data. This issue alone has been a considerable source of 

contention; however, the application of value-added data as a basis for accountability has 

added another layer to the debate. 

Critics have raised concerns about these models’ ability to prove causality (they 

cannot), and they question whether it is therefore appropriate to base teacher evaluations and 

compensation decisions on these data. Others point out that the models are limited in scope 

and do not present very broad views of student performance, putting too much emphasis on 

assessment results and, even then, often only assessments in core academic subjects. 

On the other hand, proponents argue that value-added models offer a fairer 

approach to evaluating teacher impact than other common indicators of student 

performance and proficiency levels. They favor value-added assessments because these 

consider a range of growth-measure data in gauging academic performance. These models 

can also factor in variables over which teachers and schools have no control, such as 

students’ socioeconomic status or prior achievement in other classes or schools. 

Not without their problems, value-added models nevertheless represent a promising 

application of longitudinal data. Already, many different models are being used by states 

and districts across the country, and practitioners are working to refine them in order to get 

the most accurate and reliable results. 

Book Four of Four:Advanced LDS Usage 35 
A
Case
Study:

The
Buzz
on A

head of the pack in many aspects of LDS development, the 

Arkansas Department of Education was struggling to make 

Arkansas’
Data
 its data useful to stakeholders.A researcher working with the 

Visualization
Tool
 state had pointed out that simply giving teachers and other users 

access to raw data was not enough; not enough to facilitate 

instructional improvement, and not enough for program evaluators 

to easily analyze the data for themselves. Convinced it needed a 

better approach,Arkansas decided that a visual application might 

be part of the solution. So, in addition to developing a dashboard system and scorecards, the agency worked with a 

vendor to develop a visual analysis tool to help users examine the state’s data. 

After about a year of development, the state rolled out “Hive” (dubbed after grid computing, not bees), which is now 

available online to the general public.An adjunct site gives users quick overviews of their data.The tool is loaded 

with state data, but can also be populated with local level, test, and demographic data by district administrators. 

System designers hoped to empower users with this sleek and simple web interface that allows stakeholders to 

explore test scores at the district, school, classroom, and individual levels.* Filter options for program participation, 

demographics, and socioeconomic status allow users to view additional details or specific records of interest. 

In addition to displaying assessment scale scores, Hive incorporates the Colorado Growth Model and allows users 

to place student growth percentiles on one axis and scale scores on the other.This adds another dimension, 

with scatter and bubble plots showing not only how a school, class, or student did on an assessment, but also 

improvement since prior tests.The tool can display scores or growth in two subject areas at once. Scatter plots 

show each of the individual units of analyses (e.g., schools or students) and users can roll their mouse over 

individual points. In the case of students, the individual’s sex, race, ethnicity, and socioeconomically disadvantaged 

status are displayed, along with names if the user is authorized to see them. Hive’s ability to aggregate students’ 

scores and growth by teacher has allowed authorized users to explore the patterns in school­ and classroom­level 

performance.This allows leaders to learn from successes. In some cases, schools have been identified in which 

every teacher’s students are performing and growing at exceptional levels.The state is using this information to 

examine the underlying factors that have contributed to exceptional student performance. 

Hive lets individuals work on their particular area of interest: a parent interested in comparing schools in their 

district, a teacher trying to discover patterns of performance for his students, a principal trying to determine if 

there are weak areas of instruction in her building, etc. However, while Hive provides individual users with many 

analytical and visualization capabilities, the tool’s website was also designed as a venue for collaboration as it 

allows the exchange of findings and ideas. Users can post and share graphs they created, comment on the work 

of others, and impart ideas in thread discussions.This allows users across the state to learn from colleagues and 

others with similar interests.As a result, knowledge about student achievement in the state as a whole is greatly 

increased. 

To prepare Hive users to explore and analyze the state’s data, a training course has been offered at regional 

service centers. School and district staff participate on a voluntary basis; while administrators are required to take 

*Initially,
no
classroom­level
aggregation
was
included;
this
was
added
when
teachers
showed
great
interest
in
being
able
to
view


their
impact
on
student
performance.
Careful
attention
has
been
paid
to
protecting
the
privacy
of
individual
students.
For
instance,


only
authorized
users
are
given
access
to
identifiable
information.
Pages
accessible
to
the
public
censor
student
names
and
suppress


sufficiently
unique
demographic
information
that
could
be
used
to
identify
individuals.


36 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
three hours of data analysis training, and teacher certification requires technology training that may be comprised 

of data analysis. Rather than taking an abstract approach, the training walks the users step by step through the 

process of requesting and displaying the data to answer specific questions. For example, to see how a school’s 

students compare to others in the state in a particular subject area, a trainee might be led through the process 

of finding and graphing scores and growth.To complement the training, Hive has links to helpful resources and 

training materials, including short videos to help users at all levels.A certified administrator from each district 

is also trained to upload local data and extend access to individually identifiable data to teachers and other 

authorized staff.The state uses surveys to gauge the usefulness of the training and the value of the tool in general. 

Arkansas’ visualization tool and training program are still young and expected to evolve quickly following user 

feedback. However, the initiative represents a promising, novel approach to education data display and analysis. 

For more information, visit: 

 The Hive website at http://hive.arkansas.gov 

 The Hive Quick Looks website at http://www.mrgh.com/ade 

 The Colorado Growth Model website at https://cdeapps.cde.state.co.us/growth_model_public 

Book Four of Four:Advanced LDS Usage 37 

Professional Development and 

Training for effective use of lDS Data 

Chapter 4 

S imply rolling out an LDS will not help an agency and its stakeholders

 realize its ultimate benefits. To maximize the return on the system’s 

investment, education agencies should systematically prepare users to take full advantage 

of the system, offering training to help them effectively and appropriately use the 

technological tools at their disposal. Users should also be offered professional development 

(PD) so they know what data are available; develop an appreciation for the value and 

utility of those data; and expand their skills in areas such as data location, inquiry, analysis, 

interpretation, application, and communication. 

District and school administrators, for instance, should be trained to use data for 

evaluation and decisionmaking. Teachers should understand how to use data to guide 

instructional improvement. Legislators and their aides should know how to interpret the 

data and use them to inform policymaking. The press should know how to interpret the 

data to accurately depict the education system. And parents and students should know 

how to track progress and set goals for academic improvement and success. 

Professional Development and Training Issues to Consider 

Professional development and training topics covered may include logging into the system, 

understanding report contents, and using analysis tools for various purposes. Federal and 

state rules, regulations, and policies on data use, privacy, and security may also be included; 

at the very least, informational materials should be made available. Such training will help 

the enterprise hit the ground running when the LDS and data become available. 

Need 

Assessing trainees’ needs is important. Before you begin a PD initiative, and iteratively 

along the way, determine what skills your target trainees currently have and what areas 

need to be developed. 

Book Four of Four:Advanced LDS Usage 39 
 

 

 

 

 

 

 

 

 

 

 

Current skills and practices 

 What type of data do staff and others already use and what do they do with them? 

 Do they use state assessment data? Local assessments? Other student data? 

 Do they link student data to teacher and other classroom input data? 

 How do they analyze and reflect on these data, and how do they use them to 

improve students’ educational outcomes? 

Current training programs for teachers 

 Do pre-service programs include data use training? 

 What data use training are aspiring teachers receiving at postsecondary institutions 

and through other teacher-preparation programs? 

 Does in-service PD on data use already exist? 

Focus 

Users should know what to expect. This can build excitement and support. Develop a 

focused plan for the topics that will be covered by training and PD. 

Data awareness 

 Will training build awareness of the available data, reports, and technology? What 

new types of data and indicators are, or will be, available through the LDS? 

	  What reports will be created? 

	  What ad-hoc querying and analysis tools will users have? 

Data use 

	  Will the program train users to access, create, and use reports? 

	  Will it teach how to access and utilize analytical tools? 

Data interpretation and application 

Beyond pulling data up on a computer screen or even conducting analyses, educators 

should be prepared to understand and act on the numbers. Once they have the facts, they 

must know how to adjust their practices to address problems and improve performance. 

 Will training teach users how to interpret the new data in the reports or the results 

of their own analyses? 

 How will educators and administrators turn raw data, tables, or graphs into 

information that can inform improvement planning or program evaluation? 

Data communication 

 How will you ensure users speak the same data language? If stakeholders are 

going to interpret data and use them to communicate, establishing a common 

understanding of data and the terms used to discuss them is important. For 

instance, if staff have different ideas about what “summative” and “formative” 

assessments are, a certain level of confusion will hinder discussions about the data. 

To get everyone on the same page and avoid confusion, reviewing key vocabulary 

terms or creating a glossary for reference is helpful. When discussing individual 

data elements, a metadata system or data dictionary can also be very helpful. 

40 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

 

 

The heart of “data-driven decisionmaking” is decisionmaking 

While proponents of data use in education have long emphasized the use of data in decisionmaking, a caveat should be kept in mind: people, not data, should ultimately 

drive decisions. In other words, data should be used as a tool to inform decisions, not 

dictate them.While data are invaluable to objective, evidence-based decisions, they can 

also lead users astray if not accompanied by critical thinking. 

Logic and good sense should inform all educational decisions, regardless of what the 

data say. Even if the research says a particular program is highly effective, for example, 

decisionmakers should not automatically implement it. Stepping back to take a look at the 

data or research results is important. Are the data valid for the requested purpose? Was the 

research conducted soundly and with accurate data? Can the results be generalized to the 

education environment? For instance, if the analysis was not conducted with local data, is 

the local student population similar to the sample studied? Can conditions be replicated? 

Does the agency have the financial and staffing resources to effectively implement the 

program? 

Target audience 

 Will PD and training be offered to teachers, administrators, administrative staff, 

technical support staff, researchers, legislative aides, students and parents, the 

public? 

 Will it be mandatory or voluntary for educators or others granted access to the 

data? Will data-use training be tied to educator and administrator certification or 

other form of recognition? 

Provider 

 Who will provide training and PD? For instance, will programs be taught by inhouse staff with expert knowledge of the system, consultants, researchers from a 

university, or vendors? 

 Will vendors be asked to provide training on the use of their products, both to 

in-house staff managing the system and to end-users? 

 From what level will PD be provided: from the state agency to districts, from 

regional agencies to districts or schools, from districts to schools, within schools 

by data coaches or other skilled staff, etc.? 

Book Four of Four:Advanced LDS Usage 41 
The Forum has more... 

... information about professional development and staff training: 

• Forum Guide to Decision Support Systems:A Resource for Educators (2006) 

http://nces.ed.gov/forum/pub_2006807.asp 

(See pages 17–18 of this Forum document.) 

• Forum Unified Education Technology Suite 

http://nces.ed.gov/forum/pub_tech_suite.asp 

Format 

	  In what format(s) will PD be provided? Will instructors lead face-to-face courses 

with lectures and activities, webinars, pre-recorded self-paced online video 

tutorials, online repositories of training materials, educator collaboration, etc.? 

	  How structured will the training be? Will users be taught to follow particular 

steps or procedures when using data, or will training be more abstract? One state 

education agency, for example, taught trainees a structured, step-by-step approach 

to using the data visualization tool. Many felt that this was more useful than a less 

tangible approach. In one basic, yet effective step-by-step data use methodology, 

users first formulate questions (“question posing”); then find, understand, and 

interpret data to help answer the questions (“data location comprehension and 

interpretation”); and, finally, develop an action plan to respond to the reality 

suggested by the data (“data use”). (Office of Planning, Evaluation, and Policy 

Development 2009) 

Scope 

	  On what data, reports, and analytical capabilities will the training focus? 

Professional development may focus on a specific type of data if time is limited, 

such as state assessments, but if possible educators should learn how to use local 

assessment and other data as well. Furthermore, workshops that only explain 

assessment data have been found to be less useful than those that teach how to 

translate data into actionable information for instructional planning (Marsh, Pane, 

and Hamilton 2006). Depending on the range of data, the number of reports and 

tools, and the resources available, an agency may have to decide on the range and 

depth of the training necessary. 

42 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

 

 

 

 

 

 

 

Timing 

 When will PD be offered relative to system rollout, or to the introduction of 

key system components? Agencies suggest that a balance is important. Offering 

training prior to the unveiling of a system or component will help build 

excitement and awareness. But, if it is offered too early and trainees have to wait 

a long time before implementation, this excitement could be lost, as could the 

knowledge and skills developed in the training. 

 How will your agency deal with turnover? 

 Beyond initial rollout, how often will PD be offered to users? To keep the staff 

up to speed, training should be ongoing with new-hire training and periodic 

refreshers for experienced staff. 

Sustainability 

 How will the PD program be sustained over time? Some education agencies have 

dedicated resources to provide ongoing PD as needed. A train-the-trainer approach 

can establish the capacity at the district and regional levels, allowing agencies to 

continue training over time and expand the training to reach educators and other 

data users. Posting materials, videos, and exercises online can also ensure access 

to training by a broad audience even when more formal PD cannot be provided. 

One state also packaged the materials on a compact disc that was distributed to 

districts, as many had difficulty viewing the material online due to insufficient 

bandwidth. These strategies can be particularly helpful in large states where 

trainees are far apart. Furthermore, as time passes, new staff come on board, and 

the system and its identified uses evolve. A need therefore exists to continually 

gauge user needs and identify areas for refresher courses; more in-depth training; 

or training on new data, tools, and approaches to data use. Help lines can also be 

an effective way of serving users on an ongoing, as-needed basis. 

Tracking 

Recordkeeping serves many purposes. For example, it provides data for audits and 

evaluations, and for fulfilling funding requests based on training and PD efforts. Tracking 

who receives training and PD—in effect, who is taught proper procedures for using the 

data—also offers some level of protection against unethical data use and helps agencies hold 

accountable any end users who might intentionally misuse or manipulate data. 

Logistically, this type of recordkeeping can be approached in several ways. For 

example, training can be required for the authorization of user accounts for analysis and 

querying tools. Registration in training and PD courses might also be tracked via individual 

identifiers maintained in the LDS or staff data system. 

 How will the training and PD offered and delivered be tracked? 


 When was training last offered? 


 What types of training have been most effective (see Efficacy below)? 


 Are more or less individuals being trained over time? 


Book Four of Four:Advanced LDS Usage 43 
 

 

 

“If it’s worth sending teachers to a workshop, then it’s worth having

teachers get together to figure out how to put what they’ve learned 

into practice. Otherwise, if you just leave everybody to their own 

devices, very little of it is going to be implemented.” 

(Jolly 2007) 

Efficacy 

 How will PD be measured? 

 What metrics will gauge its success? Counting the number of staff trained is 

not enough. Conducting a survey after the training can help measure trainees’ 

impressions of the program, how their understanding and interpretation of data 

improved, for what purposes they use the data, or how their practices changed 

as a result of the training. Agencies may also gauge the quantitative effects of 

the training on student performance by comparing outcomes before and after 

the training, or by comparing outcomes of students taught by trained and 

untrained teachers. 

Long-term 

 How will continued learning and application of the PD lessons be ensured? 

Onsite data analysts or coaches may help drive local efforts to use the data 

effectively. These coaches may be specially trained in-house staff or external 

consultants. A professional learning community (PLC), or collaborative teacher 

group, can also provide a venue for teachers to practice and hone their skills on 

a continuing basis (see Professional learning communities box below). Some 

agencies have also developed local-level user groups that communicate with 

the state and share knowledge and best practices among districts (DQC 2006). 

Whatever the method, schools should allow time for teachers and administrators 

to review, analyze, and determine how to act on the data. If a culture of effective 

data use is to take root, this time must be built into the schedule rather than being 

an additional burden. 

Professional learning communities 

The term “professional learning community” (PLC) has been applied to 

a range of educator and administrator groups. In this document, a PLC is 

a small, collegial group of educators who work collaboratively to share 

knowledge and learn from one another, develop and improve instructional 

strategies, and increase student learning and performance. 

PLCs are usually established at the school level, within grades and subject 

areas. They may be teacher-driven, but should have principal buy-in to 

ensure members have meeting time and other needed support. Social 

networking tools have allowed these groups to easily communicate with one 

another across schools and districts (see A Case Study: Soaring to Western Heights). These 

groups can provide an effective venue for improving data use by helping educators analyze, 

interpret, and mobilize based on facts about student and teacher needs. 

44 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Book Four of Four: Advanced LDS Usage 45

A Case Study: 

The Oregon Trail 

to Data Use

T

he Oregon Department of Education, with several partner 

organizations, embarked on a statewide professional 

development (PD) program called the Direct Access To 

Achievement (DATA) Project. To meet the needs of educators 

in the field, the first year was spent assessing needs through 

focus groups around the state and written surveys of principals, 

teachers, superintendents, IT staff, and other stakeholders. Project 

leaders sought to answer the core question: “What do we need to 

improve the use of data to improve student achievement in Oregon?” Not only did they receive some resounding 

responses, the project very quickly gained grassroots interest and support. Based on the findings, a curriculum 

was developed. After a pilot round of training was complete, the “data use for instructional improvement” arm 

of the project was adjusted based on trainee feedback. It was then rolled out statewide in 2008, when trainers 

scheduled and ran seminars across the state for school and district staff. 

Data use for instructional improvement: Training framework

Instructional training for the Oregon DATA Project is currently composed of four training strands.

Strand 1 Creating a Data Culture: One of Inquiry

This two-day course for lead and master teachers, superintendents, directors, and 

principals is meant to plant the seed for a data culture—that is, one that recognizes data as an integral 

part of teaching, learning, and school management. In this seminar, school leaders examine the story 

behind the numbers and learn to proactively respond to their new insights.

Strand 2 Using Data to Improve Learning in Districts and Schools

This interactive two-day seminar for school leaders such as directors, principals, and lead 

teachers offers in-depth analysis activities and strategies. Participants examine data to make leadership 

and instructional choices within the framework of a data-driven decisionmaking process. Participants 

examine their own, real data on their own, real students so that the process is experienced and 

understood in context. By the end of the seminar, participants have learned the methodology and 

made concrete improvement decisions about their district or school.

Strand 3 Using Data to Improve Learning in the Classroom

Focus in this two-day seminar is narrowed to the classroom level to help teachers and 

administrators use data to improve individual student learning. Training offers a chance to evaluate 

common assessments used in Oregon, as well as create an assessment framework and assessments 

aligned to instructional goals, including content, skills, and cognitive demand. Sessions include in-depth 

training on using formative and summative data to improve both classroom- and student-level outcomes; 

practical instruction on how to organize, analyze, and tie data back to improved instruction; and tools for 

progress monitoring.
46 Traveling Through Time: The Forum Guide to Longitudinal Data Systems

Strand 4 Essential Skills in Reading: Impact on Teaching and Learning

This three-part strand was designed to help district teams create a K–12 system to address 

the skills deemed critical for students’ future success. Teams are taught to use benchmark tests, progress

monitoring, and scoring guides. Training offers instruction on creating an assessment and instruction 

system based on the Common Core State Standards (CCSS) (http://www.corestandards.org) and 

research-based practices. Participants also learn how to determine student progress toward meeting the 

benchmarks; discuss implementation of the CCSS; adjust and target student instruction; and create a 

vehicle for meaningful reflection and team evaluation.

When designing a statewide PD program on data use for instructional improvement, planners took into account 

the considerable variation in technologies used for data storage and analysis across the state. Six discrete data 

warehouses in the state can transfer data to a central state data warehouse for reporting, research, or sharing. 

These separate systems have administrator dashboards, though only some equip teachers. To deal with these 

differences, Oregon’s training was designed to provide a higher level discussion of data use, laying out guiding 

principles to help staff analyze information regardless of the technology available. For example, participants 

gain an understanding of key data and data use terminology to establish a common language, including abstract 

concepts. Trainees are taught to locate, analyze, and tie data back to instruction and inform improvement plans; 

and to assess the alignment of district and school improvement plans, curricula, and programs. They also learn to 

evaluate programs based on merit—how well the programs are achieving their intended, measurable goals. 

While the main phase of Oregon’s training has ended, sustainability has been, and continues to be, a central 

goal. Several approaches have been taken to maintain the training effort:

  All training materials and full video segments from the seminars have been posted online and distributed 

widely as DVDs. 

  DATA certified over 100 trainers around the state. 

  Leaders partnered with regional Education Service Districts (ESDs) to carry on training at the district and 

regional levels. 

  Several districts decided to build the capacity to hold training sessions locally. 

  The project staff teamed up with postsecondary partners to develop a more in-depth curriculum that will 

eventually be offered at several state universities as part of pre-service training. 

  To create further incentive, administrators and teachers can earn credit toward licensure by attending 

training.

To gauge the project’s effectiveness, the impact on student performance has been measured; early data suggest 

that students of pilot district trainees are performing significantly better since training took place. In addition, 

teacher data-use skills have been assessed (trainees are asked to respond to a hypothetical scenario using data), 

and staff opinions and beliefs about data use have been collected; for example, are assessments an unnecessary 

burden or a valuable tool for improving instruction and student outcomes. 

Oregon’s data use project has gained national attention and several other states are currently considering 

using DATA as a model for their own PD initiatives. For more information, including training session videos and 

supporting materials, visit the project website at http://www.oregondataproject.org.
Conclusion 

Chapter 5

 When the vision for this guide series was crafted, states and districts around 

the country were already in various stages of LDS development. Some had 

had them in place for many years and were striving to expand their utility, others were just 

beginning to find their way. Since then, education agencies continued to make great strides 

thanks to committed state and district leaders, the federal government, and a number of 

nonprofit organizations and service providers. 

An Impetus for Innovation in Education Data 

In recent years, the U.S. Department of Education has elevated data to a new level of 

importance, tying LDS development and innovative data use to competitive funding 

opportunities via the Institute of Education Sciences (IES) Statewide Longitudinal Data 

System (SLDS) Grant Program and the Race to the Top. Indeed, the message from the 

the federal government on education data has been getting clearer and more resounding: 

In order to realize a more effective, efficient, and equitable education system, education 

stakeholders need detailed, accurate data to provide objective facts about what is 

happening in classrooms, schools, districts, and states. 

The thrust has been strong and the progress made towards establishing robust LDSs 

has been remarkable, exceeding the expectations of many. However, education agencies 

vary widely on many key LDS features and capabilities, including student and staff ID 

systems that link data about students and their teachers, the expansion of data access, the 

implementation of user-friendly analytical and querying tools, adherence to common data 

standards, and the integration and sharing of data across state lines and through the P–20 

pipeline. And despite tremendous advancement and innovation at both the state and local 

levels, much work still remains. 

Book Four of Four:Advanced LDS Usage 47 
An
Eye
Toward
the
Future


As
they
continue
to
develop
longitudinal
data
systems
and
break
down
the
barriers
that


once
prevented
information
sharing
and
access,
education
agencies
are
making
valuable,


detailed
facts
available
to
users.
How
to
most
effectively
and
fairly
use
those
data
is


still
being
debated
across
the
country,
and
stakeholders—from
state
and
local
leaders
to


researchers—are
exploring
different
approaches
to
leveraging
the
information.
While
some


of
these
uses
have
been
met
with
controversy,
the
possibilities
and
capacity
to
utilize
this


information
will
only
grow.
Great
insights
and
improvements
in
policy
and
practice
will


be
made
but,
at
the
same
time,
users
will
have
to
be
careful
in
their
interpretations
and


applications
of
the
information.
Numbers
can
explain
a
lot,
but
they
cannot
tell
the


whole
story.


No
doubt,
the
road
ahead
will
be
filled
with
obstacles
and
setbacks.
Data
quality,


detail,
linkages,
access,
privacy,
and
use
will
likely
remain
persistent
challenges
as
education


agencies
continue
to
develop
and
enhance
their
LDSs.
However,
the
remarkable
progress


made
over
the
past
several
years
is
extremely
encouraging.
As
more
progress
is
made,


these
data
will
provide
ever
greater
insight
into
the
experiences
of
students
as
they
journey


through
the
education
system
and
out
into
the
workforce—a
clear
view
of
reality
that
has


been
blurred
for
too
long.
This
is
the
kind
of
travel
through
time
that
LDSs
are
making


possible.
This
is
what
makes
all
the
effort
worthwhile.


48 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
 

appendix a. 

 

 

 

 

 

 

 

appendix a 

overview of the lDS 

guide Series 

The Traveling Through Time: The Forum Guide to Longitudinal Data Systems series consists of 

four guides intended to help state and local education agencies meet the many challenges 

involved in developing a robust longitudinal data system (LDS), populating them with 

quality data, and using this new information to improve the education system. 

Book One of Four: What is an LDS? focuses on the fundamental questions of what an 

LDS is (and what it is not), what steps should be taken to achieve a sound and successful 

system, what components make up an ideal system, and why such a system is of value 

to education. Book Two of Four: Planning and Developing an LDS, discusses the critical 

planning and development phases of an LDS project, from stakeholder engagement and 

needs assessment all the way through to system evaluation. Book Three of Four: Effectively 

Managing LDS Data explores several fundamental challenges of data management, 

focusing on data governance, data quality, privacy, and security. Finally, Book Four of 

Four: Advanced LDS Usage, addresses the effective utilization of LDS data, discussing the 

users and uses of the data; and emphasizes the need for effective training and professional 

development. The figure on the next page lays out the major issues covered in each of the 

four books in this Forum guide series. 

 Book One: What is an LDS? is available online at: 

http://nces.ed.gov/forum/pub_ 2010805.asp 

 Book Two: Planning and Developing an LDS is available online at: 

http://nces.ed.gov/forum/pub_ 2011804.asp 

 Book Three: Effectively Managing LDS Data is available online at: 

http://nces.ed.gov/forum/pub_ 2011805.asp 

To order a print copy of any of these guides, please contact: 


ED Pubs
 

U.S. Department of Education 

P.O. Box 22207
 

Alexandria, VA 22304
 

Or call toll free 1–877–4ED–PUBS or order online at http://www.edpubs.gov 

Book Four of Four:Advanced LDS Usage anced LDS Usage 49 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Book I:What Is an LDS? 

 Understanding what an LDS is (and is not) 

 Appreciating the organizational steps needed to 

institute and effectively use an LDS 

 Identifying the technical features and 

capabilities of an effective LDS and the 

additional features that can enhance the 

system’s utility 

 Recognizing the benefits of an LDS 

Book II: Planning and 

Developing an LDS 

 Engaging stakeholders 

 Describing the current system 

 Envisioning the desired system 

 Defining needs, including data and functionality 

 Gaining buy-in and funding 

 Building relationships 

 Writing an RFP 

 Building or buying a system or components 

 Transferring knowledge (e.g., from developers 

to staff) 

 Defining and measuring success 

 Refining the system 

Book III: Effectively 

Managing LDS Data 

 Defining governance structure 

 Defining roles and responsibilities 

 Collaborating to improve data quality and 

streamline operations 

 Managing changes to the system 

 Training staff to ensure data quality 

 Auditing/validating data at all levels 

 Establishing/following data standards 

 Securing data to protect privacy 

 Providing users access to key data 

Book IV:Advanced 

LDS Usage 

 Collecting, storing, and delivering key data 

 Developing useful reports to fulfill common 

data requests and needs 

 Developing user-friendly data tools to facilitate 

access and analysis 

 Training users to utilize the technology 

 Building awareness, understanding, and 

analytical capacity 

50 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
appendix B 

references 

Barone, C. (2009). Are We There Yet? What Policymakers Can Learn from Tennessee’s Growth 

Model. Washington, DC: Education Sector. 

Committee on National Statistics and the Center for Education (2008). Protecting Student 

Records and Facilitating Education Research: A Workshop Summary. Washington, DC: 

National Academies Press. 

Data Quality Campaign (DQC 2006). Creating Longitudinal Data Systems: Lessons Learned 

from Leading States. Washington, DC: Data Quality Campaign (DQC). 

Data Quality Campaign (DQC 2007). Linking Education and Social Services Data to Improve 

Child Welfare. Washington, DC: Data Quality Campaign (DQC). 

Data Quality Campaign (DQC 2009). The Next Step: Using Longitudinal Data Systems To 

Improve Student Success. Washington, DC: Data Quality Campaign (DQC). 

Dougherty, C. (2002). A Policymaker’s Guide to the Value of Longitudinal Student Data. 

Denver, Colorado: Education Commission of the States. 

Dougherty, C. (2008). Information Won’t Be Used If No One Can See It: Why States Should 

Publish Statistics Based on Longitudinal Student Data. Washington, DC: Data Quality 

Campaign (DQC). 

Education Information Management Advisory Consortium (2007). Linking Early 

Childhood Student Information Through the Workforce. EIMAC Brief, Issue 3, Dec. 2007. 

Washington, DC: Council Of Chief State School Officers. 

Jolly, A. (2007). Team-Oriented Teaching. Education Week. Retrieved from 

http://www.edweek.org/tsb/articles/2007/10/25/01jolly.h01.html on January 18, 2010. 

Marsh, J.; Pane, J.; and Hamilton, L. (2006). Making Sense of Data-Driven Decisionmaking in 

Education. Santa Monica, California: Rand Corporation. 

National Research Council (2008). Protecting Student Records and Facilitating Education 

Research: A Workshop Summary. Committee on National Statistics and Center for 

Education, Division of Behavioral and Social Sciences and Education. Washington, DC: 

The National Academies Press. 

Book Four of Four:Advanced LDS Usage anced LDS Usage 51 
Office
of
Planning,
Evaluation,
and
Policy
Development
(2009).
Implementing DataInformed Decision Making in Schools—Teacher Access, Supports, and Use.
Washington,
DC:
U.S.


Department
of
Education.


Rennie
Center
for
Education
Research
and
Policy
(2006).
Data-Driven Teaching: Tools and 

Trends.
Cambridge,
Massachusetts:
The
Rennie
Center.


Schramm,
J.
B.
and
Zalesne,
E.
K.
(2009).
High
School’s
Last
Test.
The New York Times, 

December
22,
p.
A41.


Wayman,
J.
(2005).
Guest
Editor’s
Introduction. Journal of Education for Students Placed at 

Risk, 10 (3):
235–239.


Yeagley,
R.
(2007).
Separating
Growth
from
Value
Added.
The School Administrator, 64(1):18.


52 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
appendix C 

additional resources
 

Chapter 1. Data Use: Where It Has Been, Where It Is Going 

Information Won’t Be Used If No One Can See It: Why States 

Should Publish Statistics Based on Longitudinal Student Data 

Data Quality Campaign (2008). 

http://www.dataqualitycampaign.org/files/publication-information_wont_be_used_if_ no_one_ 

sees_it-090108.pdf 

This article discusses the need get the right data to users, ensure the data are easily 

accessible, and make sure users know how to interpret the data. 

The New Stupid 

American Enterprise Institute for Public Policy Research (2008). 

http://www.aei.org/article/28972 

This article warns that, while data and research are powerful tools that can be used to 

improve education, we should use and interpret them with caution. 

Chapter 2. Prerequisites for Effective Data Use 

State Education Data Systems That Increase Learning and 

Improve Accountability 

Learning Point Associates (2004). 

http://dataqualitycampaign.org/files/Tools-State_Education_Data_Systems_That_Increase_ 

Learning_and_Improve_Accountability.pdf 

This paper examines the components needed to address system upgrades and provides 

policy recommendations to help states create data systems that promote accountability and 

improve student learning. 

Addressing the Need for Better Data on Teaching in Colorado: 

Unique Teacher Identifier Stakeholder Process Report 

The Alliance for Quality Teaching (2007). 

http://aqt.civicore.com/modules/resources/resources/69.pdf 

This report provides information about unique teacher identifier systems, and disseminates 

the outcomes of the four stakeholder meetings discussing such a system for Colorado. 

Book Four of Four:Advanced LDS Usage 53 
Telling Compelling Stories With Numbers 

http://nces.ed.gov/programs/slds/nov08_presentations.asp 

Session VI, a presentation on making sense of data, is relevant for education agency staff 

who want to know more about data use in presentations. Variations in data presentation 

through graphs and figures are presented, as are a definition and discussion of dashboards. 

Chapter 3. What Do Stakeholders Get? LDS Data Uses and Users 

Data Use 

The University of Texas at Austin 

http://edadmin.edb.utexas.edu/datause/index.htm 

This website includes many publications related to the use of data for informed 

decisionmaking at the local level. 

What Is the Role of the State Education Agency in Providing Local 

Education Agencies With Access to Key Data for Instruction? 

Education Information Management Advisory Consortium (2007).
 

This brief summarizes points raised in an EIMAC roundtable; participants discussed ways 


state education agencies could provide useful longitudinal data to districts. 


Protecting Student Records and Facilitating Education 

Research: A Workshop Summary 

National Research Council (2008). 

http://www.nap.edu/catalog.php?record_id=12514 

Chapter 3 of this report discusses the benefits of research using longitudinal data. 

The Next Step: Using Longitudinal Data Systems to Improve 

Student Success 

Data Quality Campaign (DQC 2009). 

http://dataqualitycampaign.org/files/NextStep.pdf 

This report discusses a number of key steps that education agencies need to take in order to 

facilitate effective data use. 

National Center for Analysis of Longitudinal Data in 

Education Research 

http://www.caldercenter.org 

Visit the National Center for Analysis of Longitudinal Data in Education Research 

(CALDER) for examples of research made possible by the availability of longitudinal 

student data. In addition to a large collection of reports that analyze longitudinal data, 

the site provides links to several longitudinal state data sets (http://www.caldercenter.org/ 

research/statedata.cfm). 

54 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Achieving With Data: How High-Performing School Systems Use 

Data to Improve Instruction for Elementary Students 

Center on Educational Governance, University of Southern California (2007). 

http://nces.ed.gov/programs/slds/ldsshare/documentdirectory/dataaccessuse/datause/ 

achievingwithdata.pdf 

This report looks at several school systems that have successfully used data to improve 

student learning. The document highlights their experiences and suggests key strategies to 

help other districts not only become more data driven, but also use the data effectively. 

Implementing Data-Informed Decision Making in Schools— 

Teacher Access, Supports, and Use 

U.S. Department of Education (2009). 

http://www.ed.gov/rschstat/eval/tech/data-informed-decision/data-informed-decision.doc 

This report presents findings from several studies on data-informed decisionmaking in 

schools. It draws on surveys, interviews, and case studies to explore the prevalence and 

character of data use in schools, including data availability, use, and understanding. The 

support provided to help educators effectively use the data to improve achievement is also 

considered. 

A Policymaker’s Guide to the Value of Longitudinal Student Data 

Education Commission of the States (2002). 

http://www.ecs.org/clearinghouse/40/21/4021.htm 

This early report addresses the types of policy questions longitudinal student data can answer. 

Data Mining and Statistics: What Is the Connection? 

The Data Administration Newsletter (2004). 

http://www.tdan.com/view-articles/5226 

This article describes statistics and data mining, explores the relationship between the two fields 

of inquiry, and argues that both approaches are valuable in efforts to understanding data. 

Teachers’ Use of Student Data Systems to Improve Instruction 

U.S. Department of Education (2007). 

http://www2.ed.gov/rschstat/eval/tech/teachers-data-use/teachers-data-use-intro.html 

Using national surveys of teachers and school districts, this report provides the first 

national estimates of K–12 teachers’ access to, and use of, electronic student data 

management systems. 

Making Sense of Data-Driven Decision Making in Education: 

Evidence From Recent RAND Research 

RAND Corporation (2006), 

http://www.rand.org/pubs/occasional_papers/OP170 

This paper discusses research on how schools and districts are analyzing a range of data, 

including but not limited to achievement test results to inform decisionmaking to improve 

student outcomes. 

Book Four of Four:Advanced LDS Usage 55 
Tapping into the Power of Longitudinal Data: A Guide for 

School Leaders 

Data Quality Campaign (DQC 2008). 

http://dataqualitycampaign.org/files/publications-tapping_into_the_power_of_longitudinal_data-a_ 

guide_for_school_leaders-010108.pdf 

This guide looks at ways teachers and principals can use longitudinal data to meet students’ 

individual needs and improve performance. 

Linking Teacher and Student Data to Improve Teacher and 

Teaching Quality 

Data Quality Campaign (DQC 2007). 

http://dataqualitycampaign.org/files/Meetings-DQC_Quarterly_Issue_Brief_031207.pdf 

This brief discusses the benefits of linking student and teacher data, important metrics 

for improving teacher quality and retention, how to develop comprehensive teacher data 

systems, the pros and cons of value-added measurements, and more. 

Involving Teachers in Data-Driven Decision Making: Using 

Computer Data Systems to Support Teacher Inquiry and Reflection 

Wayman, J. (2005). Journal of Education for Students Placed at Risk, 10(3), 295–308. 

http://edadmin.edb.utexas.edu/datause/papers/Wayman%20-%20Teacher%20Data%20Use.pdf 

This article discusses teacher use of data systems and tools, providing insight into their 

function and discussing conditions that make them most useful to teachers. 

What Works Clearinghouse (WWC) 

Institute of Education Sciences. 

http://ies.ed.gov/ncee/wwc 

This website provides educators, policymakers, researchers, and the public with scientific 

evidence for what works in education. This central, trusted source promotes informed 

education decisionmaking through easily accessible databases and user-friendly reports on 

the effectiveness of replicable education interventions (programs, products, practices, and 

policies) intended to improve student outcomes. 

Getting the Evidence for Evidence-Based Initiatives: How the 

Midwest States Use Data Systems to Improve Education Processes 

and Outcomes 

National Center for Education Evaluation and Regional Assistance (2007). Issues andAnswers 

Report (REL 2007–016). U.S. Department of Education, Institute of Education Sciences. 

http://ies.ed.gov/ncee/edlabs/regions/midwest/pdf/rel_2007016_sum.pdf 

This report reviews the progress of several midwestern states in developing LDSs and the 

use of data systems in general. Based on interviews with SEA officials and federal agency 

staff, the authors review the work that was done, the challenges that were faced, and the 

current requirements being pursued by the states. 

56 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Continuous Improvement: It Takes More Than Test Scores 

Bernhardt, V. (2004). ASCA Leadership Magazine, Nov.-Dec. 2004. 

http://eff.csuchico.edu/downloads/testscores.pdf 

This article summarizes why analyzing state assessment results is only the beginning of 

effective data-driven decisionmaking. 

Intersections: New Routes Open When One Type of Data 

Crosses Another 

Bernhardt, V. (2000). Journal of Staff Development, 21(1), 33–36. 

http://eff.csuchico.edu/downloads/intersct.pdf 

This article discusses how different types of data can be used to help educators answer key 

questions about student learning and the effectiveness of educational inputs. 

Multiple Measures 

Bernhardt, V. (1998). California Association for Supervision and Curriculum Development. 

http://eff.csuchico.edu/downloads/mmeasure.pdf 

This article emphasizes the need to consider a range of data about education in addition 

to student assessment data, commonly the sole source of information available. The 

author suggests we need other types of education data, including student demographics, 

educational inputs, and perceptions. The article describes why each type of data is useful, 

and what can be learned by using a combination of these data to gain greater insight before 

making decisions. 

Leadership Summit 2006 Toolkit: Using Data for School Reform 

State Educational Technology Directors Association (2006). 

http://www.setda.org/toolkit/nlitoolkit2006/data/index.htm 

This toolkit offers practical checklists, matrices, survey instruments, sample letters, and 

other tangible components that can help education technology leaders at all levels to 

improve teaching and learning at the school and student levels. 

Benefits of and Lessons Learned From Linking Teacher and 

Student Data 

Data Quality Campaign (DQC 2007). 

http://www.dataqualitycampaign.org/files/publications-benefits_of_and_lessons_learned_from_ 

linking_teacher_and_student_data-120607.pdf 

This brief looks at how several states are linking student and teacher data, the benefits of 

those links, and lessons learned. 

Using Data to Impact Instruction 

Aldine Independent School District (2006). 

http://dataqualitycampaign.org/files/Presentations-DQC_Quaterly_Meeting_Using_Data_to_ 

Impact_Instruction_092506.pdf 

This presentation provides an overview of the Aldine Independent School District’s 

approach to data use for instructional planning. The Texan district uses a combination of 

trailing and leading data, creates action plans based on those data, and keeps scorecards to 

track progress at various levels of the organization. 

Book Four of Four:Advanced LDS Usage 57 
Research Partnerships and Data Sharing Agreements 

Protecting Student Records and Facilitating Education 

Research: A Workshop Summary 

National Research Council (2008). 

http://books.nap.edu/openbook.php?record_id=12514&page=44 

See pages 45–46 and 78–79 for practical discussions of research partnerships and datasharing. 

IES Statewide Longitudinal Grant Program’s LDS Share 

Institute of Education Sciences. 

http://nces.ed.gov/programs/slds/ldsshare/slds.aspx 

This document-sharing resource contains many sample MOUs submitted by education 

agencies. 

State Educational Agencies Form Partnership to Make P–20 Data 

System Information Available for Reporting, Analysis, and Research 

Kentucky Education and Workforce Development Cabinet (2009). 

http://educationcabinet.ky.gov/newsroom/pressreleases/stateeducationalpartnership.htm 

This press release discusses Kentucky’s work to establish data sharing across P–20 through 

collaboration between K–12 and postsecondary education organizations, as well as an 

educator workforce development body. The partnership allows data sharing and analyses 

across these sectors. 

Balanced Assessment System 

Professional Papers and Recent Staff Articles 

Pearson Assessment Training Institute. 

http://www.assessmentinst.com/resources/papers 

This page links to a large collection of papers on balanced assessment and related topics. 

Assessment for Learning Defined (Stiggins 2005) is especially useful. 

Data-Driven Teaching: Tools and Trends 

Rennie Center for Education Research and Policy (2006). 

http://www.renniecenter.org/0224_Brief_FINAL.pdf 

This brief evaluates the implementation of three formative assessments in urban school 

districts. It offers useful information for selecting a formative assessment system that will 

meet a district’s needs; and for effectively implementing the system, and then applying 

those formative assessment data. 

58 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Growth Measures and Models 

Are We There Yet? What Policymakers Can Learn From 

Tennessee’s Growth Model 

Barone, C. (2009). 

http://www.educationsector.org/research/research_show.htm?doc_id=847058 

This article evaluates the Tennessee Growth Model, pointing out its strengths and 

weaknesses. The page also links to a response from the model’s developer. 

A “Student-Centered” Growth Model for School 

Accountability 

Oregon Department of Education (2009). 

http://www.ode.state.or.us/wma/policy/accountability/growth/growth-overview.pdf 

This document provides a brief summary of Oregon’s growth model, including a short 

description, explanation of why the state and its stakeholders took this approach to 

measuring student academic growth, and links to a file that includes a growth-target 

calculator with formulas. 

Value-Added Models 

Separating Growth From Value-Added 

Yeagley, R. (2007). The School Administrator, 64(1):18. 

http://www.thefreelibrary.com/separating+growth+from+value+added:+two+academic+models+ 

offer ...-a0157588355 

This article points out the differences between approaches to measuring value-added 

and growth and discusses the merits and uses of each. Another article follows this one 

addressing the implementation of a growth model in a California school system. 

Comparisons Among Various Educational Assessment 

Value-Added Models 

SAS Institute (2006). 

http://nces.ed.gov/programs/slds/ldsshare/documentdirectory/dataaccessuse/valueaddedmodels/ 

billsanders_valueaddedmodelcomparisons.pdf 

This white paper highlights the differences between several classroom-level, value-added 

models; and discusses the advantages and disadvantages of each, especially as they relate to 

teacher impact. 

Value-Added Indicators: A Powerful Tool for Evaluating 

Science and Mathematics Programs and Policies 

Meyer, R. (2000). National Center for Improving Science Education. 

http://addingvalue.wceruw.org/related%20bibliography/articles/meyer.pdf 

This brief highlights the advantages of value-added models over the more commonly used 

indicators of student performance: average and median test scores and proficiency-level 

indicators. 

Book Four of Four:Advanced LDS Usage 59 
Value-Added Indicators: Do They Make an Important Difference? 

Evidence From the Milwaukee Public Schools 

Meyer, R. (2003). Wisconsin Center for Education Research. 

http://www.eric.ed.gov/PDFS/ED497583.pdf 

This working paper demonstrates the utility of value-added data using a Wisconsin 

district’s data. It discusses why this method of measuring academic performance is superior 

to other more commonly used indicators. 

Methodological Concerns About the Education Value-Added 

Assessment System 

Amrein-Beardsley, A. Educational Researcher 37(2), 65–75. 

http://www.aera.net/uploadedfiles/publications/journals/educational_researcher/3702/ 

03edr08_065-075.pdf 

This article examines the Education Value-Added Assessment System (EVAAS), focusing 

on the methodological and practical issues associated with its approach. 

Value-Added Analysis and Education Policy 

National Center for Analysis of Longitudinal Data in Education Research (2007). 

http://www.urban.org/uploadedpdf/411577_value-added_analysis.pdf 

This brief describes issues related to estimating and measuring the quality of instruction, 

highlighting the importance of taking student differences into account and the advantages 

of using academic growth rather than differences in test scores. 

Value-Added Models in the News... 

Growth Data for Teachers Under Review 

Education Week 28(9), October 2008. 

http://www.edweek.org/ew/articles/2008/10/22/09value_ep.h28.html?tmp=164757091 

This article summarizes the issues and challenges around the use of value-added data for 

teacher evaluations. 

“Value-Added” Gauge of Teachers Probed 

Education Week 28(36), July 2009.
 

This article discusses several studies that evaluated value-added measures. It provides a 


good summary of the contemporary arguments around the use of these measures.
 

Scrutiny Heightens for “Value Added” Research Methods 

Education Week 27(36), May 2008.
 

This article makes cautionary points about the use of value-added measures. While they 


hold great promise, the article suggests that practitioners must proceed carefully as we learn 


more about how best to model and use these measures.
 

60 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Back to School: Performance Data Driving Education Now 

Pittsburgh Post-Gazette, August 29, 2006. 

http://www.post-gazette.com/pg/06241/717104-298.stm 

This article discusses Pennsylvania’s plan to implement a value-added system across the 

state, giving every district access to its results. 

William Sanders Describes Value-Added Assessment for 

Measuring Student Progress 

University of North Carolina School of Education (2006). 

http://soe.unc.edu/news_events/news/2006/sanders_william.php 

This story reports on a lecture by William Sanders on his value-added assessment system. 

Chapter 4. Professional Development and Training for 

Effective Use of LDS Data 

Direct Access to Achievement 

Oregon Department of Education. 

http://www.oregondataproject.org/ 

This website on Oregon’s Direct Access to Achievement (DATA) project contains a host of 

resources including training materials for instructional improvement informed by data. The 

site also includes a strand on improving data quality. 

Beyond the Numbers: Making Data Work for Teachers and 

School Leaders 

White, S. (2005). Advanced Learning Press. 

This book on local-level, data-driven decisionmaking was used in Oregon’s professional 

development initiative, the DATA Project. In addition to discussing the foundational 

principles for data analysis and the strategies necessary to turn analysis into action, it 

provides a specific framework for analyzing a range of data, yet is general enough to 

support data analysis efforts regardless of the specific technology available to users. 

Technology @ Your Fingertips 

National Forum on Education Statistics (1998). 

http://nces.ed.gov/pubs98/98293rev.pdf 

See chapter 6 for a collection of best practices on training staff and others to use 

technology. While the chapter applies to general technology solutions, rather than specific 

to data systems, many of its lessons are relevant to an LDS training effort. 

Collaborative Approaches to Education Data Training and 

Professional Development 

Center for Educational Performance and Information (2009). 

http://nces.ed.gov/programs/slds/zip/08_f_09a.zip 

This presentation provides an overview of Michigan’s approach to professional 

development on data quality and data use. 

Book Four of Four:Advanced LDS Usage 61 
Data Wise: A Step-by-Step Guide to Using Assessment Results to 

Improve Teaching and Learning 

Harvard Educational Pub Group (2005).
 

This book discusses what school leaders need to know and do to ensure that the piles of 


student assessment results landing on their desks are used to improve student learning in 


their schools. 


Making Sense of Data-Driven Decision Making in Education: 

Evidence From Recent RAND Research 

RAND Corporation (2006). 

http://www.rand.org/pubs/occasional_papers/OP170 

See pages 7–8 for a discussion and analysis of professional development approaches. 

Teaching and Learning Cooperative: Sample Activities 

Poway Unified School District. 

http://www.powayusd.com/projects/literacy/ssttl/pdfs/part%20ii%20samples.pdf 

This document describes various approaches to local-level professional development. 

While not specific to data analysis, it includes a section on teacher research and guides 

educators through an exercise of data-driven decisionmaking. 

62 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
appendix D 

Forum and other nCeS 

resources 

Crisis Data Management: A Forum Guide to Collecting and 

Managing Data about Displaced Students (NFES 2010–804) 

The Forum Guide to Data Ethics (NFES 2010–801) 

http://nces.ed.gov/forum/pub_2010801.asp 

Every day, educators collect and use data about students, staff, and 

schools. Some of these data originate in individual student and staff 

records that are confidential or otherwise sensitive. And even those 

data that are a matter of public record, such as aggregate school 

enrollment, need to be accessed, presented, and used in an ethically 

responsible manner. While laws set the legal parameters that govern 

data use, ethics establish fundamental principles of “right and 

wrong” that are critical to the appropriate management and use of education data in the 

technology age. This guide reflects the experience and judgment of experienced data 

managers; while there is no mandate to follow these principles, the authors hope that the 

contents will prove a useful reference to others in their work. 

http://nces.ed.gov/forum/pub_ 2010804.asp 

This publication provides guidelines that can be used by elementary 

and secondary education agencies to establish policies and procedures 

for collecting and managing education data before, during, and after a 

crisis. 

The Forum Guide to Metadata: The Meaning Behind Education 

Data (NFES 2009–805) 

http://nces.ed.gov/forum/pub_ 2009805.asp 

This guide offers best practice concepts, definitions, 

implementation strategies, and templates/tools for an audience of 

data, technology, and program staff in state and local education 

agencies. This resource was developed to improve these audiences’ 

awareness and understanding of metadata and, subsequently, the 

quality of the data in the systems they maintain. 

Book Four of Four:Advanced LDS Usage 63 
Every School Day Counts: Forum Guide to Collecting and 

Using Attendance Data (NFES 2009–804) 

http://nces.ed.gov/forum/pub_ 2009804.asp 

This guide offers best practice suggestions on collecting and using 

student attendance data to improve performance. It includes a 

standard set of codes to make attendance data comparable across 

districts and states. The publication includes real-life examples of how 

attendance information has been used by school districts. 

NCES Handbooks and NCES Handbooks Online 

http://www.nces.ed.gov/programs/handbook 

The NCES Handbooks are a valuable source of metadata 

for organizations and individuals interested in education data. These print and online 

resources define standard education terms for students, staff, schools, local education 

agencies (LEA), intermediate education agencies, and state education agencies (SEA). The 

Handbooks are intended as reference documents for public and private organizations, 

including education institutions and early childhood centers; as well as education 

researchers and other users of education data. In order to improve access to this valuable 

resource, NCES has also developed the NCES Handbooks Online, a web-based tool that 

allows users to view and download information via an electronic table of contents, a drilldown finder, element name and first letter searches, and advanced query options. 

National Education Data Model 

http://nces.ed.gov/forum/datamodel/index.aspx 

The National Education Data Model (NEDM) is the first 

non-proprietary, national education data model developed to help schools, LEAs, and 

states design or guide the selection of systems for instructional delivery, data-driven 

decisionmaking, data collection, operations, and reporting. The model provides a national 

blueprint to help schools evaluate and improve instructional tools; communicate needs to 

their umbrella agency or to vendors; enhance the movement of student information from 

one LEA to another; and, in the end, have better tools to inform instruction. NEDM can 

be used by educators, vendors, and researchers to understand the information required for 

teaching, learning, and administrative systems. 

Managing an Identity Crisis: Forum Guide to Implementing New 

Federal Race and Ethnicity Categories (NFES 2008–802) 

http://nces.ed.gov/forum/pub_ 2008802.asp 

This best-practice guide was developed to help state and local 

education agencies implement the new federal race and ethnicity 

categories, thereby reducing redundant efforts within and across states, 

improving data comparability, and minimizing reporting burden. 

Users may select and adopt strategies that will help them quickly begin 

the process of implementation in their agencies. 

64 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Forum Guide to Core Finance Data Elements (NFES 2007–801) 

http://nces.ed.gov/forum/pub_ 2007801.asp 

This publication establishes current and consistent terms and 

definitions for maintaining, collecting, reporting, and exchanging 

comparable information related to education finances. It is designed 

to accompany Financial Accounting for Local and State School 

Systems: 2003 Edition by identifying common reporting requirements 

and defining frequently used indicators and calculations that use data 

elements from accounting and other data systems. 

Forum Curriculum for Improving Education Data: A Resource for 

Local Education Agencies (NFES 2007–808) 

http://nces.ed.gov/forum/pub_ 2007808.asp 

This resource supports efforts to improve the quality of education 

data by serving as training materials for K–12 school and district staff. 

It provides lesson plans, instructional handouts, and other resources; 

and presents concepts necessary to help schools develop a culture for 

improving data quality. 

Forum Guide to Decision Support Systems: A Resource for 

Educators (NFES 2006–807) 

http://nces.ed.gov/forum/pub_ 2006807.asp 

This Forum guide was developed to help the education community 

better understand what decision support systems are, how they are 

configured, how they operate, and how they might be developed and 

implemented in an education setting. 

Forum Guide to Virtual Education (NFES 2006-803)
 

http://nces.ed.gov/forum/pub_ 2006803.asp 

This publication offers recommendations for collecting accurate, 

comparable, and useful data about virtual education in elementary and 

secondary education settings. It highlights policy questions and data 

elements critical to meeting the information needs of policymakers, 

administrators, instructors, and parents of students involved in virtual 

education. 

Book Four of Four:Advanced LDS Usage 65 
Forum Guide to the Privacy of Student Information: 

A Resource for Schools (NFES 2006–805) 

http://nces.ed.gov/forum/pub_ 2006805.asp 

This publication was written to help school and local education agency 

staff better understand and apply the Family Educational Rights and 

Privacy Act (FERPA), a federal law that protects the privacy interests 

of parents and students with respect to information maintained in 

student education records. It defines terms such as “education records” 

and “directory information,” and offers guidance for developing 

appropriate privacy policies and information disclosure procedures 

related to military recruiting, parental rights and annual notification, videotaping, online 

information, media releases, surveillance cameras, and health-related information. 

Accounting for Every Student: A Taxonomy for Standard Student 

Exit Codes (NFES 2006–804) 

http://nces.ed.gov/forum/pub_ 2006804.asp 

This publication was developed to help education agencies develop 

effective information systems for tracking students’ enrollment status. 

It presents a student-level exit code taxonomy for states and districts 

that accounts for 100 percent (not 90 or 110 percent) of all students. It 

also offers “best practice” advice regarding tracking students, collecting 

exit codes data, and distinguishing among high school completion 

credentials. 

Forum Guide to Education Indicators (NFES 2005–802) 

http://nces.ed.gov/forum/pub_ 2005802.asp 

This publication provides encyclopedia-type entries for 44 commonly 

used education indicators. Each indicator entry includes a definition, 

recommended uses, caveats and cautions, related policy questions, data 

element components, a formula, commonly reported subgroups, and 

display suggestions. The document will help readers better understand 

how to appropriately develop, apply, and interpret commonly used 

education indicators. 

Forum Guide to Building a Culture of Quality Data 

(NFES 2005–801) 

http://nces.ed.gov/forum/pub_ 2005801.asp 

This publication focuses on data entry: getting things done right at the 

source. It recommends a practical process for developing a “Culture 

of Quality Data” based around individual tip sheets for individuals 

involved in providing data, including principals, teachers, office staff, 

school board members, superintendents, data stewards, and technology 

staff. 

66 Traveling Through Time:The Forum Guide to Longitudinal Data Systems 
Forum Unified Education Technology Suite (2005)
 

http://nces.ed.gov/forum/pub_tech_ suite.asp 

This publication presents a practical, comprehensive, and tested 

approach to assessing, acquiring, instituting, managing, securing, 

and using technology in education settings. It is written for 

individuals without extensive experience with technology who have been tasked with 

leading technology initiatives in a school or district setting,. 

Forum Guide to Protecting the Privacy of Student Information: 

State and Local Education Agencies (NCES 2004–330) 

http://nces.ed.gov/forum/pub_ 2004330.asp 

This publication presents a general overview of privacy laws and 

professional practices that apply to information collected for, and 

maintained in, student records. The guide provides an overview of key 

principles and concepts governing student privacy; summarizes federal 

privacy laws; identifies issues concerning the release of information 

to both parents and external organizations; and suggests good data 

management practices for schools, districts, and state education 

agencies. 

Facilities Information Management: A Guide for State and Local 

Education Agencies (NCES 2003–400) 

http://nces.ed.gov/forum/pub_ 2003400.asp 

This publication provides a framework for identifying a basic set 

of school facilities data elements and definitions that will meet the 

information needs of school and community decisionmakers, school 

facility managers, and the general public. It presents recommendations 

for designing and maintaining an information system that addresses the 

condition, design, use, management, and financing of elementary and 

secondary education facilities. Commonly used measures, data elements, 

and additional resources for the practitioner are also included. 

Planning Guide for Maintaining School Facilities 

(NCES 2003–347) 

http://nces.ed.gov/forum/pub_ 2003347.asp 

This publication is intended to help school facilities managers plan 

for efficient and effective operations. It provides practical advice on 

a range of topics, including how to conduct a facilities audit, plan for 

maintenance to ensure smooth operations and avoid costly surprises, 

manage staff and contractors, and evaluate maintenance efforts. 

Book Four of Four:Advanced LDS Usage 67