Assessment & Growth Models
Is all of the data gathered in education leading to information overload or are they data that can be readily used to support decisions made at the school level as well as the federal level? Find out with this Optimal Reference Guide from ESP.
In Part III of our series on Growth Models, we review basic concepts about education assessment scores and how we interpret them.
Growth Models are currently being over valued, under challenged, and are in need of critical evaluation. Our Optimal Reference Book, “Growth Models – Finding Real Gains,” contributes to the debate and understanding of how we should evaluate the models.
Just say growth models in an education agency and the debate begins. These models and their cousins the value-add models embroil us all in one of the greatest politimetric struggles of our time. Overstated? Maybe, maybe not.
Part II describes growth models and is a primer for those wanting to be conversant about them&emdash;and which might be appropriate for a particular context.
If you manage or use an education database and are concerned about confidentiality, read this paper. There’s no need for extreme measures that remove too much personal information from all of our databases. Polititech decisions can support official reporting, research, FOI requests, and FERPA/HIPAA.
Project Halloween had several objectives but only one Ghoul: To get the Bugs out of our client’s web app. What else could explain the images being plagued by several persistent Ghost Images? We should have never allowed those new visuals to be developed in Casper, Wyoming. Whatever Possessed us in the first place? I would have spoken up sooner, but I was Scared Speechless.
The FERPA Sherpa guides you through the necesity to develop a Data Access and Management Policy that specifies the answers to the challenges of FERPA and a P-20W longitudinal data system. Different states have different laws and policies, but the areas that need be covered are the same. Having a Data Access and Management Policy helps all participants and stakeholders to know what they can and cannot do with student data, what they must do if they have questions or data needs, and what will ultimately happen to the individually identifiable student data.
Every state has data standards. The USED has standards for the data that states report annually. In fact, USED even has a book of recommended data elements. The REAL issue is that we don’t yet have a single, authoritative dictionary of data standards that governs all our collections, repositories, and reports throughout education. How close are we? ESP’s Optimal Reference Guide gives you a common understanding of how close we are and how we might get there.
This comprehensive report fleshes out the issues that states encounter when assigning statewide student identifiers.
Defining data scope is arguably the most difficult and the most important milestone in an ed tech project. Data scope is the agreed-upon source data to be moved from by ETL into the ed tech solution’s destination database to satisfy the contract. This paper provides insight into the process for defining data scope to reduce risk in an ed tech project.
ESP’s Optimal Reference Book is an up-to-date, single source of insightful information containing three Optimal Reference Guides related to FERPA regulations.
This comprehensive reference guide provides a template for state education agencies when they are preparing an RFP for the assignment of statewide student IDs.
EDEN/EdFacts presents mandates that change the landscape for SEAs reporting to USED. This ESP’s recommended list of proposed data elements for collection of unit-level data needed for EDEN reporting provides our insights into how an SEA can comply.
ESP’s Optimal Reference Guide discusses why it is crucial to begin planning now for changes in reporting requirements for race/ethnicity.
ESP’s mission is to enhance data driven decision making (D3M). The D3M Alliance introduces a proven, replicable architecture addressing enterprise interoperability, identity management, vertical reporting, and longitudinal data management.
Dr. Barbara Clements has seen education data standards evolve over the years and provides her historical perspective on their genesis and significance.
Best practice has been consolidated into a comprehensive guide for managing an education agency’s information system. ESP’s contracts with individual SEAs and with the USED contributed insights into effective policy and practice.
ESP’s Optimal Reference Guide provides expertise and guidance on how to accurately track and report on individual students such that the graduation rate is more accurate and has more meaning.
ESP’s Optimal Reference Guide discusses ways to bring FERPA in line with today’s technology and information practices and makes a clear case for a rewrite.
This presentation describes the steps required to complete a comprehensive disaster prevention and recovery planning process.
Existing disaster recovery planning guides tend to focus on business technology architecture – not school system realities. ESP’s whitepaper discusses conventional wisdom and best practices for education agencies.
We have become very demanding of our data. ESP’s Optimal Reference Guide discusses the nature of the data that we use to define our schools, student academic progress, and accountability indicators.
Data driven decision making relies on getting the right data, in the right way, right away, and getting them right in the process. ESP’s Optimal Reference Guide explains how the right data management makes this happen.
ESP believes that the implementation of common course classification systems across the country and the crosswalking of them together is an essential next step for education agencies.
Part I of ESP’s Optimal Reference Guide series on Data Quality ties together the foundations of data quality from the formal information systems literature with the practical aspects of data quality in the arena of public education decision making.
Part II of ESP’s Optimal Reference Guide series on Data Quality delivers best practices and principles that are time-tested and pulled from years of real school experiences.
This process map details Best Practices for how to get quality data.
There is a complex, cross-functional, system-wide process that must work to define, collect, store, analyze, and report quality data. Our white paper shows you how to get better data for D3M.
If your state is thinking of implementing an electronic record/transcript system — read this FIRST! Our paper explains why your state should consider a single PK-20 solution, not one that just handles “High School to College” transactions.
ESP’s Optimal Reference Guide directly tackles the issue of why SEAs should be involved in electronic student records exchange and the importance of standards-based electronic student records.
Longitudinal Data Systems
The Optimal Reference Guide series introduces data warehousing by defining what it is and what it is not. This paper just may help you answer whether a data warehouse is a necessary component of your overall information system.
The second in a series of Optimal Reference Guides on data warehousing details the design, structure, and configuration parameters of a data warehouse.
ESP’s recommendations for a PK-20 longitudinal data system.
The D3M Framework is a high-level picture that immediately makes sense of how education data move within an enterprise education information system and into the view of the person responsible for making decisions based on these data.
What does an education agency need for successful implementation of a longitudinal data system? Check out our complete listing of white papers and how they relate to a comprehensive longitudinal data system in this illustration.
ESP’s Optimal Reference Guide describes the steps for building a longitudinal data system depicted in our 11×17 process illustration, “New ESP Framework for Data-Driven Decision Making.”
ESP’s paper provides a checklist of the components an education agency needs to deliver an information system that truly supports Data-Driven Decision Making.
FERPA is just the start of policy and practice issues impacting how education data are managed. ESP’s Optimal Reference Guide is the third in a series focused on data-driven decision making and the development of effective data warehouses.
Presented at the NCES STATS-DC 2010 Conference. Two SEA experts describe how they establish and manage metadata standards for their states for data collections, repositories, and outputs/reports. Two national experts provide historical and future perspectives on how data standards (such as NCES Handbooks, SIF, EDFacts, and others) have evolved as the foundation for longitudinal data system data models.
Data that everyone agrees are worth the effort to collect and report are max yield data. (Presentation for the SEA Chiefs meeting in Lake Tahoe)
Data quality requires standards and discipline. Everyone and every datum matter in the pursuit of data quality. (Presentation for the Iowa Data Quality Conference)
How can quality data impact the instructional process? (Presentation for the Learning Point conference on data quality)
Process Illustrations and Supporting Documents
What does an education agency need for a successful implementation of a longitudinal data system? ESP’s Optimal Reference Guides (ORGs) and Optimal Reference Books (ORBs) explain best practices from our extensive work with education agencies nationwide.
Quality data for decision making begin with consistent definitions. We’ve used best practices in data management to create an information systems architecture for education agencies. The components and questions that need answers are depicted in our D3M Data Tree.
This whitepaper enhances to above illustration with an innovative look at how data travel from a school secretary all the way to the Secretary of Education.
See how decision makers can gain confidence in and rely upon data.
Can quality, timely data really move from districts to SEAs in weeks rather than months? See how ESP’s State Report Manager saves SEA’s time, money, and resources.
ESP’s most popular illustration shows how data move from schools, to districts, to states, to federal levels – within applications to data driven decisions.
Commissioned by the U.S. Secretary of Education, this illustration describes the technology infrastructure required to ensure NCLB success.
This white paper, commissioned by the U.S. Secretary of Education details the infrastructure described in the Illustration. This review shows how states can best evaluate their status for No Child Left Behind.
Education agencies have certain challenges that make them different than other businesses. Our paper explains why industry-standard project management methodologies fall short in delivering the results required of education agencies.
Risk can cause damaging delays or even kill a project. Our paper defines risks as they pertain to an education agency’s environment, alerts agencies to potential risks, and details our methodology for managing risks in large scale projects.
ESP’s paper covers obtaining and sustaining project buy-in using marketing techniques.
ESP created “Project Scope Document Overview with Detailed Descriptions for the Data Scope Document Section” to provide an example of best practices for reducing risk in EdTech projects by achieving agreement on project scope. In our extensive experience with education information systems, we have documented that managing Data Scope is the key to successful on-time project delivery.
ESP’s paper works backward to locate, collect, and synthesize the various data, information, knowledge, insights, indicators, and indexes that will help form the action an educator wants to make.
What criteria should SEA’s use when establishing the rules and selecting a minimum?
This comprehensive report fleshes out the confidentiality and reliability issues that states encounter when reporting education data.
ESP’s Optimal Reference Guide pushes us to think beyond the limits of our data, beyond the reports, to relationships within the data that reveal a greater potential. Data CAN be used effectively by educators. This book will show you how.
ESP’s Optimal Reference Guide defines a framework for designing and producing action reports for better data driven decision making.
This white paper presents an overview of the important issues in procuring education administrative technology systems from the learning organization’s perspective, outlines a methodology from stakeholder buy-in during the requirements-gathering phase to the eventual selection of a solution, and makes the case for the strategic advantages offered by following a highly structured procurement process.
Trends in Education
In this paper we begin to compile lessons learned about dashboards. Our hope was that we would discover what research has shown ensures effective usage of a dashboard.
We published our first version of this Optimal Reference Guide last year. Because of its popularity, we’ve released a new version (v.2.0) with more definitions and better usability (linked terms).
ESP’s paper focuses on easy-to-understand definitions of complex IT terminology and concepts. We explain why these terms are relevant and what they mean in today’s education environment.
This ESP Optimal Reference Guide reviews Dr. Glynn Ligon’s 1996 documented predictions about the future of data driven decision making in education.
ESP’s Optimal Reference Guide makes predictions about the future of education information technology.
Childrens’ Educational Records and Privacy: A Study of Elementary and Secondary School State Reporting Systems
In a growing effort to measure student progress and change, there is a popular trend to establish longitudinal data systems at the state level. The Fordham Law School’s Center on Law and Information Policy (CLIP) has released a report, based on a survey of all 50 states, that finds educational databases implemented across the country are in violation of FERPA and are ignoring key privacy concerns. The study provides best practice recommendations and proposes legislative reform to address these confidentiality concerns.
ESP’s President and CEO, Dr. Glynn D. Ligon contributed Action Step and Recommendation #7 – “Integrate Data Systems.”
SIF and ESP Solutions Group have developed this tool for schools, districts, and states to begin and continue working toward data interoperability and automated vertical reporting.
The NCES Data Handbooks provide guidance on consistency in data definitions and maintenance for education data, so that such data can be accurately aggregated and analyzed.
This U.S. Department of Education report reviews student data systems, how they are used by school staff members, and whether or not they are influencing student instruction. ESP’s Glynn D. Ligon was a member of the report’s Technical Working Group.
This Power Point presentation from USED tells you exactly what you need to know.
An Excel file of large urban school districts.
Nobody! Click one button and get all your education data! Too good to be true? Yes.