Jack Buckley, Commissioner for the National Center for Education Statistics, presented at the Content in Context Metadata Lab on the work the U.S. Department of Education has done on the Common Education Data Standards.
Common Education Data Standards, Statewide Longitudinal Data Systems, and EDF...AAP PreK-12 Learning Group
Ross Santy of the US Department of Education presented on Common Education Data Standards, Statewide Longitudinal Data Systems, and EDFacts at the Content in Context Metadata Lab on June 4, 2012. This free workshop was presented by the Association of Educational Publishers (AEP) as part of the Learning Resource Metadata Initiative (LRMI).
Greg Grossmeier, Education Technology and Policy Coordinator for Creative Commons and Co-Chair of the Learning Resource Metadata Initiative (LRMI) technical working group, presented a background and update on the project to create a common metadata specification for online learning resources. Grossmeier presented at the Content in Context Metadata Lab, presented by the Association of Educational Publishers (AEP) on June 4, 2012.
Information about LRMI and the Learning Registry, presented by Sue Cowder and Jason Hoekstra of inBloom, Jim Klo of SRI, and Thor Prichard of Clarity Innovations
Common Education Data Standards, Statewide Longitudinal Data Systems, and EDF...AAP PreK-12 Learning Group
Ross Santy of the US Department of Education presented on Common Education Data Standards, Statewide Longitudinal Data Systems, and EDFacts at the Content in Context Metadata Lab on June 4, 2012. This free workshop was presented by the Association of Educational Publishers (AEP) as part of the Learning Resource Metadata Initiative (LRMI).
Greg Grossmeier, Education Technology and Policy Coordinator for Creative Commons and Co-Chair of the Learning Resource Metadata Initiative (LRMI) technical working group, presented a background and update on the project to create a common metadata specification for online learning resources. Grossmeier presented at the Content in Context Metadata Lab, presented by the Association of Educational Publishers (AEP) on June 4, 2012.
Information about LRMI and the Learning Registry, presented by Sue Cowder and Jason Hoekstra of inBloom, Jim Klo of SRI, and Thor Prichard of Clarity Innovations
Using Semantic Analysis for Curricular Alignment (Sloan-C Presentation)Jennifer Staley
In the LMS or CMS environment, content management frequently translates into single-purpose allocation of content resources, with cataloging and meta tagging being a haphazard affair. This results in potential duplication of content and significant time loss associated with asset retrieval for incorporation into new curricula. Because content is created with the notion that all contributors have knowledge of the underlying taxonomies or common vernacular that information is based upon, it is difficult for organizations to survey their content universe for existing objects that can be incorporated into emerging workflows or to assess relationships between content across disciplines.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Slides prepared for presentation at EdSurge Fusion 2019. Description: This talk will help school leaders understand what “counts” as evidence of efficacy from an edtech company and which types of evidence can be leveraged to gain access to federal funding.
After listening to this lightning talk, attendees will be able to:
1) Understand the different forms of evidence provided by EdTech companies
2) Organize evidence types in terms of rigor
3) Understand which forms of evidence can be used to leverage federal funding
Brandt Redd of the Bill & Melinda Gates Foundation discussed the Learning Resource Metadata Initiative (LRMI) and how it realtes to other major education projects including the Shared Learning Collaborative, Learning Registry, etc.
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
The design of data systems within education can be challenging due to a lack of easily accessible information and a large variety of stakeholders with differing needs. Architecting Academic Intelligence is the process of centralizing and making accessible the student administrative information to the every member of the administration, faculty and staff of the City Colleges of Chicago so as to more efficiently promote student success.
Using Semantic Analysis for Curricular Alignment (Sloan-C Presentation)Jennifer Staley
In the LMS or CMS environment, content management frequently translates into single-purpose allocation of content resources, with cataloging and meta tagging being a haphazard affair. This results in potential duplication of content and significant time loss associated with asset retrieval for incorporation into new curricula. Because content is created with the notion that all contributors have knowledge of the underlying taxonomies or common vernacular that information is based upon, it is difficult for organizations to survey their content universe for existing objects that can be incorporated into emerging workflows or to assess relationships between content across disciplines.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Slides prepared for presentation at EdSurge Fusion 2019. Description: This talk will help school leaders understand what “counts” as evidence of efficacy from an edtech company and which types of evidence can be leveraged to gain access to federal funding.
After listening to this lightning talk, attendees will be able to:
1) Understand the different forms of evidence provided by EdTech companies
2) Organize evidence types in terms of rigor
3) Understand which forms of evidence can be used to leverage federal funding
Brandt Redd of the Bill & Melinda Gates Foundation discussed the Learning Resource Metadata Initiative (LRMI) and how it realtes to other major education projects including the Shared Learning Collaborative, Learning Registry, etc.
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
The design of data systems within education can be challenging due to a lack of easily accessible information and a large variety of stakeholders with differing needs. Architecting Academic Intelligence is the process of centralizing and making accessible the student administrative information to the every member of the administration, faculty and staff of the City Colleges of Chicago so as to more efficiently promote student success.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
Five-Star Technology Solutions' Student Data Analysis and Staff Evaluation Software presentation. Software includes data reports, assessment software and teacher evaluation modules
Speaking the language of the Open Web - LRMI and Learning Resource (Description) Visibility presented by Stuart Sutton, Managing Director, Dublin Core Metadata Initiative
This presentation from the 2014 SXSWedu Conference discusses how LRMI makes it easier to discover and use educational materials that meet the needs of the teacher or learner.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Barbra Bard Sperling gives an overview of MERLOT, a metadata registry for open educational resources.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Michelle Brennan from ISKME describes the work they've done around incorporating LRMI metadata into OER Commons.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Thor Prichard of Clarity Innovations discusses the work his team is doing around building private sandbox nodes on the Learning Registry for content developers to test the tagging, publishing searching lifecycle.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Maggie Jacobs from the New York Public Library discusses their plans for using LRMI to organize their educational resources much like the Smithsonian.
From the Education Metadata Meetup on 7/30/14 in Washington DC - Melissa Wadman and Darren Milligan talk about how the Smithsonian is using LRMI to combat the current chaotic organizational state of its educational resources.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Pearson's Marlowe Johnson discusses how the conversation around metadata within Pearson has changed, and how they use metadata to support their strategic focus on efficacy.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - David Grandison from BrainPOP discusses why and how they've implemented LRMI metadata and the results they've seen since, including an increase in Google referrals from 12% to 40%.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Knovation's Steve Nordmark discusses what he and his team has learned over the past 14 years they've spent contextualizing educational resources.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Brian Ausland from Navigation North explains how the tools they are building around Learning Registry technology will help users publish/manage to the registry and access/customize data as it's pulled out.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Elizabeth Neuman from the Wisconsin Department of Public Instruction explains her state's plans for building a digital learning portal for all Wisconsin school districts.
ISLE Open Education Resources Enabling Open Access and Integration | Educatio...AAP PreK-12 Learning Group
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Jeanne Kitchens discusses how Illinois has taken advantage of the LRMI and Learning Reigstry in building their Illinois Shared Learning Environment.
From the Education Metadata Meetup on 7/30/14 in Washington, DC - Randy Reina from Intel discusses the importance of standards for fostering a successful education marketplace.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
1. Common Education Data
Standards
Content in Context Metadata Lab
June 2012
Jack Buckley
Commissioner,
National Center for Education Statistics
2. Here’s a Hmmm…
new student: Did you mean:
Matthe Matthew ?
SmithIII Smith ?
Race = Guamanian Suffix = III ?
Gender = M Race = NHOPI ?
Sex = M ?
3. What is CEDS?
• A national collaborative effort to develop
voluntary, common data standards for a key
set of education data elements
• A vocabulary including standard
definitions, option sets & technical
specifications to streamline sharing and
comparing
Voluntary Common Vocabulary
4. Why do we need CEDS?
1. Accurate, timely, and consistent data
to inform decisionmaking
2. Share&compare high quality data within
& across P-20 sectors
5. CEDS is Not:
Required
All or nothing
A data collection
An implementation
Solely an ED undertaking
A federal unit record system
6. How do we get it done?
• Assemble stakeholders representing the field
• Use existing sources of standards
• Check alignment with the field
• Review ideas with the public
• Model elements
• Place in tools
• Release
7. Version 1
• Released in September, 2010
• 161 elements – focused on K-12
– Student record exchange across
districts/States
– Student transcripts
– High school feedback reports from
postsecondary to K-12
8. Version 2
• Released in January, 2012
• Expansion of elements and stakeholders
• Added early learning
• Changing focus of postsecondary beyond
K12 transitions
• Massive growth of K-12 elements
• Development of Logical Data Model
• Tools: Data Alignment Policy Questions
9. CEDS v2 Stakeholders (1 of 2)
• State Agencies
o State Education Agencies
o State Higher Education Agencies
o Social Services Agencies
• Local Education Agencies
o K12
o Head Start
o Social Services
• Institutions of Higher Education
o Public
o Private
o Community Colleges
10. CEDS v2 Stakeholders (2 of 2)
• U.S. Department of Education
o NCES (SLDS, IPEDS) o Financial Student Aid
o EDFacts o Office of the Undersecretary
o Office of Educ. Technology o Special Education
• U.S. Health and Human Services
• U.S. Department of Labor
• Interoperability Standard Organizations
• Education Associations
• Foundations
11.
12.
13. Standard Information: The Basics
Element
Definition
Hispanic
or Latino Option set
Ethnicity
Yes
No
NotSelected
Domain K12
Entity K12 Student
Related Use Cases
14.
15. CEDS Logical Data Model
• Provides a high-level framework for
translating standards into physical models
• System-agnostic representation
• 2 distinct views:
• Domain Entity Schema
• Normalized Data Schema
17. CEDS Align
•Web-based tool that allows users to:
• Import or input their data dictionaries
• Align their data to CEDS
• Compare themselves with others
• Analyze their data in
relation to various other
CEDS-aligned efforts
18. CEDS Connect (Upcoming)
Builds on the CEDS Align tool and allows
stakeholders to:
• Generate specific and relevant maps to
a growing pool of CEDS connections
19. Version 3 Content Areas
• Early Learning
• K12
• Postsecondary
• Workforce
• Career and Technical
Education
• Adult Education
• Race to the Top Assessments
20. K12 Development Work
• Teaching and Learning – Formative
Assessments
• Supporting Teaching and Learning
Initiatives
• Portable Student Records
• RTTA Consortia Support
21. Unpacking the model What data
elements and
structures need to
be added to
What process CEDS?
measures should Formative
become CEDS Assessment
elements?
Formative
Feedback
What additional
measured output data elements
-
error Instructional
measureddescribe learning
+ Decisions Related content
Learner
reference progressions, etc.?
metadata to
Learner
Standards Adjusted: Competencies
include, or not
Learning Progressions Instruction, (less variable)
Current Learning Goals (for CEDS v3)?
Criteria for Success Activities,
Curriculum, Unit & Lesson Plans Practice
Activities, Resources
(variable inputs)
22. Race to the Top Assessments
• Assessment Interoperability Framework (AIF)
• Identify Changes to Existing CEDS/Develop
CEDS Assessment Data Elements
• Beyond Data Elements and Data Model
• AIF Best Practice Guidance
• Prototype Demonstration
• Integrate into CEDS, including updated data
model and tools
[K12-Postsecondary version]The high school may be trying to communicate information about a student to a university out of state. Even the most basic information about a student can be misunderstood without a common vocabulary (or standard).
So what is CEDS? CEDS is a national collaborative effort to develop a voluntary, common vocabulary. <click> It provides standards for a key set of education data elements: names, definitions, option sets, technical specifications, and more. <click> Simply put, it is a Voluntary Common Vocabulary for education data. <click>
Why do we need this common vocabulary? We all know educator and policymakers need accurate, timely and consistent information to inform decisionmaking. <click> We also need to share and compare these data across our P-20 sectors.While many data standards have been used in the field for decades, there has not emerged a universal language that can serve basic information needs across all sites, levels, and sectors throughout the P-20 environment. By identifying, compiling, and building consensus around the basic, commonly used elements across P-20, CEDS meets this critical need.
There are misconceptions that pop up on any project of this size so let’s be clear on what CEDS is not --Required – CEDS is a voluntary vocabulary-All or nothing – there are many different use cases that CEDS covers and not all elements have to be utilized to find benefits-A data collection – this tends to be our biggest misconception, that CEDS is a giant federal data collect, I assure you, it is not-An implementation – there is no one implementation that will work for every user. CEDS will not provide physical implementations. We will leave that up to those in the field and assume we will see many different implementations form.-Solely an ED undertaking – NCES is developing these standards with a group of stakeholders and with several public review cycles (more on our stakeholders is coming)-A federal unit record system – I know I already explained CEDS is not a data collection. We have found it doesn’t hurt to repeat that this is not a student-record system, collection, or anything like that.
This is a very high level look at some of the major steps in our process. We got some important folks together, looked at the work already done, made sure we mapped up with the field, made our ideas public, modeled the elements to see if they worked telling our story, put our information in tool and then released. (This is a transition to next slides summarizing stakeholder groups.)
We are very pleased by the breadth and depth of the folks we have working on this project with us. We have folks representing the entire P-20 spectrum on our stakeholder group. What is interesting is the different representation we have even within SEAs, LEAs, and IHEs. [Read list…]
Note – federal staff from outside USED
This is a view of the CEDS website’s home page, ceds.ed.gov, from which you can access the CEDS elements, models, tools, and more.
CEDS can now be viewed and interacted with in three key ways: -By element: Via the CEDS Elements page, users can access a searchable glossary of the CEDS "vocabulary," including names, definitions, option sets, technical specifications, use cases, and more. -By relationship: Through the CEDS Data Model, users can explore the relationships that exist among entities and elements—viewable both through a Domain Entity Schema and a Normalized Data Schema. -By comparison: Supplemental tools, including the CEDS Alignment Tool enable users to take the next step and put CEDS into practice.
In CEDS, a standard is comprised of several pieces of information that provide context for and describe the data items:Elements, including name and definitionOption sets, including name and definitionRelated entitiesRelated use casesAlternative names and other notesHere you see the component parts of a standard -- in this case, for the element “Hispanic or Latino Ethnicity.” On the right side, which is shows the Element Details as provided on the CEDS website, you can see the element name, definition, and option set are provided. In addition, the Domain and Related Entities provide context for how this particular element is commonly used in a data model or data system. In addition, CEDS provides a collection of related use cases.The left side of the slide represents the relationship graphically, with the entity Student in the center, next to the element along with the option set.
What is the CEDS Data Model? The CEDS Data Model presents logical view of the standards, its a system-agnostic representation that contains attributes, shows cardinality, and uses the commonly-used names for all entities. When planning to build or modify a database or implementation to align with CEDS, the Data Model provides a high-level framework to translate common entities, elements and relationships into physical models for a specific database platform that addresses the indexing, performance optimization, and normalization or denormalization appropriate for the specific application addressing local information needs.
The ERD, along with XML Schema, is another one of the ways the data model is being expressed. <click> It provides a “snapshot” or “picture” of what the data model looks like. Users can navigate between the entities, or major “buckets,” and readily identify their relationships with other components within the data model.
We do most of our work in three main sub-groups: early learning, K12 and postsecondary. This year we will also be expanding into workforce, CTE, adult education, and race to the top assessments.
Let me highlight some of the K12 work going on.1. Teaching and Learning - Formative AssessmentsBuilding on the work of othersProviding context2. Supporting Teaching and Learning InitiativesCCSS IdentifiersLRMISLC/SLI3. Portable Student Records – supportinginteroperabilty work for our mobile student population.
Here is a quick view of how we are approaching formative assessments. This work is being done directly with our school district stakeholders. We are looking at different parts of this process and what elements are needed to support each one.
Finally, we have just started some work to support the Race to the Top Assessments…
Finally, we have just started some work to support the Race to the Top Assessments…