22 October 2012

Learning Maps, Common IDs and the Common Core

Today we presented at the iNACOL VSS conference on "Learning Maps, Common IDs and the Common Core. Here are primary resources associated with that presentation:
Update: 7:55pm: In addition to the above primary sources, I've written the following on the same subject:

Also I corrected the link to LearningRegistry.org.

Many thanks to the panelists: Maureen Wentworth, Michael Jay and Sharren Bates.

18 October 2012

Things Every Education Tech Entrepreneur Should Know

This weekend I'm volunteering as a coach for Startup Weekend Edu in Seattle. Preparing for this got me to thinking about things people building education technology should know. The following list isn't comprehensive but it's a good starting point. Follow the links to learn more about these topics.

Theories of Change
You need to have a good theory of how your technology will improve education. There's a lot of money to be made in record keeping and ERP-type applications. But the things that interest me and I hope interest you are those that directly improve student learning. And you need to be specific about the expected improvement. Do you want students to learn more in the same amount of time or take less time to learn a skill? Are you seeking better comprehension and retention? What about "deeper learning" – getting beyond recall and demonstrating the ability to apply concepts or solve problems.

Most ed tech theories of change start with Bloom's Two Sigma Problem. In a 1984 paper, Benjamin Bloom discussed how they had achieved two standard deviations improvement in student learning through a combination of Mastery Learning and one-on-one tutoring. Noting that 1:1 student-teacher ratios are impractical, Bloom's challenge is to find scalable ways to achieve the same results.

The following resources should stimulate your theoretical juices:
  • A 2011 Metastudy by Kurt VanLehn gives a progress report of Intelligent Tutoring Systems and an update on progress toward Bloom's Two Sigma Problem. In particular, see page 210 (the 15th of the paper) in which VanLehn explains that about half of Bloom's two sigma gains were due to changes in Mastery Learning parameters.
  • Personalized Learning is "instruction that is paced to learning needs, tailored to learning preferences, and tailored to the specific interests of different learners. In an environment that is fully personalized, the learning objectives and content as well as the method and pace may all vary." This definition is from the National Education Technology Plan which is an excellent read so long as you skip the executive summary.
  • Cognitive scientists talk about the Zone of Proximal Development. Game designers talk about Gameplay Progression. They are similar concepts and they both involve motivation and increasing skill levels. In fact, the motivational reward from this form of gameplay is achievement of greater skill.
  • Feedback loops are an essential component of Personalized Learning. (From an earlier post in this blog.)
  • The Puzzle of Motivation: Dan Pink explains the growing science of motivation without which, even the best instruction may fail.
Building Blocks
A number of organizations including the federal government, technology standards groups, associations and foundations have assembled building blocks to support innovative education technology. Some of these can improve time-to-delivery, some help interoperability between applications and some ensure that your application is based on tested learning theories:
  • The Personalized Learning Model is a framework that some of us at the Gates Foundation have used to talk about how key components in a learning system work together. It's very similar to frameworks used by others in the community.
  • The Learning Resource Metadata Initiative is a metadata schema for identifying learning resources (text, video, virtual labs, assessments, etc.) and aligning them to education standards like the Common Core.
  • The Learning Registry is a system for sharing metadata about learning resources. It's synergistic with LRMI and other metadata formats.
  • MyData Button is a federal government initiative to allow students or their parents to download their student data so that it can be used by other systems.
  • The Postsecondary Electronic Standards Council (PESC) defines data models and protocols for exchanging data among postsecondary institutions. PESC standards cover admissions applications, test score reporting, student aid applications and reporting, digital transcripts and more.
  • IMS Global defines educational content standards (where SIF and PESC concentrate on student and institutional data). IMS standards like QTI and Common Cartridge define how to package assessment items and courseware for exchange between systems. My favorite IMS standard is Learning Tools Interoperability which is a protocol that allows rich, custom learning tools to be integrated into other learning environments.
  • Ed-Fi is a data model and set of tools to support teacher and student dashboards indicating student progress.
  • The Shared Learning Collaborative (SLC) "is an alliance of states, foundations, educators, content providers, developers and vendors who are passionate about using technology to improve education." It's an ambitious multistate project that leverages many of the technologies listed above into a coherent whole. Vendor outreach programs are at dev.slcedu.org

Product and Service Categories
There are a handful of existing education technology product and service categories with new ones emerging. Here are key categories with some examples. Note that the examples I've listed just happen to be well-known systems. It's far from a comprehensive list and I don't necessarily endorse these products. In each category there are emerging products that may be more innovative than the ones I name.
  • Learning Management Systems (LMS) manage class interactions such as syllabus, assignments, learning materials, quizzes, forums, gradebook and so forth. While LMSs are capable of delivering a rich online learning experience, most deployments are supplementary to conventional classroom learning and only a fraction of their capabilities are used. Well-known examples include BlackBoard, Desire2Learn, Moodle, eCollege, Sakai, BrainHoney and Canvas but there are numerous others.
  • Instructional Improvement Systems are an emerging concept. Like an LMS, an IIS manages student learning. However, an IIS uses accumulated student data as well as effectiveness data about learning resources to customize the learning experience to individual student needs. To support continuous improvement, the IIS should place equal emphasis on data collection and data use. Most action in the IIS space is being driven by state-level RFPs often with Race to the Top funding.
  • Public Education Datasets are available from the National Center for Education Statistics and other federal and state education agencies. The Digest of Education Statistics is a compilation of many government and privately-sourced datasets. Other public datasets include EdFacts and IPEDS. There some interesting opportunities to consuming existing public data and analyzing it in new ways.
There's much more that could be added but I think I've reached the point of diminishing returns. Please use the comments to point at other important theories, building blocks or initiatives.

12 October 2012

Tips For Using the Common Core XML

The Common Core State Standards (CCSS) have been out for a couple of years now and adoption efforts are progressing. To facilitate use of the CCSS in learning applications and with metadata frameworks like LRMI, they have recently posted canonical identifiers and machine-readable XML for the standards. I wrote about that in a recent post.

Update: 1 September 2014
The CCSSO has updated the CoreStandards.org website and most of the links in this post no longer work. However, the Common Core XML files still exist and can be found with the developer information here. Sometime in the near future, I'll re-write this post to describe the new provisions that the CCSSO has made for the techie crowd.

Today I'm going into the nuts and bolts of how a developer can make use of the XML. There are some useful features that aren't obvious at first glance. For background, I recommend that you read the announcement memo that accompanied the release of the XML on the corestandards.org website.

Canonical Identifiers

The canonical identifiers for the common core state standards are available in .csv form here: http://corestandards.org/assets/E0607_ccss_identifiers.csv. The first column lists the URLs that were formerly on the Corestandards.org website. They are included to support conversions for legacy applications.

Notable is there is an exact 1:1 mapping between the three ID types and there are 1844 IDs in the table. So if two applications are using different forms of IDs (e.g. one uses GUIDs and another uses URIs) the translation is a deterministic table lookup. A closer examination will show that there's a simple algorithmic conversion between the "dot notation" identifiers and the corresponding URIs. I've written functions in c# to do the translation and posted them here. It should be easy to port them to Java or any other language.


The standards for Mathematics and ELA/Literacy follow different hierarchies that are suited to the way the standards are written and are intended to be used. The Dot Notation and URL forms of the identifiers can be parsed into the corresponding hierarchies as shown in the following examples.

Math Example

Dot Notation: CCSS.Math.Content.HSA-SSE.A.1b
URL: http://corestandards.org/Math/Content/HSA/SSE/A/1/b

InitiativeCCSS(Common Core State Standards)
SetContent(Options are 'Content' and 'Practice')
GradeHSA(High School Algebra)
DomainSSE(Seeing Structure in Expressions)

You can reference the math standards at the Component, Standard and Cluster levels. Thus, the following are all valid CCSS URI Identifiers:
If you add an ".xml" suffix to the URL then you get the computer-readable XML version of each:
The XML at the cluster and standard levels includes all child items. So the cluster includes all standards in that cluster and all components within that standard.

While the canonical IDs don't include Domain or Grade levels, you can retrieve all standards within a domain or grade by hacking the URL as follows:
And, you can retrieve all of the math standards in one XML document with this URL:

ELA/Literacy Example

Dot Notation: CCSS.ELA-Literacy.W.9-10.3d
URL: http://www.corestandards.org/ELA-Literacy/W/9-10/3/d

InitiativeCCSS(Common Core State Standards)
(Optional, not used in this example)

You can reference the literacy standards at the Component, Standard and Grade levels. Thus, the following are all valid CCSS URI Identifiers:
If you add an ".xml" suffix to the URL then you get the computer-readable XML version of each:
And, you can retrieve all of the ELA/Literacy standards in one XML document with this URL:

04 October 2012

CEDS and the Four-Layer Framework for Data Standards - Updated

About a year ago I posted a Four-Layer Framework for Data Standards. It was developed as Common Education Data Standards (CEDS) working groups were discussing the space in which CEDS operates and what makes its contribution unique. Today I'm updating the framework document – adding some clarity but mostly reconciling terminology with that used by CEDS.

In the June CEDS stakeholders' meeting the group emphasized that CEDS works strictly at layers 1 and 2 (Data Dictionary and Logical Data Model) leaving serialization and protocol to other standards organizations. This leads a unique approach (at least unique to the education standards space) in which the focus is on alignment instead of compliance.

To support this strategy, CEDS has posted the Align and Connect tools. The Align tool allows State Education Agencies, software vendors and other organizations to post their data models and show how their elements align to CEDS. Organizations can choose to make their data models public; in which case Align can be used to report the degree of alignment between two data models. The new Connect tool addresses the sharing of metric definitions like graduation rate, student financial aid repayment or college-going rate. Metrics like these are not in the data model, they are derived from that data. And different organizations may combine the data in different ways. Connect supports the sharing and eventual standardization of these metric definitions.

Another question I've gotten is how the four-layer framework overlaps with the OSI 7-layer model. Layers 1-3 (Data Dictionary, Logical Data Model and Serialization) in the four layer model map to the Application layer (layer 7) which is at the top of the OSI model. All other layers in OSI are combined into the Protocol layer in the four-layer model.

The latest four-layer document is here. It's released into the public domain under a CC0 disclaimer.