Briefing Paper #4, LD4PE Overview, describes the IMLS-funded Linked Data for Professional Education (LD4PE) project that is developing a competency-based prototype referatory of learning resources for teaching and learning Linked Data practices in design, implementation, and management. The Briefing describes the project’s milestones including the RFD-based competency framework that will drive the indexing of Linked Data learning resource descriptions to the knowledge, skills, and habits of mind those resources address.

The briefing describes the nature of the competency framework and the crowd-sourcing of expertise being used in its development. In addition, the Briefing discusses the toolkit of AngularJS editors under development for both creating of the competency framework and the learning resource descriptions.

Briefing URL: https://ld4pe.dublincore.org/briefing-papers/ld4peoverview/

LD4PE Overview—Briefing #4

URL:

https://ld4pe.dublincore.org/briefing-papers/ld4peoverview/

Creator:

Stuart A. Sutton (Information School, University of Washington)

Last Update:

2015-08-01

Licensed under a Creative Commons Attribution 4.0 International License.

This Briefing Paper provides an overview of the IMLS-funded Linked Data for Professional Education (LD4PE) project to develop a competency-based prototype referatory of learning resources for teaching and learning Linked Data practices in design, implementation, and management.

1. Introduction

Understanding Linked Data standards and practices has become a key requirement for information professionals in galleries, libraries, archives and museums (GLAM). Major national libraries and bibliographic services are leading the trend to: (1) publish authority files, catalogs, datasets, and bibliographic standards as Linked Data; and (2) gather Linked Data from diverse external sources such as dbpedia, Freebase and MusicBrainz to enhance their local discovery and retrieval systems. Broad initiatives such as LODLAM and OpenGLAM are pushing the boundaries of Linked Data integration across galleries, libraries, archives and museums.

Professionals in cultural memory institutions find themselves on shaky ground as this seismic paradigm shift pushes the need for competent professionals from national centers toward local institutions. Indeed, the challenge to professional preparedness goes deeper, also affecting the teachers of the next generation of professionals and the trainers who provide continuing professional development.

This urgent need to develop Linked Data competencies in the professional workforce is driving major initiatives to provide learning resources (LR) about the underlying standards and data model—for example, the EU’s Euclid project, LOD2, School for Data, PlanetData, Open Data Institute, Lean Semantic Web, Cloudera, GATE, Linked Data Cookbook, and Cookbook for Translating Data Models to RDF Schemas. The products of these initiatives range from curricular structures and full courses to simple “recipes”—brief packages of “how-to” videos and step-by-step instructions targeted to specific (frequently unarticulated) learning outcomes. These scattered initiatives and their resources can be easy to find by those who already know what they are looking for, but everyone else struggles to put the available resources into a context.

The LD4PE project is developing the Exploring Linked Data website to help alleviate this struggle through support for the structured discovery of the LR available online through both open and commercial providers. At the heart of the website will be a competency framework for Linked Data practice that supports indexing learning resources according to the specific competencies they address. To accomplish this goal, Exploring Linked Data is leveraging Linked Data technology by modeling in RDF and assigning global identifiers (URIs) to statements of competence, and then using those URIs to map LR metadata to nodes in the Index. Deliverables of the LD4PE project include:

1. Competency Framework. RDF-modeled “Competency Index for Linked Data” (Index) based on the ASN Description Language (ASN-DL) for describing formally promulgated competencies and benchmarks;
2. Toolkit. An openly available, web-based tool set to support the: (a) generation of RDF metadata describing learning resources; and (b) creation of learner trajectory maps expressing curricular structures or personal learning journeys superimposed over the competency framework;
3. Learning Resource Descriptions. A set of cataloged LR that have been mapped to the competencies and benchmarks of the Index to support competency-based resource discovery by teachers, trainers and learners.
4. Explore Website. A website to be managed by DCMI as part of its educational agenda for open discovery of competency-based learning resources, access to the toolkit, learner maps, and supporting resources; and
5. Best Practices. Readily accessible best practice documentation for all processes, from community-based competency framework development and LR description through learner trajectory creation.

2. Competency Index

Competency frameworks defining what a learner should know and be able to demonstrate underpin labor market credentialing mechanisms including college/university degrees, educational certificates, industrial certification, occupational licenses and even emerging micro-credentialing through digital badging. Learning environments strive for a tight coupling between these controlling frameworks, the resources necessary to achieve the learning objectives embodied in the frameworks, and what is actually assessed in terms of learner competence. [ 6 ]

2a. Index Development

Government and quasi-governmental agencies, commercial entities and professional organizations promulgate most of these competency frameworks under tight editorial control by a select body of experts. In contrast to these highly constrained contexts, the LD4PE is developing the means for crowd sourcing expertise in both the development and formative assessment of the project’s competency framework with synthesis and finalization of the published framework in the hands of a small, volunteer Editorial Board.

Projects such as schema.org are demonstrating that useful outcomes can be achieved through forms of crowd sourcing when technology enabled version control system—e.g., Github—help democratize the process of generation through broad community input.

Figure 1 illustrates the various components in the LD4PE competency-creation process. Discovery and Description, through its quest to discover, describe and map LR descriptions to competencies, provides a continuous environmental analysis of the evolving coverage of the Index in terms of:

1. orphan competencies—competencies found in LR that cannot be mapped to the Index; and
2. misaligned granularity—imprecise mapping options where there is a mismatch between granularity levels of Index competencies and those found in an LR.

Figure 1. Crowd sourcing expertise for the Index.

Linked Data Expert engagement through both solicited and unsolicited proposals for new competencies provides a mechanism through a version-controlled submission process for subsequent review and synthesis into the evolving Index by the Editorial Board.

The outcome of this process is a community-developed Index that describes a set of learning objectives and outcomes in terms of relevant knowledge, skills, practices, and habits of mind necessary to learn successful Linked Data practice from design and modeling through implementation and maintenance.

2b. Index modeling

The Index data is modeled as an RDF graph using the Achievements Standards Network Description Language (ASN-DL). The ASN-DL is comprised of two resource classes, the asn:StandardDocument representing the competency framework being described as a whole, and the asn:Statement representing the individual component competency assertions of the document. Object properties are used to replicate the original hierarchical or graph modeling of the canonical document while an evolving array of object properties express the semantic relationship between individual assertions.

The ASN-DL is extensible through addition of new properties and subproperty refinements to support domain specific profiles. Value vocabulary namespaces relevant to the jurisdiction of the competency framework may be specified and encoded as RDF using the W3C’s Simple Knowledge Organization System (SKOS).

Figure 2. Example Index ASN-DF modeling

The ASN-DL is extensible through addition of new properties and subproperty refinements to support domain specific profiles. Value vocabulary namespaces relevant to the jurisdiction of the competency framework may be specified and encoded as RDF using the W3C’s Simple Knowledge Organization System (SKOS). To date, LD4PE has not needed to define any project-specific value vocabularies.

2.c Index scope

One of the major challenges during the early months of the process has been operationalizing the term “Linked Data” in such a way that the scope of both the developing Index and the LR to be described and mapped are tractable within the context of the 2-year grant. “Linked Data” denotes a method for publishing structured data on the web that builds on: (1) a set of open standards and technologies to support semantic queries and graph traversal such as HTTP, URI/IRI, RDF, RDFa, SPARQL, triplestores, OWL, SKOS and others, and (2) the four practices first set out by Sir Tim Berners-Lee—use URIs as names for things; use HTTP URIs so that people can look up those names; When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL); include links to other URIs so that they can discover more things. [ 1 ] While the scope of the Index and the LR descriptions to be included in Exploring Lined Data could extend across the full substrate of specific areas of competence supporting practice, such a broad scope is untenable in terms of a single competency framework. The project continues to struggle with this scoping issue.

3. Toolkit

The LD4PE project is committed to development using open standards, and, where appropriate, adaptions of existing tools and toolsets—implementing anew only where necessary. The LD4PE toolkit will be a set of open tools that enable Exploring Linked Data. Key areas of project implementation are light-weight, browser-based editors for creation of LR descriptions, competency descriptions and a mapping mechanism for creating learner trajectories.

3a. Metadata Generation Tools

To support the generation of metadata describing LR and for authoring the Index and its competency assertions, the project is developing an open toolkit of browser-based editors implemented using AngularJS for: (a) RDF LR descriptions based on an application profile of the LRMI (schema.org) schema, and (b) Index metadata based on the ASN Description Language (ASN-DL) for competency framework modeling and description.

3b. Learner Trajectories Tool

While the Index defines a set of competencies, it neither prescribes any competencies as “core” nor defines a logical sequencing of its components. In other words, the Index does not, in itself, define a curriculum by prescribing a single learning trajectory or pathway through the set of competencies expressed as nodes in the Index graph. The LD4PE project will customize existing tools to enable teachers, trainers, and learners to map their own pathways through the Index graph. These pathways can be saved as named graphs that traverse, or overlay, the competency nodes of the Index.

The named graphs for learning trajectories or pathways may be identified as formal curriculum structures or as personalized trajectories created by instructors or learners as evidence of progress. They may also function as suggested paths forward in the learning process or as guides to other instructors or learners in creating their own trajectories.

Figure 3. Learning trajectory maps.

Several decades of research has shown that such maps reveal the macrostructure of the body of information or knowledge within a field, making the context of that information or knowledge more apparent and useful to learners (O’Donnell et al., 2002; Hall & O’Donnell, 1996; Hall & O’Donnell, 2010). [ 3 ], [ 4 ] & [ 5 ] Similarly, learning trajectory overlays contextualize competencies and learning outcomes in a “larger picture” of learning expectations, outcomes, and personal learner progressions.

Thus, LD4PE stands in sharp contrast to other projects in not prescribing a single curricular point of view but in providing instead the means for instructors, trainers and learners to chart multiple, diverse pathways for learning—pathways defined as public or private, individual or collective, prescribed or exploratory. Each named graph provides a different roadmap for discovering and traversing lesson plans, how-to recipes, webinars, and tutorials that have been described and aligned to the competency nodes of the Index graph.

4. Architecture

The Exploring Linked Data technical architecture consists of a WordPress public interface on top of a triplestore managing LR descriptions and learning trajectories. The Index will be publicly available through the ASN-D2L open repository, and reflected through WordPress as a custom taxonomy.

Figure 4. Exploring Linked Data architecture.

The toolkit comprised of the editing and learner trajectory tools will function as open, stand-alone applications that can be used independent of the WordPress instance and the triplestore. While the initial goal is to serve as a referatory, some LD4PE project partners will be creating LR to be hosted by Exploring Linked Data. Video-based LR will be streamed from publicly accessible services such as YouTube, SlideShare and Vimeo.

The Exploring Linked Data website will primarily provide competency-based access to learning resources. In addition, it will provide a publication venue for both learning trajectory maps and select LR such as a pilot set being created by project partners Sungkyunkwan University Institute of Information and Management (Korea), OCLC, Elsevier, Synaptica, and Access Innovations. Developed and managed by the LD4PE Project, the website will mature to become part of the Dublin Core Metadata Initiative’s freely available toolset. WordPress out-of-the-box social media functions, including broadcast and recommender systems, will foster the engagement of domain experts, teachers and trainers, learners, and resource providers.

5. Best Practice Documentation

Publicly available guidelines will be published through the Exploring Linked Data website covering best practices in:

1. Community development and management of competency frameworks;
2. Creation of useful metadata descriptions of LR using the LRMI application profile;
3. Alignment of LR to relevant competencies; and
4. Creation of LR targeted to specific competencies.

6. Conclusion

While LD4PE will focus on skills, knowledge, and professional practice in the area of Linked Data, nothing precludes the extension of its competency-based approach to other areas of LIS, archives, and museum curricula. Similar competency frameworks could be developed describing practice in knowledge organization systems, cataloging, and organizational management.

This approach will be appropriate wherever the goal is for learners to achieve competence within a defined set of knowledge, skills, and habits of mind. DCMI is committed to sustaining the products and assets of this work moving forward because it sees the project’s conceptualization and its outcomes (including best practice documentation related to the framework itself) as generalizable to all areas of principled metadata design and best practice, while also providing a guiding template for future development of DCMI’s education and training agenda.

At the close of the IMLS-funded project in late 2016, management of Exploring Linked Data with fall to DCMI’s Education & Outreach Committee and a standing Task Group (currently called the LD4PE Task Group). Under this leadership, the content of Exploring Linked Data will extend beyond the prototype’s focus on Linked Data to other areas of professional competency-based education and training in metadata design, implementation, and use.

Acknowledgements

The work described in this poster is partially funded by the Institute of Museum and Library Services (IMLS).

References

[1] Berners-Lee, Tim (2006). Design Issues: Linked Data. Retrieved June 28, 2015 from http://www.w3.org/DesignIssues/LinkedData.html.

[2] Crandall, M.D., Tennis, J., Sutton, S.A., Baker, T. & Talley, D. (2013). Planning a platform for learning linked data. In Moen, W. & Rushing, A. (Eds.), International Conference on Dublin Core and Metadata Applications. Retrieved June 28, 2015 from http://dcpapers.dublincore.org/pubs/article/view/3693/1916.

[3] Hall, R.H. & O’Donnell, A. (1996). Cognitive and affective outcomes of learning from knowledge maps. Contemp. Educ. Psychol. 21, 94-101.

[4] Hall, R.H., Hall, M.A. & Saling, C.B. (2010). The Effects of graphical postorganization strategies on learning from knowledge maps. J. Exp. Educ. 67(2):101-112 (DOI:10.1080/00220979909598347).

[5] O’Donnell, A.M., Dansereau, D.F. & Hall, R.H. (2002). Knowledge maps as scaffolds for cognitive processing. Educ. Psychol. Review 14, 71-85.

[6] Sutton, S. A. & Golder, D. (2008). Achievement Standards Network (ASN): An application profile for mapping K-12 educational resources to achievement standards. In Heike Neuroth (Ed.), Proceedings of the International Conference on Dublin Core and Metadata Applications, (pp. 69-79). Retrieved, June 28, 2015 from http://dcpapers.dublincore.org/ojs/pubs/article/view/920/916.

[7] Ward, N. & Nicholas, N. (2010). Benefits of machine readable curricula. Retrieved, June 28, 2015 from http://bit.ly/CurriculaBenefits.

ASN-DL Overview—Briefing #1

URL:

https://ld4pe.dublincore.org/asn-briefing-1/

Editors:

Joseph Chapmann (Desire2Learn)

Stuart A. Sutton (Information School, University of Washington)

Last Update:

2015-07-01

Licensed under a Creative Commons Attribution 4.0 International License.

Basic ASN Architecture

The ASN is made up of two fundamental entities: (1) standards documents, and (2) statements. ASN takes each standards document as it is produced by its official promulgating agency and “atomizes” its content into atomic statements. These two entities—documents and statements—are framed in terms of an entity-relationship model (ER) and embodied in RDF/XML (Resource Description Framework). Both structural and semantic relationships between the ASN’s primary entities—the standards document entity and its atomic statement entities—have been defined.

The following diagram illustrates the use of the document and statement entities in expressing a single standard in the Ohio 2001 mathematics standard. The relationships between the entities in the diagram are hierarchical and are represented by the arrows in the diagram.

Note that each entity has been assigned a unique identifier in the form of a Uniform Resource Identifier (URI).

The diagram above illustrates what is called an ASN Taxon Path which is defined as a single traversal of a branch of a standards document–here from the root (i.e., the standards document description) to a leaf (i.e., a statement at some arbitrary level in the branch hierarchy).  Just as the atomizing of the text de-contextualizes the standard document’s member statements for unique identification and reference, the notion of the ASN Taxon Path re-contextualizes those statements.  This re-contextualization is fundamental to human comprehension of the standards since many atomic statements, standing alone, convey insufficient meaning.  As a result, all ASN services return full ASN Taxon Paths.

The following figure illustrates another ASN Taxon Path.

For the purpose of illustrating the notion of the ASN entities and relationships, the content of both of the ASN Taxon Paths illustrated above has been kept at a minimum.  In fact, metadata describing additional attributes (e.g., subjects, grade levels, etc.) of both the standard document and statement entities are provided.  Documentation of the ASN Application Profile is available. 

Resolving (Dereferencing) an ASN URI

All ASN URIs can be resolved or dereferenced by humans or applications.  The dereferencing of an ASN URI returns the full upward path of the ASN Taxon Path in which the entity identified by the URI is a member.  Thus, dereferencing the ASN URI  http://purl.org/ASN/resources/S1024B7C found in the Ohio mathematics example above returns an RDF/XML description of the full Taxon Path as illustrated including the text of its member statements.

If the statement URI being referenced is an entity further up in the hierarchical path from the literal leaf, dereferencing would treat the URI being dereferenced as the leaf entity in the Taxon Path and return the upward path.  The Taxon Path in such a case would not include statements downward in the path from the dereferenced entity.

The dereferencing of an ASN URI is managed by the ASN URI Resolver.  The ASN URI Resolver is the simplest service offered by the ASN and requires no special knowledge on the part of humans or applications to handle dereferencing.

Cross-Jurisdiction Mapping (Intermediary Services)

Intermediaries in the context of K-12 content standards are services that handle the mapping of an ASN state standards statement URI to a statement URI from another state.  In other words, it is a service that draws a more or less rough equivalency relationship between standards statements in two separate standards documents.  Those documents may be two versions of a single state’s standards or documents from two entirely different states.  Intermediaries make it possible to fulfill the following sorts of end-user needs:

1. I have an ASN URI for a standard from Nebraska, is there an equivalent standards statement in the Arizona standards?
2. I have retrieved a very useful lesson plan that has been correlated to a Nevada standard but I live in California.  What California standard might this lesson plan help me fulfill?
3. I have retrieved a very useful lesson plan correlated to a Nevada standard using an ASN URI and I’d like to see whether there are other similar lesson plans I might use to meet my Nevada standard but which have been correlated to standards from other states using ASN URIs.  Are there any such lesson plans?

While there are a number of existing and emerging intermediaries based on different theoretical and technical premises, they all basically function as illustrated in the following figure:

In the yellow ASN band across the figure, we see illustrative standards statements from Texas, California and New York.  The mapping choices between the state standards are two-fold: (1) a service could  do a one-to-many mapping from each state statement to its equivalent in each of the other state standards documents—a tedious, impractical process; or (2) a single set of all encompassing expressions (an intermediary) might be created that capture the content of all of the state standards that make it possible to map each state statement once to the intermediary and then to use that intermediary as a “switching language.”  All intermediaries pursue some form of the second choice.

The ASN supports the design and development of any number of 3rd party intermediaries based on different principles and technologies.

Extending Granularity of an ASN Taxon Path

For some service providers consuming and using the ASN state standards, the level of granularity of leaf statements may not be sufficiently granular to meet all service purposes.  For example, the following statement from an Ohio standard might be further atomized into sixteen additional, more granular statements based on the cognitive process (analyze, solve, verify, and interpret) and the task (addition, subtraction, multiplication and division):

“Analyze and solve multi-step problems involving addition, subtraction, multiplication and division using an organized approach, and verify and interpret results with respect to the original problem.” [http://purl.org/ASN/resources/S1024B7C]

The ASN makes it possible to increase the granularity of expression in a standards document by distinguishing between canonical (original) statements as promulgated by the standards bodies and non-canonical (derived) statements added by 3^rd parties.  Derived 3rd party ASN statements “refine” original statements by making more specific, granular assertions. Since derived statements are treated as first-class entities in the ASN, they are assigned URIs in the same manner as original statements and clearly identified as such.  Of course, derived statements can be easily eliminate, not display, or treated in some manner as simple annotations by services consuming the standards documents.

As a result of this extensibility, any authorized 3^rd party may directly create more granular statements in the ASN or without authorization in their own namespace and relate those statements to the canonical (original) statements using the ASN-identified structural relations in the ER model.

While these 3^rd party derived statements may be included in the ASN repository, nothing precludes a service from creating its own namespace to handle its refinements of canonical ASN statements and exposing those refinements (or not) to the Web community.

The following figure illustrates this 3rd party “annotation” processes.

Example uses of the ASN refinement capability might include:

• Testing services creating assessment instruments that target only parts of canonical statements; or
• A school district wishing to refine its state’s ASN standards expressed in grade bands by breaking out facets of particular official statements by individual grades while still maintaining the semantic association with the state statement.

Expressing “Strength of Fit”

Frequently, it is useful to map from one resource to another to express the level of useful similarity between the two nodes. Such mappings may be from one competency framework node to another node in the same or another framework (i.e., competency-to-competency), or to map from a learning resource to a competency node (i.e., learning resource-to-competency). In ASN, the utility of that mapping is a function of its “strength of fit”. The ASN ontology provides an array of properties that express this strength of fit relationship in competency-to-competency mappings (called “alignments” in ASN) and learning resource-to-competency mappings (called “correlations” in ASN).

The table below includes brief descriptions of the current competency-to-competency mapping predicates. For the full description of both the competency-to-competency mapping properties and the learning resource-to-competency mapping properties, see Briefing Paper #2: ASN Ontology.

Relationship	Definition	Illustration
Align To	A target competency to which the described competency is aligned. Note: An “alignment” is a general assertion of some degree of unidentified equivalency between the two resources being aligned. (Inverse of Aligned From)
Align From	A target competency to which the described competency is aligned. Note: An “alignment” is a general assertion of some degree of unidentified equivalency between the two resources being aligned. (Inverse of Aligned To)
Broad Alignment	The target competency covers all of the relevant concepts in the competency being described as well as relevant concepts not found in the competency being described.
Exact Alignment	The relevant concepts in both the described and target competencies are coextensive.
Major Alignment	Major overlap of relevant concepts between the described and target competencies.
Minor Alignment	Minor overlap of relevant concepts between the described and target competencies.
Narrow Alignment	The competency being described covers all of the relevant concepts in the target competency as well as relevant concepts not found in the target competency.
Prerequisite Alignment	The target competency is a prerequisite to the competency being described.