The IMLS-funded project Linked Data for Professional Education (LD4PE) has created a "Competency Index for Linked Data". The Index provides a concise and readable map of concepts and skills related to the practices and technologies of Linked Data for the benefit of interested learners and their teachers.

1. Linked Data Competency Index:
Mapping the field for teachers and learners
Thomas Baker
Dublin Core Metadata Initiative
AIMS Webinar
11 October 2017

2. The Linked Data Competency Index provides:
•a concise and readable map of concepts and skills
•related to practices and technologies of Linked Data
•for benefit of interested learners (and teachers).
Created by LD4PE Project, http://explore.dublincore.net, with generous
funding from the Institute of Museum and Library Services (IMLS).
2017-10-11 AIMS Webinar 2

3. “Competency Index”
A thematic set of competencies organized by
•Topic
– Competency: a tweet-length phrase about knowledge or
skills that can be learned
• Benchmark: an action that demonstrates accomplishment in a given
competency
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4. • Topic: Querying RDF Data
– Competency: Understands that a SPARQL query matches an RDF graph
against a pattern of triples with fixed and variable values
– Competency: Understands the basic syntax of a SPARQL query
• Benchmark: Uses angle brackets for delimiting URIs.
• Benchmark: Uses question marks for indicating variables.
• Benchmark: Uses PREFIX for base URIs.
2017-10-11 AIMS Webinar 4
Linked Data Competency Index
Example

5. • Topic: Querying RDF Data
– Competency: Understands that a SPARQL query matches an RDF graph
against a pattern of triples with fixed and variable values
– Competency: Understands the basic syntax of a SPARQL query
• Benchmark:Uses angle brackets for delimiting URIs.
• Benchmark: Uses question marks for indicating variables.
• Benchmark: Uses PREFIX for base URIs.
2017-10-11 AIMS Webinar 5
LD4PE Competency Index
Example topic

6. LD4PE Competency Index
Overview of topics
• Fundamentals of Resource Description
Framework
• Identity in RDF
• RDF data model
• Related data models
• RDF serialization
• Fundamentals of Linked Data
• Web technology
• Linked data principles
• Linked Data policies and best practices
• Non-RDF Linked Data
• RDF vocabularies and application profiles
• Finding RDF-based vocabularies
• Designing RDF-based vocabularies
• Maintaining RDF vocabularies
• Versioning RDF vocabularies
• Publishing RDF vocabularies
• Mapping RDF vocabularies
• RDF application profiles
• Creating and transforming RDF Data
• Managing identifiers (URIs)
• Creating RDF data
• Versioning RDF data
• RDF data provenance
• Cleaning and reconciling RDF data
• Mapping and enriching RDF data
• Interacting with RDF Data
• Finding RDF Data
• Processing RDF data using programming languages
• Querying RDF Data
• Visualizing RDF Data
• Reasoning over RDF data
• Assessing RDF data quality
• RDF Data analytics
• Manipulating RDF Data
• Creating Linked Data applications
• Storing RDF data
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6 topic clusters
30 topics
95 competencies

7. • Topic: Querying RDF Data
– Competency: Understands that a SPARQL query matches an RDF graph
against a pattern of triples with fixed and variable values
– Competency: Knows the basic syntax of a SPARQL query
• Benchmark: Uses angle brackets for delimiting URIs.
• Benchmark: Uses question marks for indicating variables.
• Benchmark: Uses PREFIX for base URIs.
2017-10-11 AIMS Webinar 7
Linked Data Competency Index
Competencies and benchmarks

8. Competencies
•Understands
•Knows
•Recognizes
•Differentiates ...
understanding (learning)
Benchmarks
•Uses
•Expresses
•Demonstrates
•Distills
•Converts ...
doing (exam questions,
homework assignments)
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Linked Data Competency Index
Understanding / Doing

9. • Competency: Knows Web Ontology Language, or OWL (2004), an RDF
vocabulary of properties and classes that extend support for expressive data
modeling and automated inferencing (reasoning).
• Competency: Knows that the word “ontology” is ambiguous, referring to any
RDF vocabulary, but more typically a set of OWL classes and properties
designed to support inferencing in a specific domain.
Ideally, spells out acronyms and provides context to give non-expert readers a
rough idea what they mean.
2017-10-11 AIMS Webinar 9
LD4PE Competency Index
Provide context

10. • Enough topics to convey a map of the domain
• Enough detail on domain competency
Other competency indexes make other design choices, e.g., to
support exams or ceritifcation.
2017-10-11 AIMS Webinar 10
LD4PE Competency Index
What LDCI tries to cover

11. • NOT: Levels of difficulty
– “Basic” for a library scientist may be “difficult” for a
computer scientist (and vice versa)
• NOT: Ranking or ordering topics
– for the same reasons
Competencies are building blocks that can be assembled into
different courses or curricula.
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LD4PE Competency Index
What it does not cover

12. • Describe what a learner can learn.
• Describe skills that demonstrate understanding (e.g.,
homework, quizzes, exams...).
• Basis for:
– job descriptions
– course syllabi
– university degrees
– micro-credentials
– digital badges
• Tag descriptions of learning resources...
2017-10-11 AIMS Webinar 12
LD4PE Competency Index
What is a competency index used for?

13. 620 resources described
http://explore.dublincore.net/explore-learning-resources-by-competency/

14. 2017-10-11 AIMS Webinar 14
Example: YouTube video tagged using LDCI

15. Example: YouTube video tagged using LDCI
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16. https://dcmi.github.io/ldci/D2695955/
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17. 2017-10-11 AIMS Webinar 17
Linked Data Competency Index in Chinese
https://dcmi.github.io/ldci-zh/D2695955-zh/

18. Crowdsourcing LDCI maintenance
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19. Users can propose new competencies
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20. • Students: help choose courses that cover what you want to
learn.
• Instructors: design a course, syllabus, homework, quizzes,
exams.
• Employers: write a job description.
• Self-learners: explore technologies and methods related to
Linked Data.
2017-10-11 AIMS Webinar 20
LD4PE Competency Index
Who can use it?

21. • Since 1800s: “industrial” classroom:
– instructors lecture (“sage on the stage”)
– students listen and take notes
– achievement measured by a grade on the exam
• Trend: learning tailored to the individual:
– students watch the lectures online before class
– students pursue customized learning objectives
– instructors give individualized help (“guide at the side”)
– learners learn at own pace
– life-long learning
– achievement measured in competencies acquired
2017-10-11 AIMS Webinar 21
LD4PE Competency Index
Learning tailored to the individual

22. LDCI is work in progress!
Follow us on Github!
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