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.
Linked Data Competency Index : Mapping the field for teachers and learners
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
2017-10-11 AIMS Webinar 3
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
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...
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LD4PE Competency Index
What is a competency index used for?
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.
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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
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LD4PE Competency Index
Learning tailored to the individual
22. LDCI is work in progress!
Follow us on Github!
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