linked data competency index : mapping the field for teachers and learners

Linked Data Competency Index:Mapping the field for teachers and learners

Thomas Baker

Dublin Core Metadata Initiative

AIMS Webinar11 October 2017

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

http://explore.dublincore.net/

“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


• 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.


Linked Data Competency IndexExample


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.


LD4PE Competency IndexExample topic

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


6 topic clusters30 topics95 competencies


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.


Linked Data Competency Index

Competencies and benchmarks

Competencies•Understands•Knows•Recognizes•Differentiates ...

understanding (learning)

Benchmarks•Uses•Expresses•Demonstrates•Distills•Converts ...

doing (exam questions, homework assignments)


Linked Data Competency Index

Understanding / Doing

• 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.



Provide context

• 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.



What LDCI tries to cover

• 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.


LD4PE Competency IndexWhat it does not cover

• 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...


LD4PE Competency IndexWhat is a competency index used for?

620 resources describedhttp://explore.dublincore.net/explore-learning-resources-by-competency/


Example: YouTube video tagged using LDCI

https://dcmi.github.io/ldci/D2695955/



Linked Data Competency Index in Chinesehttps://dcmi.github.io/ldci-zh/D2695955-zh/

Crowdsourcing LDCI maintenance


Users can propose new competencies


• 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.



Who can use it?

• 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



Learning tailored to the individual

LDCI is work in progress!Follow us on Github!


linked data competency index : mapping the field for teachers and learners

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