Linked Data Competency Index : Mapping the field for teachers and learners
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The Linked Data Competency Index (LDCI) offers a structured overview of competencies and benchmarks related to linked data practices and technologies for teachers and learners. It includes various topics such as querying RDF data, RDF vocabularies, and creating linked data applications, organized into competencies that gauge knowledge and skills. The index serves to enhance learning by enabling users to tailor courses and curricula based on their individual goals, and is a collaborative effort that can adapt over time.
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 2017-10-11 AIMS Webinar 6 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) 2017-10-11 AIMS Webinar 8 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. 2017-10-11 AIMS Webinar 11 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?
- 14. 2017-10-11 AIMS Webinar 14 Example: YouTube video tagged using LDCI
- 15. Example: YouTube video tagged using LDCI 2017-10-11 AIMS Webinar 15
- 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 2017-10-11 AIMS Webinar 18
- 19. Users can propose new competencies 2017-10-11 AIMS Webinar 19
- 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! 2017-10-11 AIMS Webinar 22