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Gaining Advantage in e-Learning with Semantic Adaptive Technology
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Transcript of Gaining Advantage in e-Learning with Semantic Adaptive Technology
Ontotext – Impelsys Webinar Series
END-TO-END SMART PUBLISHING AND E-LEARNING
GAINING ADVANTAGE IN E-LEARNING WITH SEMANTIC ADAPTIVE TECHNOLOGY
THURSDAY 28 JULY | 11AM EDT | 4PM BST | 6PM EEST
July 2016
We will talk about…
Introduction About Impelsys and Ontotext
Adaptive Semantic Solution Adaptive Semantic Platform Use cases Demonstrations Adaptive Semantic Solution – Production Process Questions & Answers
Impelsys & Ontotext: PartnershipPublishing x Technology | Content x Semantics
Introduction1
About Impelsys
15 YEARS
100% PUBLISHING &EDUCATION FOCUS
350+
EMPLOYEES New York HEAD QUARTERS
• Digital Product Development• Content Delivery Solution –
iPublishCentral• Authoring & Editorial Workflows • Mobility & Bespoke solutions• DRM & Analytics
Bangalore
R&D
• Global team, local sales & accounts support
• Innovation Hub & Global Delivery Center at Bangalore
• Technology partners• Cutting-edge infrastructure on Amazon &
Rackspace
New York Bangalore London SFO
iPublishCentral – Global Reach
Millions Of B2BUsersStudents
InstructorsProfessionals
15,000LIBRARIES
Million+B2C Users
LIVE PORTALS
100+
TITLES250,000
GlobalCustomer Presence
Supporting Content Delivery For Global Brands
About Ontotext
16 YEARS
100% SEM.TECH. FOCUS
350+
EMPLOYEES Sofia HEAD QUARTERS
• Semantic graph database engine combined with Content management solutions
• Interlinking text and data to unveil meaning
• Delivering unmatched search and exploration
Sofia R&D
• Global team, local sales & accounts support
• R&D Center at Sofia, Bulgaria• Serving BBC, FT, Wiley, Oxford UP,
IET, …• SaaS infrastructure on Amazon and
on premiseNew York Sofia London Frankfurt
Ontotext Capabilities
Integrate proprietary databases and taxonomies with Linked Data Infer facts and relationships
Interlink text and with big data Better content analytics, retrieval and
recommendation
Positioning in Graph DBs
“Despite all of this attention the market is dominated by Neo4J and OntoText (GraphDB), which are graph and RDF database providers respectively. These are the longest established vendors in this space (both founded in 2000) so they have a longevity and experience that other suppliers cannot yet match. How long this will remain the case remains to be seen.”Bloor Group whitepaperGraph Databases, April 2015http://www.bloorresearch.com/technology/graph-databases/
Ontotext Clients (selection)
Major financial Information agency
Major business and legalInformation agency
Why Impelsys & Ontotext
Impelsys
Ontotext
Semantic publishing and
eLearning technology
platform
Semantic enrichment and
personalized recommendatio
ns
Graph database, data and
knowledge representation
Authoring solution
Content transformation &
SMEs
Content & e-learning delivery
Offer semantically enriched solutions to publishers and e-learning providers E-Learning Authoring & Editorial workflows Semantic Content Enrichment, Knowledge
Graph management, Thesauri and Ontology management, Linked Open Data integration
Transformation services/Content authoring and editorial outsourcing
Delivery, personalization and recommendation solutions
Together Impelsys’ iPublishCentral/publishing BPO and Ontotext’s Semantic Publishing Platform bring end-to-end semantic publishing and content editing/transformation services to the market
Personalized learning for effective and efficient learning outcome
Adaptive Semantic Solution3
Adaptive LearningAdaptive learning is an educational method to orchestrate the allocation of mediated resources according to the unique needs of each learner.
Typical Courseware
Adaptive Courseware
Presentation of Concepts – Typical Courseware
Presentation of Concepts – Adaptive Courseware
Adaptive Technology Architectures
Traditional Approach
Impelsys Approac
h
Value Proposition Traditional server based Adaptive system is:
Costly Complex to implement Not flexible
SemTech powered Adaptive Technology is: Inexpensive Simple to implement Flexible Platform independent
Adaptive Semantic Platform2eLearning vertical
Dynamic Added Value
Adaptive Semantic Platform
API stack
Mapping Across Curricula
Mapping Content and Curricula: Details
Adaptive Semantic Technology
Adaptive Semantic Technology: Details
Use cases4
• Goals− Better management and
enrichment of e-learning content− Improved reuse of legacy content− Increase user engagement
• Challenges− Content locked only for specific
products instead of being enriched and reused for development of dynamic content offerings
• Approach− Semantic enrichment of learning
objects across different subjects and product lines
− Smarter search and contextual recommendations of relevant learning objects
Use case 1: Global Educational Publisher
• Goals− Improved and more efficient vocabulary
management− Metadata enrichment of all available assets− Efficient search and relevant recommendations− Automatic association of assets to curricula
• Challenges− Lack of integration between the different systems
of the customer− A lot of manual operations on metadata
enrichment and association of asset to curricula
• Approach− Knowledge Base development, responsible for
managing vocabularies, curricula, ontologies, assets metadata
− Semantic enrichment of metadata− Semantic recommendation engine
Use case 2: Global Provider of Multimedia Assets for Educational Publishers
Use case 3: RCNi Learning (Royal College of Nursing)
Requirement • Learning management platform to deliver
learning modules to practicing nurses and nursing students.
• Platform to help practicing nurses meet their continuing professional development (CPD) requirements.
• Course modules to be developed from existing RCNi journals.
Impelsys Approach• iPublishCentral Learn platform with
administrator, instructor and student access.
• Dedicated native mobile apps for anytime, anywhere access.
• SMEs’ (Subject Matter Experts), cognitive scientists and instructional designers to convert journals to learning modules.
• Adopted semantic technology to automate
courseware development process.
Demonstrations5
Demo 1: Impelsys Adaptive Content
Demo 2: BBC Wildlife Portal
Production process6
Production Process SMEs and IDs analyze the subject/ topic, identify
Concepts and prepare the Courseware Prepare different levels of concepts (normal,
medium, and detailed) Specify different kinds of content (textual, A/V,
simulation, etc.) Prepare Pre-test, topic level tests and transition
rules Transition rules are created as a special language
interpreted by Adaptive Engine
Analyze Atomize & Enrich Reprocess Package, Test
& Deploy
Analyze- Assets (text, A/V,
Images, Simulations)- Learning Objects- Topics- Assessments- Metadata and taxonomy
/ ontology analysis- Data consolidation
analysis
Chunking & data modelling- Breakdown into smaller
LOs (Nodes)- Assign weights to Nodes- Create concept-wise
mini quizzes- Associate Nodes with
quizzes- Identify Node transition
paths & conditions- Ontology & ThesauriSemantic enrichment of content- Repackaging of content
(eg. Text with images, etc)
- Automatic tagging of LOs
Quality assurance- Verify Atomized Content
by SMEs and Customer- Verify data model and
semantic enrichment
Reprocess- Create pre-test to
measure learner’s initial knowledge level and learning reference Create instrumentation at each Node (using xAPI or TINCAN)
- Define rich LOs in the knowledge graph
- Specify transition rules for each node
- Create initial Learning Path using Instruction Design and Pedagogic principles
Quality assurance- Verify transition rules
with SMEs and teachers / trainers
Package- Create UI- Package as per SCORM
or plain HTML5/ JavaScript
Test- Test UI transitions- Verify contentQuality Check- Verify Adaptive Course
with SMEs and teachers / trainers
- Verify UX and Adaptive Course with pilot user groups
Non-Adaptive Course
Adaptive Course
Production Process - Detailed
Analyze Atomize & Enrich Reprocess Package, Test
& Deploy
2-3 weeks 1-1,5 months 3-4 weeks 1-2 weeks
Non-Adaptive Course
Adaptive Course
Production Process - Timeframe
QUESTIONS?
July 2016