In this presentation, we will introduce you to a solution that involves adaptive semantic technology for educational institutions and e-learning providers. You will learn how to integrate 3rd party resources, legacy assets, and other content sources to create the so-called knowledge graph of all structured and unstructured data.
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Gaining Advantage in e-Learning with Semantic Adaptive Technology
1. 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
2. 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
3. Impelsys & Ontotext: Partnership
Publishing x Technology | Content x Semantics
Introduction1
4. 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
5. iPublishCentral – Global Reach
Millions
Of B2B
Users
Students
Instructors
Professionals
15,000
LIBRARIES
Million+
B2C Users
LIVE
PORTALS
100+
TITLES
250,000
Global
Customer Presence
7. 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 premise
New York Sofia London Frankfurt
8. 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
9. 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 whitepaper
Graph Databases, April 2015
http://www.bloorresearch.com/technology/graph-databases/
13. Adaptive Learning
Adaptive learning is an educational method to orchestrate the
allocation of mediated resources according to the unique needs of
each learner.
19. 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
29. • 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
30. • 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
31. 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.
36. 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
37. 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 & Thesauri
Semantic 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 content
Quality 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