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