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Introduction to
E-Learning Technologies
Prof. dr. Paul De Bra
Dr. ir. Natalia Stash
Presentation Outline
• What is & Why E-learning?
➡
• E-learning technologies: where?
@ Information
Systems
Section
of Mathematics
& Computer
Science Dpt.
@ TU/e
About Me
Russia, Saint-Petersburg State Electro-Technical
University, 1999
•Engineer diploma
Development of the distance learning server for
studying of artificial intelligence
Russia, Saint-Petersburg, 1999-2001
•Projects
– IDLE (Intelligent Distance Learning Environment)
– Distance Learning Course on Business Planning
About Me
The Netherlands, TU/e, 2007
•PhD thesis
Incorporating Cognitive/Learning Styles
in a General-Purpose Adaptive Hypermedia System
The Netherlands, TU/e, 2001 until now
•Projects
– AHA! (Adaptive Hypermedia for All)
– GRAPPLE (Generic Responsive Adaptive Personal
Learning Environment)
– CHIP (Cultural Heritage Information Personalization)
What is E-Learning?/
Some History Facts
• Distance education dates to at least as early as 1728,
when the Boston Gazette printed an advertisement
from Caleb Phillips, teacher of the new method of
Short Hand, that offered
“… any persons in the country desirous to learn this
Art, may, by having the several lessons sent
weekly to them, be as perfectly instructed as
those that live in Boston.”
What is E-Learning?/
Generations of Distance Education
• 1840s: Stenographic Short Hand by
Isaac Pitman,
through written & print materials
• End of 1960s: Multimedia distance education
through print materials,
radio and TV broadcasting, cassettes,
computers e.g. PLATO (computer system)
Q: What is missing in these forms of distance education?
What is E-Learning?/
Generations of Distance Education
• 1970s: Computer-mediated communication
through email, teleconferences,
growing use of Internet
Why E-Learning?/
Online Course vs Textbook
• physical
• local
• uniform
Why E-Learning?/
Online Course vs Textbook
• paperless
• global
• possibly adaptive
Popular Tools for E-Learning:
Learning Management Systems
WebCT (Course Tools) or
Blackboard Learning System, now
owned by Blackboard Inc.
Universities Worldwide Offering E-Learning
Top universities offering e-learning:
• The Open University, UK
• University of British Columbia, Canada
• Pennsylvania State University, USA
• University of Florida, USA
• Walden University, USA
• to add more ...
Dutch Universities
Offering E-Learning
• Open University
http://www.ou.nl
• Collaboration between
www.elevatehealth.eu
• Fontys University of Applied Sciences
http://www.fontys.nl
E-Learning @ TU/e
• CoffeeDregs: Visualizing
programshttp://wiki.netbeans.org/CoffeeDregs
• DiagNow - an interactive lecture-response system
• Escom-Method a multimedia guide through a
knowledge area
• peach³ - for assignments evaluation
peach3.nl
E-Learning @ Information Systems Section,
Mathematics & CS Dpt., TU/e
• Specialization in Adaptive E-learning
• Software for developing adaptive applications:
– AHA! (Adaptive Hypermedia Architecture)
– GALE (Generic Adaptive Learning Environment)
The first adaptive online thesis
Adaptive E-Learning &
Adaptive Web-Based Applications in General @IS
• IS scope is not limited to e-learning:
– AHA! and GALE are general-purpose tools for
various types of adaptive applications
– CHIP demonstrator - personalized museum guide
based on Semantic Web Technologies
Ideal Adaptive Teaching
Master - Apprentice −>
Tutor - Student (Learner)
One Size Does Not Fit All
Adaptive E-Learning
• Consider adaptation to:
knowledge
goals & tasks
cognitive/learning styles
/ Department of Mathematics and Computer Science
Adaptive E-Learning
• Most commonly used types of relationships between
course topics:
– knowledge propagation
– prerequisites
– A is a prerequisite for B, means:
• You should study A before B or
• You should study A before you can understand B
Example Created with AHA!:
Presentation for Visual+Global Learner
Example Created with AHA!:
Presentation for Verbal+Analytic Learner
Example Created with AHA!:
Inferring Preference for Image vs Text
Step 1
Step N
Image
Text
Image
Step 2
Text
Image
Text
...
Step N+1
...
Text
Example Created with GALE:
Course about MilkyWay
/ Department of Mathematics and Computer Science
Example Created with GALE:
World of Biomes
/ Department of Mathematics and Computer Science
Example Created with GALE:
World of Biomes
/ Department of Mathematics and Computer Science
Example Created with GALE:
World of Biomes
/ Department of Mathematics and Computer Science
Example Created with GALE:
Latex Tutorial
/ Department of Mathematics and Computer Science
Example Created with GALE:
Research Methods Book
Example:
C# Online Tutorial
Example:
C# Online Tutorial
Example:
C# Online Tutorial
Example:
Course About Tennis
Designing an Online Adaptive Course
Consists of:
1. Designing information structure (domain model)
2. Information creation (application content)
3.Defining navigation structure (application content)
4. Defining pedagogical structure (adaptation model):
Which navigation is desirable during learning?
•An adaptive system such as AHA! or GALE should
support navigation
/ Department of Computer Science
Example:
Milkyway Information Structure
• The “logical” structure
of Milkyway
• Different relationship
types result in links in
the application
Example: Milkyway Pedagogical Structure
• Adaptation can be
supported by Artificial
Intelligence
Authoring Adaptive Application:
Domain Model
Authoring Adaptive Application:
Application Content/XHTML Page Using GALE Syntax
<h2>Authoring AHA! Applications</h2>
<p>Creating AHA! applications involves four main steps:
writing <gale:a href="pages">pages</gale:a>,
creating a <gale:a href="domainmodel">conceptual
structure</gale:a> of the application,
defining the <gale:a href="rules">adaptation rules</gale:a>
<gale:if expr="${rules#knowledge}&gt;50">
<gale:block>
<gale:a href="rules"> (event-condition-action rules)</gale:a>
</gale:block>
</gale:if>, and defining the <gale:a href="layout">look and
feel, or layout</gale:a> of the application.</p>
Authoring Adaptive Application:
Adaptation Model
Prerequisites Structure
• Different ways of creating prerequisites structure:
– Top-down (deductive) approach
– from abstract to concrete concepts
– Bottom-up (inductive) approach
– from concrete to abstract concepts
• Adaptive systems allow for the implementation of
different prerequisites structures for one course to
address individual learner’s preferences
How to Realize Prerequisites
• You should study A before B
– “should”: different adaptation techniques either
enforce this or give advice
– “study”: this can be access or read or take a test;
result is a knowledge value
– how much knowledge is enough?
– “before”: this does not imply just before
Conclusions:
Applications Developed With AHA! and GALE
• Adaptation to address individual differences
• Adaptation through prerequisites
• Various prerequisite structures are possible for the
same course
• Time estimation for authoring an adaptive online
course (TU/e master students who followed
Adaptive Systems course) - 10 to 25 hours
Use of Semantic Technologies in E-Learning
• In software, semantic technology encodes
meanings separately from data and
content files, and separately from
application code
• In computer science and information
science, an ontology formally represents
knowledge as a set of concepts within a
domain, and the relationships among
those concepts. It can be used to reason
about the entities within that domain and
may be used to describe the domain
Use of Semantic Technologies in E-Learning
• Previous examples do not (yet) make use of semantic
structures defined in ontologies
==> amount of reasoning over semantic structures is
limited
• Next, we will see how to make adaptive applications
based on semantic structures from CHIP project
Although CHIP prototype is a recommender system, a
personalized museum guide, it can also be seen as an elearning application - teaching about Art
Use of Semantic Technologies in E-Learning:
Ontologies Mapping
Ontology A:
#Rembrandt
Ontology B:
#Rembrandt van Rein
Ontology C:
#Rembrandt
Harmensz. van Rein
ULAN vocabulary
(Union List of Artists Names)
http://www.getty.edu/vocabularies/ulan#500011051
Use of Semantic Technologies in E-Learning:
Reasoning Example
part of
(stored)
Amsterd
am
NorthHolland
part of
(deduced)
part of
(stored)
The
Netherland
s
Cultural Heritage Information Personalization,
CHIP: a Personalized Museum Guide
• Developed in collaboration between
TU/e, Telematica Institute and Rijksmuseum
• Personalized access through multiple devices
– PC, iPhone, etc.
• Recommendations are based on:
– metadata about artworks & art topics
• http://www.chip-project.org
CHIP Data
CHIP Web-Based Tools
Art Recommender
Artworks and Concepts
Recommendations
Tour Wizard
Managing and Visualizing
Museum Tours
Interactive User Modeling
Mobile
Museum Guide
Guiding the user
inside the museum