Introduction to E-Learning Technologies - Prof. dr. Paul De Bra Dr. ir. Natalia Stash
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
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 programs http://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 thesisAdaptive 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 TechnologiesIdeal 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 ScienceAdaptive 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 Image Text Step 2 Image Text ...
Image Text Step N+1
Step N
...
TextExample Created with GALE:
Course about MilkyWay
/ Department of Mathematics and Computer ScienceExample Created with GALE:
World of Biomes
/ Department of Mathematics and Computer ScienceExample Created with GALE:
World of Biomes
/ Department of Mathematics and Computer ScienceExample Created with GALE:
World of Biomes
/ Department of Mathematics and Computer ScienceExample Created with GALE:
Latex Tutorial
/ Department of Mathematics and Computer ScienceExample 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 ScienceExample:
Milkyway Information Structure
Milkyway Eta Carinae
• The “logical” structure
Parent
of Milkyway Produces
Nebula Star
• Different relationship
types result in links in Rotates Around
the application Planet Sun
Is Planet Of
Earth Mars
Is Moon Of
Moon_Earth Phobos
Deimos
MoonExample:
Milkyway Pedagogical Structure
Milkyway Eta Carinae
• Adaptation can be
Parent
supported by Artificial Produces
Intelligence Nebula Star
Rotates Around
Planet Sun
Is Planet Of
Earth Mars
Is Moon Of
Moon_Earth Phobos
Deimos
MoonAuthoring Adaptive Application: Domain Model
Authoring Adaptive Application:
Application Content/XHTML Page Using GALE Syntax
Authoring AHA! Applications
Creating AHA! applications involves four main steps:
writing pages,
creating a conceptual
structure of the application,
defining the adaptation rules
(event-condition-action rules)
, and defining the look and
feel, or layout of the application.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 beforeConclusions: 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 domainUse 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 e-learning application - teaching about Art
Use of Semantic Technologies in E-Learning:
Ontologies Mapping
Ontology C:
Ontology A: Ontology B:
#Rembrandt
#Rembrandt
#Rembrandt van Rein
Harmensz. van Rein
ULAN vocabulary
(Union List of Artists Names)
http://www.getty.edu/vocabularies/ulan#500011051Use of Semantic Technologies in E-Learning:
Reasoning Example
North-
part of part of
Holland
(stored) (stored)
The
Amsterdam
Netherlands
part of
(deduced)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
Mobile
Art Recommender Tour Wizard Museum Guide
Artworks and Concepts Managing and Visualizing Guiding the user
Recommendations Museum Tours inside the museum
Interactive User ModelingYou can also read
