Introduction to E-Learning Technologies

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 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

Parent

Milkyway

Eta Carinae

Produces

Star

Nebula

Rotates Around

Sun

Planet

Is Planet Of

Mars

Earth Is Moon Of Moon_Earth

Phobos Deimos

Moon

Example: Milkyway Pedagogical Structure • Adaptation can be supported by Artificial Intelligence

Parent

Milkyway

Eta Carinae

Produces

Star

Nebula

Rotates Around

Sun

Planet

Is Planet Of

Mars

Earth Is Moon Of Moon_Earth

Phobos Deimos

Moon

Authoring 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 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 e-learning 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)

NorthHolland

Amsterdam part of (deduced)

part of (stored)

The Netherlands

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