1. Introduction to E-Learning Technologies Prof. dr. Paul De Bra Dr. ir. Natalia Stash

2. Presentation Outline What is & Why E-learning? ➡ @ Information Systems Section of Mathematics & Computer Science Dpt. @ TU/e E-learning technologies: where?

3. 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

4. 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)

5. 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 offeredShort Hand “… 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.”

6. 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?

7. What is E-Learning?/ Generations of Distance Education 1970s: Computer-mediated communication through email, teleconferences, growing use of Internet

8. Why E-Learning?/ Online Course vs Textbook physical local uniform

9. Why E-Learning?/ Online Course vs Textbook paperless global possibly adaptive

10. Popular Tools for E-Learning: Learning Management Systems WebCT (Course Tools) or Blackboard Learning System, now owned by Blackboard Inc.

11. 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...

12. 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

13. E-Learning @ TU/e CoffeeDregs: Visualizing programshttp://wiki.netbeans.org/CoffeeDregshttp://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

14. 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

15. 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

16. Ideal Adaptive Teaching Master - Apprentice −> Tutor - Student (Learner)

17. One Size Does Not Fit All

18. Adaptive E-Learning / Department of Mathematics and Computer Science Consider adaptation to: knowledge goals & tasks cognitive/learning styles

19. 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

20. Example Created with AHA!: Presentation for Visual+Global Learner

21. Example Created with AHA!: Presentation for Verbal+Analytic Learner

22. Example Created with AHA!: Inferring Preference for Image vs Text

23. ImageText ImageText ImageText Step 1Step 2 Step N Text Step N+1...

24. Example Created with GALE: Course about MilkyWay / Department of Mathematics and Computer Science

25. Example Created with GALE: World of Biomes / Department of Mathematics and Computer Science

26. Example Created with GALE: World of Biomes / Department of Mathematics and Computer Science

27. Example Created with GALE: World of Biomes / Department of Mathematics and Computer Science

28. Example Created with GALE: Latex Tutorial / Department of Mathematics and Computer Science

29. Example Created with GALE: Research Methods Book

30. Example: C# Online Tutorial

33. Example: Course About Tennis

34. 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

35. Example: Milkyway Information Structure The “logical” structure of Milkyway Different relationship types result in links in the application

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

37. Authoring Adaptive Application: Domain Model

38. 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.

39. Authoring Adaptive Application: Adaptation Model

40. 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

41. 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

42. 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

43. Use of Semantic Technologies in E-Learning In software, semantic technology encodes meanings separately from data and content files, and separately from application codesoftware 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 domaincomputer scienceinformation sciencedomainreason

44. 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

45. 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

46. Use of Semantic Technologies in E-Learning: Reasoning Example Amsterda m North- Holland The Netherlands part of (stored) part of (deduced) part of (stored)

47. 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

48. CHIP Data

49. CHIP Web-Based Tools Art RecommenderTour Wizard Mobile Museum Guide Artworks and Concepts Recommendations Managing and Visualizing Museum Tours Guiding the user inside the museum Interactive User Modeling