2. Adaptive Systems for E-Learning Peter Brusilovsky School of Information Sciences University of Pittsburgh, USA peterb@sis.pitt.edu http://www2.sis.pitt.edu/~peterb

3. Overview The Context Technologies Web impact for adaptive educational systems Adaptive educational systems and E-Learning Challenges for adaptive educational systems

4. Overview The Context Technologies Web impact for adaptive educational systems Adaptive educational systems and E-Learning Challenges for adaptive educational systems

5. The Context Adaptive systems Why adaptive? Adaptive vs. intelligent

6. Adaptive systems Classic loop user modeling - adaptation in adaptive systems

7. Adaptive software systems Intelligent Tutoring Systems –adaptive course sequencing –adaptive... Adaptive Hypermedia Systems –adaptive presentation –adaptive navigation support Adaptive Help Systems Adaptive...

8. Why AWBES? greater diversity of users –“user centered” systems may not work new “unprepared” users –traditional systems are too complicated users are “alone” –limited help from a peer or a teacher

9. Intelligent vs. Adaptive 1. Intelligent but not adaptive (no student model!) 2. Adaptive but not really intelligent 3. Intelligent and adaptive Intelligent ES Adaptive ES 2 3 1

10. Overview The Context Technologies Web impact for adaptive educational systems Adaptive educational systems and E-Learning Challenges for adaptive educational systems

11. Technologies Origins of AWBES technologies ITS Technologies AH Technologies Web-Inspired Technologies

12. Origins of AWBES Technologies Intelligent Tutoring Systems Adaptive Hypermedia Systems Adaptive Web-based Educational Systems

13. Origins of AWBES Technologies Adaptive Hypermedia Systems Intelligent Tutoring Systems Adaptive Hypermedia Intelligent Tutoring Adaptive Presentation Adaptive Navigation Support Curriculum Sequencing Intelligent Solution Analysis Problem Solving Support

14. Origins of AIWBES Technologies Adaptive Hypermedia Systems Intelligent Tutoring Systems Information Retrieval Adaptive Hypermedia Adaptive Information Filtering Intelligent Monitoring Intelligent Collaborative Learning Intelligent Tutoring Machine Learning, Data Mining CSCL

15. Technology inheritance examples Intelligent Tutoring Systems (since 1970) –CALAT (CAIRNE, NTT) –PAT-ONLINE (PAT, Carnegie Mellon) Adaptive Hypermedia Systems (since 1990) –AHA (Adaptive Hypertext Course, Eindhoven) –KBS-HyperBook (KB Hypertext, Hannover) ITS and AHS –ELM-ART (ELM-PE, Trier, ISIS-Tutor, MSU)

16. Inherited Technologies Intelligent Tutoring Systems –course sequencing –intelligent analysis of problem solutions –interactive problem solving support –example-based problem solving Adaptive Hypermedia Systems –adaptive presentation –adaptive navigation support

17. Course Sequencing Oldest ITS technology –SCHOLAR, BIP, GCAI... Goal: individualized “best” sequence of educational activities –information to read –examples to explore –problems to solve... Curriculum sequencing, instructional planning,...

18. Sequencing with models Given the state of UM and the current goal pick up the best topic or ULM within a subset of relevant ones (defined by links) Special cases with multi-topic indexing and several kinds of ULM Applying explicit pedagogical strategy to sequencing

19. Intelligent problem solving support The “main duty” of ITS From diagnosis to problem solving support High-interactive technologies –interactive problem solving support Low-interactive technologies –intelligent analysis of problem solutions –example-based problem solving

20. High-interactive support Classic System: Lisp-Tutor The “ultimate goal” of many ITS developers Support on every step of problem solving –Coach-style intervention –Highlight wrong step –Immediate feedback –Goal posting –Several levels of help by request

21. Example: PAT-Online

22. Low-interactive technologies Intelligent analysis of problem solutions –Classic system: PROUST –Support: Identifying bugs for remediation and positive help –Works after the (partial) solution is completed Example-based problem solving support –Classic system: ELM-PE –Works before the solution is completed

23. Example: ELM-ART

24. Problem-solving support Important for WBE –problem solving is a key to understanding –lack of problem solving help Hardest technology to implement –research issue –implementation issue Excellent student modeling capability!

25. Adaptive hypermedia Hypermedia systems = Pages + Links Adaptive presentation –content adaptation Adaptive navigation support –link adaptation

26. Adaptive navigation support Direct guidance Hiding, restricting, disabling Generation Sorting Annotation Map adaptation

27. Adaptive annotation: Icons Annotations for topic states in Manuel Excell: not seen (white lens) ; partially seen (grey lens) ; and completed (black lens)

28. Adaptive annotation: Font color Annotations for concept states in ISIS-Tutor: not ready (neutral); ready and new (red); seen (green); and learned (green+)

29. Adaptive hiding Hiding links to concepts in ISIS-Tutor: not ready (neutral) links are removed. The rest of 64 links fits one screen.

30. Adaptive annotation: InterBook 1. Concept role 2. Current concept state 3. Current section state 4. Linked sections state 4 3 2 1 √

31. ANS: Evaluation ISIS-Tutor: hypermedia-based ITS, adapting to user knowledge on the subject Fixed learning goal setting Learning time and number of visited nodes decreased No effect for navigation strategies and recall

32. Adaptive presentation techniques Conditional text filtering ITEM/IP, PT, AHA! Adaptive stretchtext MetaDoc, KN-AHS, PUSH, ADAPTS Frame-based adaptation Hypadapter, EPIAIM, ARIANNA, SETA Full natural language generation ILEX, PEBA-II, Ecran Total

33. Example: Stretchtext (PUSH)

34. Example: Stretchtext (ADAPTS)

35. Adaptive presentation: evaluation MetaDoc: On-line documentation system, adapting to user knowledge on the subject Reading comprehension time decreased Understanding increased for novices No effect for navigation time, number of nodes visited, number of operations

36. Topic-based Student Modeling Benefits –Easier for students and teachers to grasp –Easier for teachers to index content –Clear interface for presentation of progress Shortcomings –The user model is too coarse-grained –Precision of user modeling is low

37. Demo: QuizGuide

38. Concept-based Student Modeling Benefits –The user model is fine-grained –Precision of user modeling is good Shortcomings –Harder for students and teachers to grasp –Harder for teachers to index content –Presentation of progress is harder to integrate into the system interface

39. Demo: NavEx

40. NavEx: Interface

41. Indexing Examples in NavEx Concepts derived from language constructs –C-code parser (based on UNIX lex & yacc) –51 concepts totally (include, void, main_func, decl_var, etc) Ask teacher to assign examples to lectures –Use a subsetting approach to divide extracted concepts into prerequisite and outcome concepts

42. Web-inspired technologies One ITS, many student models - group-level adaptation! –Collaboration, group interaction, awareness –Typical goal is adaptive collaboration support Whole class progress is available - helping the teacher! –Intelligent class monitoring Web is a large information resource - helping to find relevant open corpus information –Adaptive information filtering

43. Adaptive collaboration support Peer help Collaborative group formation Group collaboration support –Collaborative work support –Forum discussion support Mutual awareness support Social navigation

44. Demo: Knowledge Sea II

45. Knowledge Sea Map

46. Map Interface Keywords related to documents inside the cell Indicator of density of document inside the cell Lecture Notes (landmarks) Indicator of user traffic Background color: indicator of group traffic Indicator of existence of annotation

47. Cell Content Interface Background: Indicator of group traffic Indicator of user traffic indicator of existence of annotation List of the document inside the current cell Indicator of the position of the current cell in the map

48. Overview The Context Technologies Web impact for adaptive educational systems Adaptive educational systems and E-Learning Challenges for adaptive educational systems

49. Web Impact for AES Just a new platform? Web impact –Changing the paradigm Web benefits Web value –New AES technologies –What else?

50. Old AI-CAI Paradigm (1970) Goal: replace primitive CAI in transfering knowledge (content) to students

51. Classic ITS paradigm (1980s) Goal: support problem solving Classroom context No learning material on-line No adaptive hypermedia No course sequencing Interactive problem solving support is the core technology

52. AWBES: The new paradigm Goal: comprehensive support Self-study context All learning material on-line: - presentations, tests, examples, problems Curriculum sequencing Adaptive navigation support Problem solving support

53. Web benefits Visibility and impact From laboratories to classrooms –Equipment issue –Maintenance issue –Natural part of WBE Testing base and data collection Standard technologies and component reuse

54. Web value One tutor, many students  model matching One student, many tutors  –Distributed AWBES with reusable components Adaptive educational services Teacher participation –Mega-ITS (assembling by request) interaction time flexibility Mega-Tutor (Rowley), Topic Server (Murray)

55. Overview The Context Technologies Web impact for adaptive educational systems Adaptive educational systems and E-Learning Challenges for adaptive educational systems

56. Challenges How to make it working in practice? –AWBES systems use advanced techniques - hard to develop –AWBES content is based on knowledge - hard to create Some Solutions –Component-based architectures for AWBES –Authoring support –Open Corpus Adaptive Systems

57. CMS vs. Research Systems Research systems can provide a better support of each feature Adaptive systems show how to implement nearly each component adaptively How it can be used in real E-Learning context? We need flexibility: –Course authors can choose best components and best content for their needs –Components providers and content providers have a chance to compete in developing better products

58. What is the Future? How to use good component/content if you have a Blackboard, WebCT or other major CMS? Is the future model a Blackboard-style giant system where all components are advanced and adaptive? –Wait for the CMS giants to integrate better tools? –Create our own “adaptive Blackboards” Is there any other choice?

59. Gradual adoption of AWBES Static course sequencing - domain modeling for courseware engineering Customized course generation Adaptive testing Sequencing and navigation support Model matching Problem-solving support

60. Re-use/Standards Movement Learning Object Re-use supported by coming standards is another major research direction in E- Learning The re-use movement joins many existing streams of work driven by similar ideas –Create content once, use many times –Content independent from the “host” system –Content and interfaces with the host system are based on standards (metadata, CMI, etc) Let content providers be players in E-Learning The future is components and re-use

61. Knowledge Tee Architecture Portal Activity Server Student Modeling Server Value-added Service

62. Authoring Support Powerful tools for authors to create intelligent content ITS content editors –Algebra Tutor (Ritter) Adaptive Hypermedia authoring tools –MetaLinks (Murray) –AHA! (De Bra) –NetCoach (Weber)

63. MetaLinks (Murray)

64. NetCoach (Weber)

65. Open Corpus Adaptive Systems Classic AWBES - Closed Corpus of pre-processed content Integrate Open Corpus content Bringing open corpus content in by indexing –KBS-HyperBook, SIGUE Processing open corpus content without manual indexing –NavEx (content-based guidance) –Knowledge Sea (social guidance)