1 / 64

Adaptive Systems for E-Learning

This article discusses the use of adaptive systems in e-learning, exploring the technologies and challenges associated with them. It highlights the benefits of adaptive systems in catering to diverse learners and addresses the limitations of traditional systems. The article also compares intelligent and adaptive systems, emphasizing the importance of integrating both approaches for effective e-learning.

maryemurray
Download Presentation

Adaptive Systems for E-Learning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

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

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

  4. The Context • Adaptive systems • Why adaptive? • Adaptive vs. intelligent

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

  6. Adaptive software systems • Intelligent Tutoring Systems • adaptive course sequencing • adaptive . . . • Adaptive Hypermedia Systems • adaptive presentation • adaptive navigation support • Adaptive Help Systems • Adaptive . . .

  7. 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

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

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

  10. Technologies • Origins of AWBES technologies • ITS Technologies • AH Technologies • Web-Inspired Technologies

  11. Origins of AWBES Technologies Adaptive HypermediaSystems Intelligent TutoringSystems Adaptive Web-basedEducational Systems

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

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

  14. 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)

  15. 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

  16. 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, ...

  17. 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

  18. 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

  19. 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

  20. Example: PAT-Online

  21. 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

  22. Example: ELM-ART

  23. 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!

  24. Adaptive hypermedia • Hypermedia systems = Pages + Links • Adaptive presentation • content adaptation • Adaptive navigation support • link adaptation

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

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

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

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

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

  30. 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

  31. 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

  32. Example: Stretchtext (PUSH)

  33. Example: Stretchtext (ADAPTS)

  34. 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

  35. 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

  36. Demo: QuizGuide

  37. 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

  38. Demo: NavEx

  39. NavEx: Interface

  40. 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

  41. 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

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

  43. Demo: Knowledge Sea II

  44. Knowledge Sea Map

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

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

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

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

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

  50. 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

More Related