INFSCI 2955 Adaptive Web Systems Session 1-2: Adaptive E-Learning Systems

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INFSCI 2955Adaptive Web SystemsSession 1-2
Adaptive E-Learning Systems

• Peter Brusilovsky
• School of Information Sciences
• University of Pittsburgh, USA
• http//www.sis.pitt.edu/peterb/2955-092/

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(No Transcript)
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Overview

• The Context
• Technologies
• Adaptive E-Learning Systems vs. Learning
  Management Systems (LMS)

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The Context

• Adaptive systems
• Why adaptive?
• Adaptive vs. intelligent

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Adaptive systems

• Classic loop user modeling - adaptation in
  adaptive systems

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Adaptive Software Systems

• Intelligent Tutoring Systems
• Adaptive Help Systems
• Adaptive Interfaces
• Adaptive Hypermedia Systems
• Adaptive Search
• Adaptive Filtering/Recommendation
• Adaptive

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Major Issues

• What is adaptive?
• Adaptive sequencing of educational tasks
• Adaptive content presentation
• Adaptive ordering of search results
• What kinds of information about user?
• User knowledge
• User interests
• User individual traits

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Why Adaptive E-Learning?

• Adaptation was always an issue in education -
  what is special about the Web?
• 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

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Intelligent vs. Adaptive

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

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2
3
Adaptive ES
Intelligent ES
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Technologies

• Origins of AEL technologies
• ITS Technologies
• AH Technologies
• Native Web Technologies

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Origins of AEL Technologies
Adaptive HypermediaSystems
Intelligent TutoringSystems
Adaptive Web-basedEducational Systems
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Origins of AEL Technologies (1)
Intelligent Tutoring Systems
Adaptive Hypermedia Systems
Adaptive Hypermedia
Intelligent Tutoring
Adaptive Presentation
Problem Solving Support
Curriculum Sequencing
Adaptive Navigation Support
Intelligent Solution Analysis
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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)

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Technology Fusion
Adaptive Educational Systems
Adaptive Web
Adaptive E-Learning
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Adaptive Web

• Adaptive information filtering
• Adaptive search
• Adaptive recommendation
• Social navigation
• Log mining
• Machine learning
• CSCW

Brusilovsky, P., Kobsa, A., and Neidl, W. (eds.)
(2007) The Adaptive Web Methods and Strategies
of Web Personalization. Lecture Notes in Computer
Science, Vol. 4321, Berlin Heidelberg New York
Springer-Verlag.
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Origins of AEL Technologies (2)
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
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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

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Inherited Technologies

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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How to Model User Knowledge

• Domain model
• The whole body of domain knowledge is decomposed
  into set of smaller knowledge units
• A set of concepts, topics, etc
• Student model
• Overlay model
• Student knowledge is measured independently for
  each knowledge unit

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Vector vs. network models

• Vector - no relationships
• Precedence (prerequisite) relationship
• is-a, part-of, analogy (Wescourt et al, 1977)
• Genetic relationships (Goldstein, 1979)

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Vector model
Concept 4
Concept 1
Concept N
Concept 2
Concept 5
Concept 3
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Network model
Concept 4
Concept 1
Concept N
Concept 2
Concept 5
Concept 3
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Simple overlay model
Concept 4
Concept 1
no
yes
Concept N
no
Concept 2
yes
no
yes
Concept 5
Concept 3
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Simple overlay model
Concept 4
Concept 1
no
yes
Concept N
no
Concept 2
yes
no
yes
Concept 5
Concept 3
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Weighted overlay model
Concept 4
Concept 1
3
10
Concept N
0
Concept 2
7
2
4
Concept 5
Concept 3
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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, ...

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Course Sequencing

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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Active vs. passive sequencing

• Active sequencing
• goal-driven expansion of knowledge/skills
• achieve an educational goal
• predefined (whole course)
• flexible (set by a teacher or a student)
• Passive sequencing (remediation)
• sequence of actions to repair misunderstanding or
  lack of knowledge

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Levels of sequencing

• High level and low level sequencing

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Sequencing options

• On each level sequencing decisions can be made
  differently
• Which item to choose?
• When to stop?
• Options
• predefined
• random
• adaptive
• student decides

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Simple cases of sequencing

• No topics
• One task type
• Problem sequencing and mastery learning
• Question sequencing
• Page sequencing

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What do we need for sequencing?

• Domain model
• Network of concepts
• User model
• Overlay model of user knowledge
• Model of Educational Material
• Content Indexing
• Goal model (for active sequencing)

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Simple case one concept per ULM
Concept 4
Concept 1
Concept N
Concept 2
Concept 5
Concept 3

• Random selection if there are no links -Scholar
• Links can be used to restrict the order

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Indexing ULMs with concepts
Example 1
Examples
Concepts
Concept 4
Example 2
Example M
Concept 1
Concept N
Problems
Concept 2
Problem 1
Concept 5
Problem 2
Problem K
Concept 3
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Simple goal model

• Learning goal as a set of topics

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More complicated models

• Sequence, stack, tree

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Indexing teaching material

• Types of indexing
• One concept per ULM
• Indexing of ULMs with concepts
• How to get the ULMs indexed?
• Manual indexing (closed corpus)
• Computer indexing (open corpus)

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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

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Sequencing for AES

• Simplest technology to implement with CGI
• Important for WBE
• no perfect order
• lack of guidance
• No student modeling capability!
• Requires external sources of knowledge about
  student
• Problem/question sequencing is self-sufficient

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ELM-ART question sequencing
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SIETTE Adaptive Quizzes
Combination ofCAT and concept Based adaptation
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Models in SIETTE
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Beyond Sequencing Generation
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Adaptive Problem Solving Support

• The main duty of ITS
• From diagnosis to problem solving support
• Highly-interactive support
• interactive problem solving support
• Low-interactive support
• intelligent analysis of problem solutions

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Adaptive Problem Solving Support

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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Models for interactive problem-solving support
and diagnosis

• Domain model
• Concept model (same as for sequencing)
• Bug model
• Constraint model
• Student model
• Generalized overlay model (works with bug model
  and constraint model too)
• Teaching material - feedback messages for
  bugs/constraints

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Bug models

• Each concept/skill has a set of associated
  bugs/misconceptions and sub-optimal skills
• There are help/hint/remediation messages for bugs

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Intelligent analysis of problem solutions

• Intelligent analysis of problem solutions
• Classic system PROUST
• Support Identifying misconceptions (bug model)
  and broken constraints (CM)
• Provides feedback adapted to the user model
  remediation, positive help
• Low interactivity Works after the (partial)
  solution is completed

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Example ELM-ART
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Example SQL-Tutor
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Interactive Problem Solving Support

• Classic System Lisp-Tutor
• The ultimate goal of many ITS developers
• Several kinds of adaptive feedback on every step
  of problem solving
• Coach-style intervention
• Highlight wrong step
• What is wrong
• What is the correct step
• Several levels of help by request

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Example PAT-Online
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Example WADEIn
http//adapt2.sis.pitt.edu/cbum/
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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!

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Do we need bug models?

• Lots of works on bug models in the between
  1974-1985
• Bugs has limited applicability
• Problem solving feedback only. Sequencing does
  not take bugs into account whatever
  misconceptions the student has - effectively we
  only can re-teach the same material
• Short-term model once corrected should
  disappear, so not necessary to keep

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Models for example-based problem solving support

• Need to represent problem-solving cases
• Episodic learner model
• Every solution is decomposed on smaller
  components, but not concepts!
• Keeping track what components were used and when
  - not an overlay!
• ELM-PE and ELM-ART - only systems that use this
  model

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Adaptive hypermedia

• Hypermedia systems Pages Links
• Adaptive presentation
• content adaptation
• Adaptive navigation support
• link adaptation
• Could be considered as soft sequencing
• Helping the user to get to the right content

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Adaptive problem solving support

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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Adaptive Navigation Support

• Direct guidance
• Hiding, restricting, disabling
• Generation
• Sorting
• Annotation
• Map adaptation

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Adaptive Annotation Icons

• Annotations for topic states in Manuel Excell
  not seen (white lens) partially seen (grey
  lens) and completed (black lens)

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Adaptive Annotation Icons
4

3
2
v
1
InterBook system

• 1. Concept role
• 2. Current concept state

3. Current section state 4. Linked sections state
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Adaptive Annotation Font Color
Annotations for concept states in ISIS-Tutor not
ready (neutral) ready and new (red) seen
(green) and learned (green)
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Adaptive Hiding
Hiding links to concepts in ISIS-Tutor not ready
(neutral) links are removed. The rest of 64 links
fits one screen.
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What Size of concept?

• How much domain knowledge should a concept cover?
• Two practical approaches
• Topic-based student modeling
• Large topics, one per ULM/page
• Concept-based student modeling
• Small concepts, many per ULM/page

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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

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Demo QuizGuide
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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

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Demo NavEx
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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

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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

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Adaptive Presentation

• 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

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Adaptive Presentation

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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Example PUSH (stretchtext)
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Example SASY
Scrutable adaptivepresentation
http//www.cs.usyd.edu.au/marek/sasy/
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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

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Models for Adaptive Hypermedia

• Domain model - same as for sequencing
• User model - same as for sequencing
• Goal model - same as for sequencing
• Model of the learning material
• For ANS - same as for sequencing
• For AP - could use fragment or frame indexing

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Indexing of nodes

• Domain model

Hyperspace
Concept 4
Concept 1
Concept n
Concept 2
Concept m
Concept 3
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Indexing of page fragments
Node
Concepts
Fragment 1
Concept 4
Concept 1
Concept N
Concept 2
Fragment 2
Concept 5
Concept 3
Fragment K
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Adapting to User Knowledge Other Ideas

• Adaptive interface
• Presence of menus and widgets in an educational
  applet can be adapted to user knowledge
• Educational animation and simulation
• Adaptive explanations
• Adaptive visualization

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Demo WADEIn
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Demo Improve
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Adapting to Individual Traits

• Source of knowledge
• educational psychology research on individual
  differences
• Known as cognitive or learning styles
• Field dependence, wholist/serialist (Pask)
• Kolb, VARK, Felder-Silverman classifiers

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Style-Adaptive Hypermedia

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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Style-Adaptive Hypermedia

• Different content for different style
• Recommended/ordered links
• Generated on a page
• Mixed evidences in favor
• Different navigation tools for different styles
• Adding/removing maps, advanced organizers, etc.
• Good review
• Bajraktarevic, N., Hall, W., and Fullick, P.
  2003. Incorporating Learning Styles in Hypermedia
  Environment Empirical Evaluation, In Proceedings
  of Workshop on Adaptive Hypermedia and Adaptive
  Web-Based Systems, Nottingham, 41-52.
  http//wwwis.win.tue.nl/ah2003/proceedings/paper4.
  pdf

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Example AES-CS
Interface for field-independent learners
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Example AES-CS
Interface for field-dependent learners
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Style-Adaptive Feedback

• What is modeled?
• User knowledge of the subject
• User individual traits
• What is adapted?
• Order of educational activities
• Presentation of hypertext links
• Presented content
• Problem solving feedback

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Overview Classic Technologies
What? Knowledge Styles
Order of activities Sequencing ?
Feedback Adaptive diagnosis Style-adaptive feedback
Content Adaptive presentation Adaptive presentation
Links Adaptive navigation support Adaptive navigation support
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Origins of AEL Technologies (2)
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
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Native Web Technologies

• Availability of logs - helping the teacher!
• Log-mining
• Intelligent class monitoring - class progress is
  available!
• One system, many users - group adaptation!
• Adaptive collaboration support
• Web is a large information resource - helping to
  find relevant open corpus information
• Adaptive content recommendation
• Possible combinations of the above
• Collaborative recommendation
• Social navigation

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What You Can Get from Logs?

• Log processing and presentation
• Presenting student progress on topic and concept
  level making sense of class
• Course/site improvements
• Grouping users by learning styles
• Intelligent class monitoring
• Comparing progress, identifying students way
  ahead and behind

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Adaptive Collaboration Support

• Peer help
• Collaborative group formation
• Group collaboration support
• Collaborative work support
• Forum discussion support
• Mutual awareness support
• More information
• Proceedings of the Workshop on Personalization in
  E-learning Environments at Individual and Group
  Level at the 11th International Conference on
  User Modeling, http//hermis.di.uoa.gr/PeLEIGL/PIN
  G07-proceedings.pdf

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Personalized Access to Educational Resources

• A lot of resources are available on the Web and
  in educational DL/Repostitories
• A new direction of adaptation - provide
  personalized access to these resources
• Content based recommender
• Adding advantage of community wisdom
• Collaborative recommender systems
• Social navigation systems

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Modeling User Interests

• Concept-level modeling
• Same domain models as in knowledge modeling, but
  the overlay models level of interests, not level
  of knowledge
• Keyword-level modeling
• Uses a long list of keywords (terms) in place of
  domain model
• User interests are modeled as weigthed vector or
  terms
• Originated from adaptive filtering/search area

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Keyword User Profiles
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Use of Profiles in Adaptive Web
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Use of Profiles in AES ML Tutor
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Social Computing

• Web 2.0 for education
• Collaborative filtering
• AlteredVista
• Collaborative resource discovery systems
• CoFIND
• UMtella (Demo)
• Presence-based collaboration (Educo)
• Social navigation support for open corpus
  resources (Knowledge Sea II)

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Demo UMtella
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Example EDUCO
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Demo Knowledge Sea II
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KSII 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
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Overview

• The Context
• Technologies
• Adaptive E-Learning Systems vs. Learning
  Management Systems (LMS)

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Learning Management Systems

• From separate tools to Learning Management
  Systems (LMS)
• University-level
• Cyberprof, Mallard, CM Online...
• Consulting
• eCollege, Eduprise...
• Commercial
• TopClass, WebCT, LearningSpace, Blackboard...
• Open Source
• Moodle, SAKAI
• Standardization IMS, IEEE LTCS, SCORM...

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What LMS Can Do

• For students
• Course information and content delivery
• Assessment and grades
• Communication and collaboration
• For teachers
• Authoring
• Learning control
• Student monitoring
• Communication

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What AES Can Do for Students

• Presentation
• Adaptive presentation, adaptive navigation
  support, adaptive sequencing
• Assessment
• Adaptive testing
• Communication and collaboration
• Peer help and collaborative group formation
• Collaboration coach
• Learning by doing
• Problem solving support

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What AES Can Do for Teachers

• Student monitoring
• Identifying students in trouble
• Control
• Sequencing
• Adaptive navigation support
• Authoring
• Concept-based authoring and courseware engineering

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AES vs. LMS

• Adaptive E-Learning systems can provide a more
  advanced support for most functions
• Course material presentation - InterBook, AHA
• Assessment with quizzes - SIETTE
• Threaded discussions - collaboration agents
• Student management - intelligent monitoring
• Why LMS are not really adaptive?
• Except simple control and learning design

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Challenges

• How to make it working in practice?
• AES systems use advanced techniques - hard to
  develop
• AWBES content is based on knowledge - hard to
  create
• AES require login and user modeling - hard to
  integrate
• Possible solutions - (watch, PhD students!)
• Component-based architectures for AWBES
• Authoring support
• Open Corpus adaptive systems

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Component-based Architectures

• Research systems can provide a better support of
  almost each function of E-Learning process
• Adaptive systems show how to implement nearly
  each component adaptively
• We need the ability to assemble from components
• 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

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Current State

• Several component-based frameworks
• ADAPT2, ActiveMath, MEDEA,
• Attempts to develop systems with internal
  components
• Reusable user/student model servers
• Some matching work in the standardization movement

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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

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What is the Future?

• How to use good component/content if you have a
  Blackboard, Moodle 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?

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ADAPT2 Architecture
Portal
ActivityServer
Value-added Service
Student Modeling Server
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Knowledge Tree II Portal
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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)

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MetaLinks (Murray)
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AHA! (De Bra)
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NetCoach (Weber)
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Open Corpus Adaptive Systems

• Classic AES - 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)

121
Further Reading

• Brusilovsky, P. (2001) Adaptive hypermedia. User
  Modeling and User Adapted Interaction 11 (1/2),
  87-110
• Brusilovsky, P. and Peylo, C. (2003) Adaptive and
  intelligent Web-based educational systems.
  International Journal of Artificial Intelligence
  in Education 13 (2-4), 159-172.
• Brusilovsky, P. (2003) Developing Adaptive
  Educational Hypermedia Systems From Design
  Models to Authoring Tools. In T. Murray, S.
  Blessing and S. Ainsworth (eds.) Authoring Tools
  for Advanced Technology Learning Environments
  Toward cost-effective adaptive, interactive, and
  intelligent educational software. Kluwer
  Dordrecht, pp. 377-409.
• Brusilovsky, P. and Millan, E. (2007) User models
  for adaptive hypermedia and adaptive educational
  systems. In P. Brusilovsky, A. Kobsa and W.
  Neidl (eds.) The Adaptive Web Methods and
  Strategies of Web Personalization. Lecture Notes
  in Computer Science, Vol. 4321, Berlin Heidelberg
  New York Springer-Verlag, pp. 3-53.