Adaptive Learning Environments

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Adaptive Learning Environments

• Prof. dr. Paul De Bra
• Eindhoven University of Technology

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Topics

• The need for adaptation
• personalized adaptable / adaptive
• User Modeling
• Adaptation
• adaptive presentation
• adaptive navigation
• Authoring
• Examples (if we have time)

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We live in a one size fits all world

• But we are not all the same size(physically or
  mentally)

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Whats the main difference between these pictures?
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Automatic ? Adaptive

• Automatic systems automatic behavior according
  to fixed rules
• Adaptive systems automatic behavior with rules
  that change based on environmental factors
• first-order adaptation the change in the
  automatic behavior follows fixed rules
• second-order adaptation the change in the
  automatic behavior is itself also adaptive
• etc. there is no limit to how adaptive systems
  can be
• In this course we deal with user-adaptive
  systemsthey adapt to users and the users
  environment

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Adaptation in any type of Information System

• Adaptation of the Information
• information adapted to who/where/when you are
• information adapted to what you are doing and
  what you have done before (e.g. learning)
• presentation adapted to circumstances (e.g. the
  device you use, the network, etc.)
• Adaptation of the Process
• adaptation of interaction and/or dialog
• adaptation of navigation structures
• adaptation of the order of tasks and steps

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Advantages of Adaptive Systems

• Increased efficiency
• optimal process (of navigation, dialog, study
  order, etc.)
• minimum number of steps
• maximum benefit (of relevant information)
• Increased satisfaction
• system gives good advice and relevant information
• interactive applications do not make stupid moves
• Return on investment
• recommending products the user needs is a form
  ofadvertising that really works
• adaptive (non-IS) systems have better technical
  performance

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Disadvantages of Adaptive Systems

• Adaptive Systems may learn the wrong behavior
• adaptive games learn badly from bad players
• generally adaptation good for one user may be
  bad for another user it is personal after all
• Adaptive Systems may outsmart the users
• all doomsday movies in which machines take over
  the world blame second order adaptive systems
• a game that learns how always to win is no fun
• an adaptive information system may effectively
  perform censorship
• it may be hard to tell an adaptive system that it
  is wrong

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User-Adaptive Systems
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Main issues in Adaptive Systems

• Questions to ask when designing an adaptive
  application
• Why do we want adaptation?
• What can be adapted?
• What can we adapt to?
• How can we collect the right information?
• How can we process/use that information
• Exercise answer these questions for
• a presentation (lectures, talks at conferences)
• an on-line textbook
• a newspaper site or an on-line TV-guide
• a (book, cd, computer, etc.) store
• a (computer) help system

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Forward and Backward Reasoning

• Two opposite approaches for adaptation
• forward reasoning
• register events
• translated events to user model information
• store the user model information
• adaptation based directly on user model
  information
• backward reasoning
• register events
• store rules to deduce user model information from
  events
• store rules to deduce adaptation from user model
  information
• performing adaptation requires backward
  reasoning decide which user model information is
  needed and then deduce which event information is
  needed for that.

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Application Areas of AS

• Educational hypermedia systems
• on-line course text, with on-line multiple-choice
  or other machine- interpretable tests
• we use AEH, AES and ALE as near-synonyms
• On-line information systems
• information kiosk, documentation systems,
  encyclopedias, etc.
• On-line help systems
• context-sensitive help, (think of Clippy)
• Information retrieval and filtering
• adaptive recommender systems
• etc.

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Adaptive Educational Hypermedia

• Origin Intelligent Tutoring Systems
• combination of reading material and tests
• adaptive course sequencing, depending on test
  results
• In Adaptive Educational Hypermedia
• more freedom for the learner guidance instead of
  enforced sequence
• adaptive content of the course material to solve
  comprehension problems when pages or chapters
  are read out of sequence
• adaptation based on reading as well as tests

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What can we Adapt to?

• Knowledge of the user
• initialization using stereotypes (beginner,
  intermediate, expert)
• represented in an overlay model of the concept
  structure of the application
• fine grained or coarse grained
• based on browsing and on tests
• Goals, tasks or interest
• mapped onto the applications concept structure
• difficult to determine unless it is preset by the
  user or a workflow system
• goals may change often and more radically than
  knowledge

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What can we Adapt to? (cont.)

• Background and experience
• background users experience outside the
  application
• experience users experience with the
  applications hyperspace
• Preferences
• any explicitly entered aspect of the user that
  can be used for adaptation
• examples media preferences, cognitive style,
  etc.
• Context / environment
• aspects of the users environment, like browsing
  device,window size, network bandwidth,
  processing power, etc.

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User Modeling
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Modeling Knowledge in AES

• Moving target knowledge changes while using the
  application
• scalar model knowledge of whole course measured
  on one scale (used e.g. in MetaDoc)
• structural model domain knowledge divided into
  independent fragments knowledge measuredper
  fragment
• type of knowledge (declarative vs. procedural)
• level of knowledge (compared to some ideal)
• positive (overlay) or negative information(bug
  model) can be used

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Overlay Modeling of User Knowledge

• Domain of an application modeled through a
  structure (set, hierarchy, network) of concepts.
• concepts can be large chunks (like book chapters)
• concepts can be tiny (like paragraphs or
  fragments of text, rules or constraints)
• relationships between concepts may include
• part-of defines a hierarchy from large learning
  objectives down to small (atomic) items to be
  learned
• is-a semantic relationship between concepts
• prerequisite study this before that
• some systems (e.g. AHA!) allow the definition
  ofarbitrary relationships

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Which types of knowledge values?

• Early systems Boolean value (known/not known)
• works for sets of concepts, but not for
  hierarchies (not possible to propagate knowledge
  up the hierarchy)
• Numeric value (e.g. percentage)
• how much you know about a concept
• what is the probability that you know the concept
• Several values per concept
• e.g. to distinguish sources of the information
• knowledge from reading is different
  fromknowledge from test, activities, etc.

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Modeling Users Interest

• Initially weighed vector of keywords
• this mimics how early IR systems worked
• More recently weighed overlay of domain model
• more accurate representation of interest
• able to deal with synonyms (since terms are
  matched to concepts)
• semantic links (as used in ontologies) allow to
  compensate for sparsity
• move from manual classification of documents to
  automatic matching between documents and an
  ontology

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Modeling Goals and Tasks

• Representation of the user's purpose
• goal typically represented using a goal
  catalog(in fact an overlay model)?
• systems typically assume the user has one goal
• automatic determination of the goal is
  difficult(use glass box approach show goal, let
  user change it)?
• the goal can change much more rapidly than
  knowledge or interest
• Determining the user's goal/task is much easier
  when adaptation is done within a
  workflowmanagement system

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Modeling Users Background

• User's previous experience outside the core
  domain of the application
• e.g. (prior) education, profession, job
  responsibilities, experience in related areas,
  ...
• system can typically deal with only a few
  possibilities, leading to a stereotype model
• background is typically very stable
• background is hard to determine automatically

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Modeling Individual Traits

• Features that together define the user as an
  individual
• personality traits (e.g. introvert/extrovert)
• cognitive styles (e.g. holist/serialist)
• cognitive factors (e.g. working memory capacity)
• learning styles (like cognitive styles but
  specific to how the user likes to learn)

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Modeling Users Context of Work

• User model contain context features although
  these are not really all user features.
• platform screen dimensions, browser software
  and network bandwidth may vary a lot
• location important for mobile applications
• affective state motivation, frustration,
  engagement

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Feature-Based vs. Stereotype Modeling

• Stereotypes simple, can be designed carefully,
  very useful for bootstrapping adaptive
  applications
• Feature-Based allows for many more variations
• each feature considered can be used to adapt
  something
• detailed features leading to micro-adaptationdo
  not necessary leading to overall adaptationthat
  makes sense

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Uncertainty-Based User Modeling

• Most used techniques Bayesian Networks and Fuzzy
  Logic
• user actions provide evidence that the user
  has(or does not have) knowledge of a concept
• an expert needs to develop a qualitative model
• each concept becomes a random variable (node in
  BN)
• source of evidence reading time, answers to
  tests, etc.
• consider direction between evidential nodes E
  andknowledge nodes K
• causal direction K ? E (knowledge leads to
  evidence)
• diagnostic direction E ? K (evidence leads to
  knowledge)
• independence of variables influences validityof
  the model

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Generic User Modeling Systems

• Adaptive Systems with built-in UM
• close match between UM structure and AS needs
• high performance possible (no communication
  overhead)
• UM not easily exchangeable with other AS
• AS using a generic User Modeling System
• cuts down on AS development cost
• communication overhead
• unneeded features may involve performance penalty
• UM can be shared between AS

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Requirements for Generic UM Systems

• Generality, including domain independence
• Expressiveness and strong inferential
  capabilities
• Support for quick adaptation
• Extensibility
• Import of External User-Related Information
• Management of Distributed Information
• Support for Open Standards
• Load Balancing
• Failover Strategies
• Transactional Consistency
• Privacy Support

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Requirements for Sharing UM Data

• Sharing a technical API is not enough
• the AS must translate its internal user
  identities to the UM's user identities (and vice
  versa)
• data about users need to be standardized
• shared ontologies are needed for different AS
  dealing with the same domain (ontology alignment)
• agreement on who can update what
• agreement on meaning of values in the UM
• Scrutability of UM
• UM data must be understandable for the user
• users must have control over theirUM data

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User Modeling in GRAPPLE

• User model is inherently distributed
• The LMS contains fairly stable information about
  the user
• The ALE contains dynamically changing information
  about the user
• There may be several components of each type
• Different UM services may contradict each other
• conflict resolution needed
• Not every application is allowed to access/update
  UM data on every server
• elaborate security/privacy settings needed

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The GRAPPLE UM Architecture

• Synchronous communication
• send query to broker
• broker forwards query to appropriate server(s)
• answers are sent back (through the broker)
• Asynchronous communication
• applications signal a query or update to an
  event bus (or broker)
• services handle these events and may produce a
  response which is posted to the event bus
• caching is used to prevent applications from
  hanging while waiting for answers/responses

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Adaptation
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What Do We Adapt in AEH?

• Adaptive presentation
• adapting the information
• adapting the presentation of that information
• selecting the media and media-related factors
  such as image or video quality and size
• Adaptive navigation
• adapting the link anchors that are shown
• adapting the link destinations
• giving overviews for navigation support and
  fororientation support

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Adaptive Presentation
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Canned Text Adaptation

• Inserting/removing fragments
• prerequisite explanations inserted when the user
  appears to need them
• additional explanations additional details or
  examples for some users
• comparative explanations only shown to users who
  can make the comparison
• Altering fragments
• Most useful for selecting among a number of
  alternatives
• Can be done to choose explanations or examples,
  but also to choose a single term
• Sorting fragments
• Can be done to perform relevance ranking for
  instance

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Canned Text Adaptation (cont.)?

• Stretchtext
• Similar to replacement links in the Guide
  hypertext system
• Items can be open or closed system decides
  adaptively which items to open when a page is
  accessed
• Dimming fragments
• Text not intended for this user is
  de-emphasized(greyed out, smaller font, etc.)
• Can be combined with stretchtext to create
  de-emphasized text that conditionally appears, or
  only appears after some event (like clicking on
  a tooltip icon)

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Example of inserting/removing fragments, course
2L690

• Before reading about Xanadu the URL page shows
• In Xanadu (a fully distributed hypertext
  system, developed by Ted Nelson at Brown
  University, from 1965 on) there was only one
  protocol, so that part could be missing.
• After reading about Xanadu this becomes
• In Xanadu there was only one protocol, so that
  part could be missing.

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Example of inserting/removing fragments the GEA
system.

• selects objects based on matching attributes
  (arguments) to user preferences
• presents arguments with relevance greater than a
  (customizable) threshold.

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Example with group adaptation Intrigue (adaptive
tourist guide)
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Stretchtext examplethe Push system
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Scaling-based Adaptation
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Adaptive Navigation Support
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Adaptive Navigation Support

• Direct guidance
• like an adaptive guided tour
• next button with adaptively determined link
  destination
• Adaptive link generation
• the system may discover new useful links between
  pagesand add them
• the system may use previous navigation or page
  similarityto add links
• generating a list of links is typical in
  information retrievaland filtering systems
• Variant Adaptive link destinations
• link anchor is fixed (or at least always present)
  but the system decides on the link destination
  on the fly

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Adaptive Navigation Support (cont.)

• Adaptive link annotation
• all links are visible, but an annotation
  indicates relevance
• the link anchor may be changed (e.g. in color) or
  additional annotation symbols can be used
• Adaptive link hiding
• pure hiding means the link anchor is shown as
  normal text (the user cannot see there is a link)
• link disabling means the link does not work it
  may or may not still be shown as if it were a
  link
• link removal means the link anchor is removed
  (and as a consequence the link cannot be used)
• a combination is possible hidingdisabling means
  the link anchor text is just plain text

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Adaptive Navigation Support (cont.)

• Map adaptation
• complete (site)maps are not feasible for a
  non-trivial hyperspace
• a local or global map can be adapted by
  annotating or removing nodes or larger parts
• a map can also be adapted by moving nodes around
• maps can be graphical or textual
• adaptation can be based on relevance, but also on
  group presence

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Example of Direct Guidance

• Simple suggest one best page to go to
• Webwatchercurious eyes
• Sometimes anext button
• Popular in ITS(sequencing)

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Example Link Ordering/Sorting

• Sorting links from most to least relevant.
• first introduced in Hypadapter (Lisp tutor)
• manual reordering by the user (if supported) can
  be used as feedback to update the user model

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ExampleLink Annotation in ELM-ART
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Examplelink annotation in Interbook
4

3
2
v
1
3. Current section state 4. Linked sections state
1. Concept role 2. Current concept state
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ExampleLink Annotation in ISIS-Tutor
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Example Link Annotation and Hiding in ISIS-Tutor
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ExampleLink Generation in Alice
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Adaptation in GRAPPLE

• Based on AHA! version 4 must be very generic
• three separate components UM server, DM/AM
  server, adaptation engine (AE)
• linked through an event bus
• separation between concepts and content
• adaptation rules can call arbitrary (Java) code
• supports forward and backward reasoning
• UM caching to improve performance
• adaptation to arbitrary XML formats
• prepared to adapt within or without a surrounding
  LMS environment

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AHA! Examples

• Most AHA! applications look like this
• This is the default layout, but any other
  layout is possible.

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AHA! can look very different

• Interbook style

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Creating Adaptive Applications

• Main question at what level to define the
  adaptation (and the user model updates)?
• AE works with adaptation rules
• tutorial.readme.knowledge 100
• if (tutorial.readme.knowledge) gt 50 then
• For authoring we prefer higher-level concept
  relationships
• A is a prerequisite for B
• A is a child of B (in a concept hierarchy)
• Some applications require still higher-level
  constructs sequences, process models, etc.
• In GRAPPLE CAM or Conceptual Adaptation Model

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Authoring in AHA!the Graph Author
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Example Applications

• The AHA! tutorial
• http//aha.win.tue.nl/tutorial/
• An adaptive paper about the Design of AHA!(and a
  presentation about it)
• http//aha.win.tue.nl/ahadesign/
• http//aha.win.tue.nl/ahadesigntalk/
• The hypermedia course 2L690
• http//wwwis.win.tue.nl/2L690/
• An adaptive version of a BBC course on Business
  English
• http//www.learning-demo.eu/aha/BE/
• AlcoZone an alcohol tutorial from Virginia Tech
• http//www.alcohol.vt.edu/alcozone06/

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Acknowledgements

• AHA! was partly developed with a grant from the
  NLnet Foundation
• Part of this work was performed as part of the
  Minerva ALS project (Adaptive Learning Spaces),
  229714-CP-1-2006-1-NL-MPP
• Part of this work was performed as part of the EU
  FP7 STREP project GRAPPLE (215434)