June 19, 2008 TEL & KM Summerschool 2008 Slide 1 Adaptive Learning Environments Prof. dr. Paul De Bra Eindhoven University of Technology

June 19, 2008 TEL & KM Summerschool

2008Slide 1

Adaptive Learning Environments

Prof. dr. Paul De BraEindhoven University of

Technology


2008Slide 2

Topics

• The need for adaptation– personalized: adaptable / adaptive

• User Modeling• Adaptation

– adaptive presentation– adaptive navigation

• Authoring• Examples (if we have time)


2008Slide 3

We live in a “one size fits all” world

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


2008Slide 4

What’s the main difference between these pictures?


2008Slide 5

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 systems:

they adapt to users and the users’ environment


2008Slide 6

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


2008Slide 7

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 of

advertising that really works– adaptive (non-IS) systems have better technical

performance


2008Slide 8

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


2008Slide 9

User-Adaptive Systems

.

SystemProcesses

User Model

Collects

Data about user

Adaptation effect

User Modeling

Adaptation

Processes


2008Slide 10

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


2008Slide 11

Forward and Backward Reasoning• Two opposite approaches for adaptation:• forward reasoning:

1. register events2. translated events to user model information3. store the user model information4. adaptation based directly on user model information

• backward reasoning:1. register events2. store rules to deduce user model information from

events3. store rules to deduce adaptation from user model

information4. performing adaptation requires backward reasoning:

decide which user model information is needed and then deduce which event information is needed for that.


2008Slide 12

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.


2008Slide 13

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


2008Slide 14

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


2008Slide 15

What can we Adapt to? (cont.)

• Background and experience– background = user’s experience outside the application– experience = user’s experience with the application’s

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 user’s environment, like browsing device,

window size, network bandwidth, processing power, etc.


2008Slide 16

User Modeling

.

SystemProcesses

User Model

Collects

Data about user

Adaptation effect

User Modeling

Adaptation

Processes


2008Slide 17

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


2008Slide 18

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 of

arbitrary relationships


2008Slide 19

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 from

knowledge from test, activities, etc.


2008Slide 20

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


2008Slide 21

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


2008Slide 22

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


2008Slide 23

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)


2008Slide 24

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


2008Slide 25

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-adaptation

do not necessary leading to overall adaptationthat makes sense


2008Slide 26

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 and

knowledge 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


2008Slide 27

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


2008Slide 28

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


2008Slide 29

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 their

UM data


2008Slide 30

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


2008Slide 31

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


2008Slide 32

Adaptation

.

SystemProcesses

User Model

Collects

Data about user

Adaptation effect

User Modeling

Adaptation

Processes


2008Slide 33

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


2008Slide 34

Adaptive Presentation


2008Slide 35

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


2008Slide 36

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)


2008Slide 37

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.

…


2008Slide 38

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.


2008Slide 39

Example with group adaptation: Intrigue (adaptive tourist guide)


2008Slide 40

Stretchtext example:the Push system


2008Slide 41

Scaling-based Adaptation


2008Slide 42

Adaptive Navigation Support


2008Slide 43

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 pages

and 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”


2008Slide 44

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: hiding+disabling means the link

anchor text is just plain text


2008Slide 45

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


2008Slide 46

Example of Direct Guidance

• Simple: suggest one best page to go to– Webwatcher:

curious eyes– Sometimes a

“next” button– Popular in ITS

(sequencing)


2008Slide 47

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


2008Slide 48

Example:Link Annotation in ELM-ART


2008Slide 49

Example:link annotation in Interbook

1. Concept role2. Current concept state

3. Current section state

4. Linked sections state

4

3

2

1

√


2008Slide 50

Example:Link Annotation in ISIS-Tutor


2008Slide 51

Example: Link Annotation and Hiding in ISIS-Tutor


2008Slide 52

Example:Link Generation in Alice


2008Slide 53

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


2008Slide 54

AHA! Examples

• Most AHA! applications look like this:

• This is the “default” layout, but any other layout is possible.


2008Slide 55

AHA! can look very different

• Interbook style:


2008Slide 56

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) > 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


2008Slide 57

Authoring in AHA!:the Graph Author


2008Slide 58

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/


2008Slide 59

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)

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June 19, 2008 TEL & KM Summerschool 2008 Slide 1 Adaptive Learning Environments Prof. dr. Paul De Bra Eindhoven University of Technology