Adpative learning environment diffusable
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Transcript of Adpative learning environment diffusable
Adaptive LearningAdaptive Learning Environment
Mona LAROUSSIMona LAROUSSI
"The best way to predict thefuture is to invent it."Alan Kay 1
Summary• The need for adaptation
personalized: adaptable / adaptive /
y
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– personalized: adaptable / adaptive / flexible etc
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• Learner Modeling• Different kind of Adaptationp
– adaptive presentationadaptive navigation– adaptive navigation
– Adaptive interaction • Our work
"The best way to predict thefuture is to invent it."Alan Kay 2
We live in a “one size fits all” worldWe live in a one size fits all world
"The best way to predict thefuture is to invent it."Alan Kay 3
But we are not all the same sizeBut we are not all the same size
"The best way to predict thefuture is to invent it."Alan Kay 4
Automatic ≠ AdaptiveAutomatic ≠ AdaptiveFixed behavior automatic behavior that
depends on environmental factors
"The best way to predict thefuture is to invent it."Alan Kay 5
Adaptation in any type of Information System
Ad t ti f th I f ti• Adaptation of the Information– information adapted to who/where/when you are
information adapted to what you are doing and what– information adapted to what you are doing and what you have done before (e.g. learning)
– presentation adapted to circumstances (e.g. thepresentation 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
"The best way to predict thefuture is to invent it."Alan Kay 6
Disadvantages of Adaptive SystemsDisadvantages of Adaptive Systems
l th b h i• may learn the wrong behavior• Adaptive Systems may outsmart the users
"The best way to predict thefuture is to invent it."Alan Kay 7
Advantages of Adaptive SystemsAdvantages of Adaptive Systems
I d ffi i• Increased efficiency:• Return on investment
"The best way to predict thefuture is to invent it."Alan Kay 8
Main issues in Adaptive SystemsMain 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?– What can we adapt to?
– How can we collect the right information?
– How can we process/use that information
"The best way to predict thefuture is to invent it."Alan Kay 9
ADAPTIVE LEARNING SYSTEM
"The best way to predict thefuture is to invent it."Alan Kay 10
The beginningThe beginning
Multi-plateform
Multi-plateform
Multi-environne
mentplateformes
plateformes
"The best way to predict thefuture is to invent it."Alan Kay 11
NowNow
Pervasive Computing
"The best way to predict thefuture is to invent it."Alan Kay 12
Dimensions added by technologies
"The best way to predict thefuture is to invent it."Alan Kay 13
ArchitectureArchitecture
"The best way to predict thefuture is to invent it."Alan Kay 14
LEARNER MODEL
"The best way to predict thefuture is to invent it."Alan Kay 15
Learner ProfileLearner Profile
• Common term for user models• This information is used to get the user to more g
relevant information• Views on user profiles in IR communityViews on user profiles in IR community
– Classic - a reference point– Modern - simple form of a user modelModern simple form of a user model
"The best way to predict thefuture is to invent it."Alan Kay 16
Core vs Extended User ProfileCore vs. Extended User Profile
C fil• Core profile– contains information related to the user
search goals and interests• Extended profile p
– contains information related to the user as a person in order to understand or model the puse that a person will make with the information retrieved
"The best way to predict thefuture is to invent it."Alan Kay 17
Group ProfilesGroup ProfilesA t i t i fil i• A system can maintain a group profile in parallel or instead of user profile
• Could resolve the privacy issue (navigation with group profile)(navigation with group profile)
• Could be use for new group members at h b i ithe beginning
• Could be used in addition to the userCould be used in addition to the user profile to add group “wisdom”
"The best way to predict thefuture is to invent it."Alan Kay 18
Extended ProfileExtended Profile
G l• Goals• Interests• Background:• Preferences• Preferences• Learning styles
"The best way to predict thefuture is to invent it."Alan Kay 19
Who Maintains the Profile?Who Maintains the Profile?• Profile is provided and maintained by• Profile is provided and maintained by
the user/administratorSometimes the only choice– Sometimes the only choice
• The system constructs and updates the fil ( t ti li ti )profile (automatic personalization)
• Collaborative - user and systemy– User creates, system maintains– User can influence and editUser can influence and edit– Does it help or not?
"The best way to predict thefuture is to invent it."Alan Kay 20
Learner Information Package
"The best way to predict thefuture is to invent it."Alan Kay 21
ADAPTATIVITY IN LE
"The best way to predict thefuture is to invent it."Alan Kay 22
Learning Management SystemsLearning Management Systems
• LMSs offer a “personal” learning environment:– registration for courses
personalization of the “workspace”– personalization of the workspace– access to course material– assignments, tests, group work– communication tools: messages, discussion g ,
forums, chat– no built-in adaptive learning functionality
"The best way to predict thefuture is to invent it."Alan Kay
no built in adaptive learning functionality23
Evaluation of adaptativity in LMS by QWS method
Adaptabilité Personnalisation Extensibilité Adaptativité Rang
Valeur maximum * # * *
ATutor | # # | 3
Dokeos | 0 * + 2Dokeos | 0 + 2
dotLRN + + * 0 2
ILIAS + # * 0 2
LON CAPA # # | 2LON‐CAPA + # # | 2
Moodle # + * | 1
OpenUSS # # # 0 2
Sakai 0 0 * 0 3
Spaghettilearning + # + 0 3
"The best way to predict thefuture is to invent it."Alan Kay 24
What can we Adapt to?What can we Adapt to?• Knowledge of the userg
– initialization using stereotypes (beginner, intermediate, expert)– represented in an overlay model of the concept structure of the
applicationapplication– 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– difficult to determine unless it is preset by the user or a workflow system
– goals may change often and more radically than knowledge
"The best way to predict thefuture is to invent it."Alan Kay 25
What can we Adapt to? (cont )What can we Adapt to? (cont.)B k d d i• Background and experience– background = user’s experience outside the application– experience = user’s experience with the application’sexperience user s experience with the application s
hyperspace• Preferences
li i l d f h h b d f– 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 etcwindow size, network bandwidth, processing power, etc.
"The best way to predict thefuture is to invent it."Alan Kay 26
What Do We Adapt in ALE?What Do We Adapt in ALE?
Ad ti t ti• 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 sizeas image or video quality and size• Adaptive navigation:
adapting the link anchors that are shown– adapting the link anchors that are shown– adapting the link destinations– giving “overviews” for navigation support and for– giving overviews for navigation support and for
orientation support
"The best way to predict thefuture is to invent it."Alan Kay 27
What Do We Adapt in ALE?What Do We Adapt in ALE?
Ad ti i t ti• Adaptive interaction:– Answer – question– Nature
• Adaptive communication:– ToolsTools – Use of tools
"The best way to predict thefuture is to invent it."Alan Kay 28
"The best way to predict thefuture is to invent it."Alan Kay 29
Content AdaptationContent AdaptationInserting/removing fragments• Inserting/removing fragments– prerequisite explanations: inserted when the user appears to
need themdditi l l ti dditi l d t il l f– additional explanations: additional details or examples for some
users– comparative explanations: only shown to users who can make
the comparisonthe 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 fragmentsg g
– Can be done to perform relevance ranking for instance
"The best way to predict thefuture is to invent it."Alan Kay 30
Content adaptationContent adaptation
St t ht t• Stretchtext• Dimming fragments
"The best way to predict thefuture is to invent it."Alan Kay 31
Adaptive Navigation SupportAdaptive Navigation Support• Direct guidanceg• Adaptive link • Variant: Adaptive link destinationsp• Adaptive link annotation• Adaptive link hidingdap e d g
"The best way to predict thefuture is to invent it."Alan Kay 32
Connexion à la plateforme Sélection du style
d’apprentissage & des préférences de
l’étudiantÉtudiant
Mise à jour du style et des préférences associés à l’étudiant
VisuelAuditif Kinesthésique
Observation de l’utilisation de la
Actions adaptatives
l’utilisation de la plateforme
Plateforme Adapté au style
Auditif
Plateforme Adapté au style
Visuel
Plateforme Adapté au style kinesthésique
"The best way to predict thefuture is to invent it."Alan Kay
Our work
"The best way to predict thefuture is to invent it."Alan Kay 34
Adapative learning 2.0
"The best way to predict thefuture is to invent it."Alan Kay 35
CAAML /Contact-me
"The best way to predict thefuture is to invent it."Alan Kay 36
L’apprentissage malléable : Concepts de base
ActivitéActivité ApprenantApprenant• Inspiré de la théorie de l’activité : théorie d’étudesl activité : théorie d études socioculturelles
EnvironnementEnvironnement
Contexte Contexte d’interactiond’interaction
"The best way to predict thefuture is to invent it."Alan Kay 37
Le méta-modèle du langage CAAML
class Class Model
ContextAdaptativityCondition Organisation Manifest SmartObject EmbeddedEnvironmentalSensor
CAAML
CoAdaptativityCondition
ActivityAdaptativityCondition
LearningDesignTool MobileDevice
Sensor MobileDeviceSensor1..* 0..*
Global
p y
Prerequisites
Ressource Service
Tool MobileDevice
Caracteristics0..**
1..*
0..*
1..* 0..*
Context
Dynamic
Condition LearningScenario
Objectives Pervasive
Mobile
LearningResource Physical
Software
1..*
uses
*
Person
Context
Static
RolePart
Phase
Learning Coaching
ELearning
LearningResource Physical**
1..*
*
ActivityRole
A ti it St t
ActivityContext+performs
*
*
+using
"The best way to predict thefuture is to invent it."Alan Kay
ActivityStructure
OutcomeNotification+triggers
+creates *
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Le projet ContAct-Me
Contact Me qui est un environnement auteurContact‐Me qui est un environnement auteur dédié à l’apprentissage malléable basé sur le l C d d d llangage CAAML . Et se compose de deux modules de base:
Le modeleur (modélisation et transformation deLe modeleur (modélisation et transformation de modèles) ‐ en design time Le générateur d’applications d’apprentissageLe générateur d applications d apprentissage malléable et simulateur de l’exécution des
i i é li é d i"The best way to predict thefuture is to invent it."Alan Kay 39
activités contextualisées et adaptatives en run time
A c t i v i t éd 'a p p r e n t i s s a g e
A c t i v i t é d ec o a c h i n gS u p p o r t m o b i l e
ContAct-Me
0 . . 1R è g l e d 'i n f é r e n c e1 . . *
C a p t e u r
t y p e : T y p e - c a p t e u r
E l é m e n t c o n t e x t u e ld y n a m i q u e
s e u i _ t o l é r a n c e _ m i n : s t r i n gs e u i _ t o l é r a n c e _ m a x : s t r i n ga d a p t a t i f : b o o l e a nV a l e u r : s t r i n g
E l e m e n t c o n t e x t u e ls t a t i q u e
1 . . *R o l e
G r o u p e
n b r e - m e m b r e : i n t e g e r
R è g l e
S u j e t
E l e m e n t c o n t e x t u e la c t i v i t é
S o u r c e d ' a c q u i s i t i o n
I d : s t r i n gD e s c r i p t i o n : s t r i n g
0 . . *1 . . *
E l e m e n t c o n t e x t u e la p p r e n a n t
1 . . *
W e b S e r v i c e
p a t h : s t r i n g
S c é n a r i o
i d : i n t e g e ri n t i t u l é : s t r i n gd e s c r i p t i o n : s t r i n g
1 . . *1 . . *
1 . . *
1
0 . . *
1 . . *
A c t i v i t é
i d : i i n t e g e rn o m : i n t e g e rD e s c r i p t i o n : s t r i n g
O u t i l O b j e t
1 . . *
1 . . *
1 . . *
1 . . *
0 . . *
O u t i l p h y s i q u e o u t i l m é t h o d o l o g i q u e
E l é m e n t c o n t e x t u e l
n o m : s t r i n gd e s c r i p t i o n : s t r i n g
C o n t e x t
i d : i n t e g e r
0 . . *
R e l a t i o n
R e l a t i o n - t y p e
1 . . * 1 . . *
Méta‐modèle d’activités
Le langage CAAMLModule de
Transformation CAAML /IMS-LD Méta modèle d activités
contextualisées et des règles de co‐adaptativité
Modeleur graphique(GMF GEF et RCP)
Module de réutilisation des modèles
IMS LD
/IMS LD(ATL)
Modèle CAAML
Modèle IMS‐LD (GMF, GEF et RCP) IMS-LD
Module de transformation
CAAML EN modèle IMS-LD
(sans mobilité)
Modèle CAAML
modèle IMS LD étendu
Générateur d’applicationsGénération Générateur d applications d’apprentissage malléable à
partir de modèles
et émulation
d’interfaces mobiles en XHTML-MP
Modèle IMS‐LD étendu (XML)
Un Simulateur de l’exécution de la co-adaptativité entre contexte et application
(DIASIM)Simulation de l’exécution de
la co-
XHTML MP (XSLT,
XHTML-MP)
"The best way to predict thefuture is to invent it."Alan Kay
adaptativitéentre contexte
et activités
Tracks
"The best way to predict thefuture is to invent it."Alan Kay 41
Interrogation du profil LIP suivant un langage de requêtes graphique offrant deslangage de requêtes graphique offrant des fourchettes (date, activités, par un ou
groupe d’apprenants)
"The best way to predict thefuture is to invent it."Alan Kay 424242
Fractal adaptive wsFractal adaptive ws
User model’s adaptation Adaptation layer
Context’s adaptation
Mobility layer An adaptive Composition
Mobility layer
Web serviceWeb service Web service
An adaptive composite Web service
Mobile Web service
Mobile adaptive Web service
"The best way to predict thefuture is to invent it."Alan Kay 43
An adaptive composite Web service
Adaptive scosAdaptive scos
SCO11
SCO12SCO1 ht ?SA2
SCO12
SCO13
SCO1.htm ?Apprenant 2
SA3
Manifest Ressources
Ressources alternatives
SCO11SCO12 SA1
Apprenant 3
Cours
Activité1
Activité2
SCO1.htm
SCO2.doc
s SCO12SCO13
SCO21
Apprenant 1
Activité3 SCO3.pdf SCO22
SCO31
"The best way to predict thefuture is to invent it."Alan Kay 44
SCO32SCO33
SCO34
And …..
"The best way to predict thefuture is to invent it."Alan Kay 45
4 YOUROU
"The best way to predict thefuture is to invent it."Alan Kay 46
Questions [email protected]
"The best way to predict thefuture is to invent it."Alan Kay