Adpative learning environment diffusable
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Adpative learning environment diffusable

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Adpative learning environment diffusable Adpative learning environment diffusable Presentation Transcript

  • Adaptive Learning Environment Mona LAROUSSI"The best way to predict thefuture is to invent it."Alan Kay 1
  • Summary y • The need for adaptation – personalized: adaptable / adaptive / T flexible etc B • L Learner M d li Modeling • Different kind of Adaptation p – adaptive presentation – 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” world one all"The best way to predict thefuture is to invent it."Alan Kay 3
  • But we are not all the same size"The best way to predict thefuture is to invent it."Alan Kay 4
  • Automatic ≠ Adaptive Fixed 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 of the Information Adaptation f th I f ti – 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"The best way to predict thefuture is to invent it."Alan Kay 6
  • Disadvantages of Adaptive Systems • may l learn th wrong b h i the behavior • Adaptive Systems may outsmart the users"The best way to predict thefuture is to invent it."Alan Kay 7
  • Advantages of Adaptive Systems • Increased efficiency: I d ffi i • Return on investment"The best way to predict thefuture is to invent it."Alan Kay 8
  • 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"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 beginning Multi- environne Multi- ment plateform es"The best way to predict thefuture is to invent it."Alan Kay 11
  • Now 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
  • Architecture"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 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 community – Classic - a reference point – Modern - simple form of a user model"The best way to predict thefuture is to invent it."Alan Kay 16
  • Core vs. Extended User Profile vs • C Core profile fil – contains information related to the user search goals and interests • Extended profile p – contains information related to the user as a p person in order to understand or model the use that a person will make with the information retrieved"The best way to predict thefuture is to invent it."Alan Kay 17
  • Group Profiles • A system can maintain a group profile i t i t i fil in parallel or instead of user profile • Could resolve the privacy issue (navigation with group profile) • Could be use for new group members at the b i i h beginning • Could 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 Profile • Goals G l • Interests • Background: • Preferences • Learning styles"The best way to predict thefuture is to invent it."Alan Kay 19
  • Who Maintains the Profile? • Profile is provided and maintained by the user/administrator – Sometimes the only choice • The system constructs and updates the profile ( t fil (automatic personalization) ti li ti ) • Collaborative - user and system y – User creates, system maintains – User 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 Systems • LMSs offer a “personal” learning environment: – registration for courses – personalization of the “workspace” workspace – access to course material – assignments, tests, group work – communication tools: messages, discussion g , forums, chat – no built-in adaptive learning functionality built in"The best way to predict thefuture is to invent it."Alan Kay 23
  • Evaluation of adaptativity in LMS by QWS method Adaptabilité Personnalisation Extensibilité Adaptativité Rang Valeur maximum * # * * ATutor | # # | 3 Dokeos | 0 * + 2 dotLRN + + * 0 2 ILIAS + # * 0 2 LON‐CAPA LON 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? • Knowledge of the user g – 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"The best way to predict thefuture is to invent it."Alan Kay 25
  • What can we Adapt to? (cont ) (cont.) • B k Background and experience d d i – background = user’s experience outside the application – experience = user’s experience with the application’s user s application s hyperspace • Preferences – any explicitly entered aspect of the user that can b used f li i l d f h h be d 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 size, bandwidth, power, etc."The best way to predict thefuture is to invent it."Alan Kay 26
  • What Do We Adapt in ALE? • Ad ti presentation: Adaptive t ti – 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 for overviews orientation support"The best way to predict thefuture is to invent it."Alan Kay 27
  • What Do We Adapt in ALE? • Ad ti i t Adaptive interaction: ti – Answer – question – Nature • Adaptive communication: – Tools – 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 Adaptation • Inserting/removing fragments – prerequisite explanations: inserted when the user appears to need them – additional explanations: additional details or examples for some dditi l l ti dditi l d t il l f 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 g g – Can be done to perform relevance ranking for instance"The best way to predict thefuture is to invent it."Alan Kay 30
  • Content adaptation • St t ht t Stretchtext • Dimming fragments"The best way to predict thefuture is to invent it."Alan Kay 31
  • Adaptive Navigation Support • Direct guidance g • Adaptive link • Variant: Adaptive link destinations p • Adaptive link annotation • Adaptive link hiding dap 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  Mise à jour du  style et des  préférences de  préférences  Étudiant l’étudiant associés à  l’étudiant Auditif Visuel Kinesthésique Observation de  l’utilisation de la  l’utilisation de la plateforme Actions adaptatives Plateforme  Plateforme  Plateforme  Adapté au style  Adapté au style  Adapté au style  Auditif Visuel 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 Activité Apprenant • Inspiré de la théorie de l activité d études l’activité : théorie d’études socioculturelles Environnement Contexte d’interaction"The best way to predict thefuture is to invent it."Alan Kay 37
  • Le méta-modèle du langageclass Class M d o el CAAML S artO m bject E b d m ed edE v n iron entalSens r m o ContextAdaptativityCondition Organisation Manifest Activity daptativity ondition A C Sensor M bileD iceSens o ev or 1..* 0..* LearningDesign C d p oA aptativityC dition y on Tool M bileD o evice 1..* 1..* 0..* Prereq ites uis Global Caracteristics 0..* 0..* * Ressource Service Condition LearningScenario M bile o * uses Software Dynamic Objectives Pervas e iv 1..* Context LearningR ource es P y ic h s al Phase * ELearning * Static 1..* Person Learn g in C hin oac g * RolePart * +using Role +performs Activity Activity ontext C * "The best way to predict the A ti it S ctivityStruc re t tut +creates * future is to invent it." Alan Kay +triggers 38 Notification Outcome
  • Le projet ContAct-Me Contact‐Me qui est un environnement auteur  Contact Me qui est un environnement auteur dédié à l’apprentissage malléable basé sur le  langage CAAML . Et se compose de deux modules  l C d d d l de base: Le modeleur (modélisation et transformation de  Le modeleur (modélisation et transformation de modèles) ‐ en design time  Le générateur d applications d apprentissage  Le générateur d’applications d’apprentissage malléable  et simulateur de l’exécution des  activités contextualisées et adaptatives en run  i ié li é"The best way to predict thefuture is to invent it." d iAlan Kay 39 time
  • ContAct-Me A c tiv i té A c t iv i t é d e d a p p r e n t i s s a g e c o a c h in g S u p p o rt m o b il e S c é n a r io A c t iv it é id : i n t e g e r 1 .. * 1 . .* o u t i l m é th o d o l o g iq u e id :i in te g e r O u t il p h y s iq u e in t i t u lé : s t r in g n o m :in t e g e r d e s c r ip t i o n : s t r in g 1 . .* D e s c r i p t io n : s tr i n g Module de S u je t 1 ..* 1 . .* O u til O b je t 0 ..* R e la t io n 1 ..* R e la t io n - t y p e 1 ..* id : 1 ..* 1 .. * 1 C o n te x t in te g e r 1 ..* R o le E le m e n t c o n t e x t u e l a c tiv i té 1 .. * E le m e n t c o n te x tu e l Le langage CAAML Transformatio E l é m e n t c o n te x tu e l a p p re n a n t n o m : s t r in g d e s c r ip ti o n : s t r in g 1 . .* R è g le 0 .. * 0 ..* E l e m e n t c o n te x tu e l s t a tiq u e G r o u p e n b r e -m e m b r e : in t e g e r R è g le d i n f é r e n c e 0 ..1 n CAAML 1 . .* E lé m e n t c o n te x tu e l W e b S e r v ic e d y n a m iq u e p a t h : s trin g s e u i _ t o lé r a n c e _ m i n : s t r i n g s e u i _ t o lé r a n c e _ m a x : s t r i n g S o u rc e d a c q u is i tio n a d a p t a t if : b o o le a n 1 ..* V a le u r : s t r in g C a p t e u r Id : s t rin g 0 ..* ty p e : T y p e -c a p te u r D e s c r ip t io n : s tr in g /IMS LD /IMS-LD Méta modèle d activités  Méta‐modèle d’activités contextualisées et des  (ATL) règles de co‐adaptativité Module de réutilisation Modèle  Modeleur graphique des modèles CAAMLModèle IMS‐LD IMS-LD IMS LD (sans  (GMF, (GMF GEF et RCP) mobilité) Module de transformation CAAML EN modèle IMS-LD IMS LD étendu Modèle CAAML  Génération Générateur d’applications d applications et émulation d’apprentissage malléable à d’interfaces partir de modèles mobiles en XHTML MP XHTML-MP (XSLT, Un Simulateur de l’exécution Modèle IMS‐LD étendu  (XML) XHTML-MP) de la co-adaptativité entre contexte et application Simulation de (DIASIM) l’exécution de la co-"The best way to predict the adaptativitéfuture is to invent it." entre contexteAlan Kay 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 des  langage 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 42 42
  • Fractal adaptive ws User model’s adaptation Adaptation layer Context’s adaptation Mobility layer Mobility layer An adaptive Composition Web service Web service Web service Mobile adaptive Web service Mobile Web service An adaptive composite Web service"The best way to predict thefuture is to invent it."Alan Kay 43
  • Adaptive scos SCO11 SA2 SCO1.htm SCO1 ht SCO12 ? Apprenant 2 SCO13 SA3 Ressources Apprenant alternatives 3 Manifest Ressource SCO11 SA1 s SCO12 Cours SCO13 Activité1 SCO1.htm Apprenant 1 Activité2 SCO2.doc SCO21 Activité3 SCO3.pdf SCO22 SCO31"The best way to predict the SCO32future is to invent it." SCO33Alan Kay 44 SCO34
  • And ….."The best way to predict thefuture is to invent it."Alan Kay 45
  • 4 YOUR OU"The best way to predict thefuture is to invent it."Alan Kay 46
  • Questions Mona.laroussi@insat.rnu.tn"The best way to predict thefuture is to invent it."Alan Kay