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Adaptive Learning Environments

Adaptive Learning Environments. Prof. dr. Paul De Bra Eindhoven University of Technology. 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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Adaptive Learning Environments

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

  2. Topics • The need for adaptation • personalized: adaptable / adaptive • User Modeling • Adaptation • adaptive presentation • adaptive navigation • Authoring • Examples (if we have time) TEL & KM Summerschool 2008

  3. We live in a “one size fits all” world But we are not all the same size(physically or mentally) TEL & KM Summerschool 2008

  4. What’s the main difference between these pictures? TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 ofadvertising that really works • adaptive (non-IS) systems have better technical performance TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  9. User-Adaptive Systems TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  11. 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. TEL & KM Summerschool 2008

  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. TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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. TEL & KM Summerschool 2008

  16. User Modeling TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 ofarbitrary relationships TEL & KM Summerschool 2008

  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 fromknowledge from test, activities, etc. TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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) TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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-adaptationdo not necessary leading to overall adaptationthat makes sense TEL & KM Summerschool 2008

  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 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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 theirUM data TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  32. Adaptation TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  34. Adaptive Presentation TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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) TEL & KM Summerschool 2008

  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. … TEL & KM Summerschool 2008

  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. TEL & KM Summerschool 2008

  39. Example with group adaptation: Intrigue (adaptive tourist guide) TEL & KM Summerschool 2008

  40. Stretchtext example:the Push system TEL & KM Summerschool 2008

  41. Scaling-based Adaptation TEL & KM Summerschool 2008

  42. Adaptive Navigation Support TEL & KM Summerschool 2008

  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 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” TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  46. Example of Direct Guidance • Simple: suggest one best page to go to • Webwatcher:curious eyes • Sometimes a“next” button • Popular in ITS(sequencing) TEL & KM Summerschool 2008

  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 TEL & KM Summerschool 2008

  48. Example:Link Annotation in ELM-ART TEL & KM Summerschool 2008

  49. Example:link annotation in Interbook 4 3 2 √ 1 3. Current section state 4. Linked sections state 1. Concept role 2. Current concept state TEL & KM Summerschool 2008

  50. Example:Link Annotation in ISIS-Tutor TEL & KM Summerschool 2008

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