Mash-Up Personal Learning Environments

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Fridolin Wild at TENCompetence Winter School, Innsbruck, February 2009

Text of Mash-Up Personal Learning Environments

  • 1.MashUpPersonalLearning Environments TENcompetence WinterSchool, February2nd,2009,Innsbruck FridolinWild ViennaUniversityofEconomics andBusinessAdministration

2. (created with 3. Structure of this Talk Preliminaries Critique: Flaws of PersonalisationProblem Personal Learning Environments (F.I) Fundamentals End-User Development (F.II) Activity Theory (F.III) A Mash-Up PLE ( SolutionThe Rendering EngineThe Scripting LanguageThe PrototypeAn Example ActivitySharing Patterns Conclusion 4. Preliminaries 5. Learning Environments= Tools that bring together people andcontent artefacts in activities thatsupport in constructing and processinginformation and knowledge. ... Are probably around us ever since the homo habilis started to use more sophisticated stone tools at the beginning of the Pleistocene some two million years ago. 6. Assumption I. Learning environments are inherently networks:encompass actors, artefacts, and toolsin various locationswith heterogeneous affiliations,purposes, styles, objectives, etc.Network effects make the network exponentiallymore valuable with its growing size 7. Assumption II. Learning Environmentsare learning outcomes!not an instructional control condition!For example, a learner may prefer to email an expertinstead of reading a paper: Adaptation strategies gobeyond navigational adaptation through content artefactsSetting up and maintaining a learning environmentis part of the learning work: future experiences will bemade through and with itknowing tools, people, artefacts, and activities (=LE)enables 8. Assumption III. Learning to learn, while at the same time learningcontent is better than just (re-) constructingknowledge.Acquisition of rich professional competences such associal, self, and methodological competence... is superior to only acquiring content competence(i.e. Domain-specific skill, facts, rules, ...)Due to ever decreasing half-lifeof domain-specific knowledgeConstruction != Transfer! 9. Assumption IV. Designing for Emergence... is more powerful than programming by instructionEmergent behaviour:observable dynamics show unanticipated activitySurprising: the participating systems have not beeninstructed to do so specifically (may even not haveintended it)Why? Because models involved are simpler whileachieving the same effectExample: Walking Robot 10. Flaws of Personalisation 11. Flaws of Personalisation Claim: Instructional design theories andadaptive & intelligent technologiesdo not support or even violatethese assumptions! 12. Instr. Des. Theories 13. Instructional Design Theories ... offer explicit guidance to help people learn betterBut: Environment = instructional control condition(cf. e.g. Reigeluth, 1999)But: Environment = separate fromdesired learning outcomes (cf. e.g. Reigeluth, 1999)Even in constructivist instructional theories:LE is created by instructional designer(cf. e.g. Mayer, 1999; Jonassen, 1999)Appear in applied research in two flavours:with and without strong AI component 14. Strong AI Position= system intelligence monitors, diagnoses, and guides automatically Inherently ill-defined: cannot monitor everything Constantly overwhelmed: what is relevant Computationally expensive: or even impossible Even if: no understanding (cf. Searles Chinese Room) (from: 15. Weak AI Position mixture of minor automatic system adaptationsalong a coarse-grain instructional design masterplan engineered by a teacher or instructionaldesignerLearning-paths are fine-tuned along learnercharacteristics and user profiles to conform to trailsenvisioned (not necessarily proven) by teachersBut: No perfect instructional designerIn fact: most instructors are only domain-experts,not didactical ones 16. Weak AI Position (2) Furthermore: planned adaptation takes awayexperiences from the learner:External planning reduces challengesThus reduces chances to become competentLearners are not only sense-makersinstructed by teachers along a predefined pathLearners need to actively adapttheir learning environmentsso that they can construct the rich professionalcompetences necessary for successful learning(cf. Rychen & Salganik, 2003) 17. Instructional Design TheoriesLocus of control only with the instructional designer or with the system Not (not even additionally) with the learner But: Learners are not patients that need an aptitude treatment. => Shortcoming of ID Theory! 18. ADAPTATION TECHN. 19. Adaptation Technologies Varying degree of control:Adaptive fluent segue AdaptableSystem adapts User adapts(Oppermann, Rashev, & Kinshuk, 1997; Dolog, 2008) Three important streams:Adaptive (educational) hypermediaLearning DesignAdaptive Hypermedia Generators 20. Adaptive (Educational) HypermediaGeneric Types:adaptive navigation support: path and link adaptationadaptive presentation support: presentation of acontent subset in new arrangementsEducation Specific Types:Sequencing: adaptation of the navigation paththrough pre-existing learning materialProblem-solving: evaluate the student createdcontent summatively or formatively through theprovision of feedback, etc.Student Model Matching: collaborative filtering toidentfy matching peers or identify differences (Brusilovsky, 1999) 21. Adaptive (Educational) Hypermedia Main Problems of AEH:Lack of reusability and interoperabilityMissing standards foradaptation interoperabilityprimarily navigation through content(=represented domain-specific knowledge)Processing and construction activitiesnot in focusEnvironments are not outcomes,do not support environment design (cf. Henze & Brusilovsky, 2007; Holden & Kay, 1999; Kravcik, 2008; Wild, 2009) 22. Learning DesignKoper & Tattersell (2005):learning design = instructional designSpecht & Burgos (2007):adaptation possibilities within IMS-LD:Only pacing, content, sequencing,and navigational aspectsenvironment is no generic component that can beadapted (or tools/functions/services), nor drivingfactor for informaiton gathering nor method foradaptationTowle and Halm (2005): embedding adaptivestrategies in units of learning 23. Learning Design Services postulated to be known at design time(LD 1.0 has four services!)Services have to be instantiated through formalautomated proceduresBut: Van Rosmalen & Boticario:runtime adaptation (distributed multi-agents addedas staff in the aLFanet project)But: Olivier & Tattersall (2005): integrating learningservices in the environment section of LD 24. LD continued Targets mainly instructional designers(see guidelines, see practice)But: Olivier & Tattersall (2005) predict applicationprofiles that enhance LD with service provided byparticular communities, though interoperability withother players than is no longer givenBut: Extensions proposed (cf. Vogten et al., 2008):formalisation, reproducability, and reusability of LDscan also be catalyzed through the PCM thatfacilitates development of learning material throughthe learners themselves. 25. LD Shortcomings Services != ToolsPerceivable surface of a tool makes a difference (cf.e.g. Pituch and Lee (2004): the user interface oftools influences the processes pursued with themAgreement on sharing services can always only bethe second step after innovating new servicesSpecifying services at design time is inflexible 26. Adaptive Hypermedia Generators LAG: language for expressing information onassembly,adaptationand strategiesplus proceduresof intelligent adaptation applicationsHypertext StructureRule-based path adaptation(Cristea, Smits, & De Bra, 2007) 27. Adaptive Hypermedia Generators WebML + UML-Guide:client-side adaptation of web applications (Ceri et al.,2005)WebML: follows hypertext modelUML-Guide (modified state diagrammes): usernavigation through a system can be modelledBoth together can generate personalised appsBut: restricted to content and path design,And: expert designer recommended 28. Summary of the CritiqueThe prevailing paradigm isrule, not environment!Learners are executing along minor adaptationswhat instructional designers (mostly teachers)have foreseen.No real support for learning environment design (=constructing and maintaining learningenvironments). 29. PLEs 30. Personal Learning Environments Not yet a theory and no longer a movementIn the revival of the recent years: starting asopposition to learning management systemsCommon ground:all projects envision an empowered learner capableof self-direction for whom tightly- and loosely-coupledtools facilitate the process of defining outcomes,planning their achievement, conducting knowledgeconstruction, and regulating plus assessing(van Harmelen, 2008) 31. History of PLEsEarly Work: Focus on user- and conversationcentred perspective (Liber, 2000; Kearney et al.,2005)personal spaceused for developmental planningand aggregating navigational as well asconversational tracesNext Phase: interoperability issues (RSS/ATOM,service integration via APIs, ) (Downes, 2005;Wilson, 2005; Wilson, 2005; Wilson et al., 2007)Today: heterogeneous set of implementationstrategies 32. PLE Implementation Strategies Coordinated usee.g. with the help of browserbookmarks to involved web appsSimple connectors for data exchangeand service interoperabilityAbstracted, generalised connectors that form so-called conduitse.g. those supported by the social browser Flocke.g. by the service-oriented PLE Plex (Wilson et al., 2007) 33. Augmented Landscapes: VLE+PLE individualsuse subsets oftools and servicesprovided by institutionactors can choose from a growingvariety of optionsgradually transcend institutional landscape actors appear as emigrants orimmigrants leave and join institutional landscape for particular purposes 34. EUD 35. End-User Development Deals with the idea thatend-usersdesign their environmentsfor the intended usageEvolve systems from easy to useto easy to developFor example: Excel ScriptingFor example: Apple