• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Mash-Up Personal Learning Environments
 

Mash-Up Personal Learning Environments

on

  • 882 views

Talk given at the TENcompetence winter school 2009.

Talk given at the TENcompetence winter school 2009.

Statistics

Views

Total Views
882
Views on SlideShare
816
Embed Views
66

Actions

Likes
1
Downloads
12
Comments
0

3 Embeds 66

http://dws-table.bhptemp.intermesh.net 63
http://dev.teleurope.eu 2
http://universe.stellarnet.eu 1

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Mash-Up Personal Learning Environments Mash-Up Personal Learning Environments Presentation Transcript

    • Mash-Up Personal Learning Environments
      TENcompetence Winter School, February 2nd, 2009, Innsbruck
      Fridolin WildVienna University of Economics and Business Administration
    • (createdwith http://www.wordle.net)
    • Structure of this Talk
      Preliminaries
      Critique: Flaws of Personalisation
      Personal Learning Environments (F.I)
      End-User Development (F.II)
      Activity Theory (F.III)
      A Mash-UpPLE (MUPPLE.org)
      The Rendering Engine
      The Scripting Language
      The Prototype
      An Example Activity
      Sharing Patterns
      Conclusion
      Problem
      Fundamentals
      Solution
    • Preliminaries
    • ... 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.
      Learning Environments
      = Toolsthat bring together people and content artefacts in activities that support in constructing and processing information and knowledge.
    • Assumption I.
      Learning environments are inherently networks:
      encompass actors, artefacts, and tools
      in various locations
      with heterogeneous affiliations, purposes, styles, objectives, etc.
      Network effects make the network exponentially more valuable with its growing size
    • Assumption II.
      Learning Environments are learning outcomes!
      not an instructional control condition!
      For example, a learner may prefer to email an expert instead of reading a paper: Adaptation strategies go beyond navigational adaptation through content artefacts
      Setting up and maintaining a learning environment is part of the learning work: future experiences will be made through and with it
      knowing tools, people, artefacts, and activities (=LE) enables
    • Assumption III.
      Learning to learn, while at the same time learning content is better than just (re-) constructing knowledge.
      Acquisition of rich professional competences such as social, self, and methodological competence
      ... is superior to only acquiring content competence (i.e. Domain-specific skill, facts, rules, ...)
      Due to ever decreasing half-life of domain-specific knowledge
      Construction != Transfer!
    • Assumption IV.
      Designing for Emergence
      ... is more powerful than programming by instruction
      Emergent behaviour:
      observable dynamics show unanticipated activity
      Surprising: the participating systems have not been instructed to do so specifically (may even not have intended it)
      Why? Because models involved are simpler while achieving the same effect
      Example: Walking Robot
    • Flaws of Personalisation
    • Flaws of Personalisation
      Claim:
      Instructional design theories and
      adaptive & intelligent technologies
      do not support or even violate these assumptions!
    • Instr. Des. Theories
    • Instructional Design Theories
      ... offer explicit guidance to help people learn better
      But: Environment = instructional control condition (cf. e.g. Reigeluth, 1999)
      But: Environment = separate from desired 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
    • 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. Searle’s Chinese Room)
      (from: modernlove.comicgenesis.com)
    • Weak AI Position
      mixture of minor automatic system adaptations along a coarse-grain instructional design master plan engineered by a teacher or instructional designer
      Learning-paths are fine-tuned along learner characteristics and user profiles to conform to trails envisioned (not necessarily proven) by teachers
      But: No perfect instructional designer
      In fact: most instructors are only domain-experts, not didactical ones
    • Weak AI Position (2)
      Furthermore: planned adaptation takes away experiences from the learner:
      External planning reduces challenges
      Thus reduces chances to become competent
      Learners are not only sense-makers instructed by teachers along a predefined path
      Learners need to actively adapt their learning environments
      so that they can construct the rich professional competences necessary for successful learning (cf. Rychen & Salganik, 2003)
    • Instructional Design Theories
      Locus 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!
    • ADAPTATION TECHN.
    • Adaptation Technologies
      Varying degree of control:
      Adaptive ←‒‒‒‒ fluent segue ‒‒‒‒-> AdaptableSystem adapts ←‒‒‒‒‒‒‒‒‒‒-> User adapts(Oppermann, Rashev, & Kinshuk, 1997; Dolog, 2008)
      Three important streams:
      Adaptive (educational) hypermedia
      Learning Design
      Adaptive Hypermedia Generators
    • Adaptive (Educational) Hypermedia
      Generic Types:
      adaptive navigation support: path and link adaptation
      adaptive presentation support: presentation of a content subset in new arrangements
      Education Specific Types:
      Sequencing: adaptation of the navigation path through pre-existing learning material
      Problem-solving: evaluate the student created content summatively or formatively through the provision of feedback, etc.
      Student Model Matching:collaborative filtering to identfy matching peers or identify differences
      (Brusilovsky, 1999)
    • Adaptive (Educational) Hypermedia
      Main Problems of AEH:
      Lack of reusability and interoperability
      Missing standards for adaptation interoperability
      primarily navigation through content (=represented domain-specific knowledge)
      Processing and construction activities not in focus
      Environments are not outcomes, do not support environment design
      (cf. Henze & Brusilovsky, 2007; Holden & Kay, 1999; Kravcik, 2008; Wild, 2009)
    • Learning Design
      Koper & Tattersell (2005): learning design = instructional design
      Specht & Burgos (2007): adaptation possibilities within IMS-LD:
      Only pacing, content, sequencing, and navigational aspects
      environment is no generic component that can be adapted (or tools/functions/services), nor driving factor for informaiton gathering nor method for adaptation
      Towle and Halm (2005): embedding adaptive strategies in units of learning
    • Learning Design
      Services postulatedtobeknownat design time (LD 1.0 hasfourservices!)
      Services havetobeinstantiatedthrough formal automatedprocedures
      But: Van Rosmalen & Boticario: runtime adaptation (distributed multi-agents added as staff in the aLFanet project)
      But: Olivier & Tattersall (2005): integratinglearningservices in theenvironmentsectionof LD
    • LD continued
      Targets mainlyinstructionaldesigners(seeguidelines, seepractice)
      But: Olivier & Tattersall (2005) predictapplicationprofilesthatenhance LD withserviceprovidedbyparticularcommunities, thoughinteroperabilitywithotherplayersthanisnolongergiven
      But: Extensionsproposed (cf. Vogten et al., 2008): formalisation, reproducability, andreusabilityof LDs can also becatalyzedthroughthe PCM thatfacilitatesdevelopmentoflearning material throughthelearnersthemselves.
    • LD Shortcomings
      Services != Tools
      Perceivablesurfaceof a toolmakes a difference (cf. e.g. Pituchand Lee (2004): theuserinterfaceoftoolsinfluencestheprocessespursuedwiththem
      Agreement on sharingservicescanalwaysonlybethesecondstep after innovatingnewservices
      Specifyingservicesat design time is inflexible
    • Adaptive Hypermedia Generators
      LAG: language for expressing information on
      assembly,
      adaptation
      and strategies
      plus procedures
      of intelligent adaptation applications
      Hypertext Structure
      Rule-based path adaptation
      (Cristea, Smits, & De Bra, 2007)
    • Adaptive Hypermedia Generators
      WebML + UML-Guide:
      client-side adaptation of web applications (Ceri et al., 2005)
      WebML: follows hypertext model
      UML-Guide (modified state diagrammes): user navigation through a system can be modelled
      Both together can generate personalised apps
      But: restricted to content and path design,
      And: expert designer recommended
    • Summary of the Critique
      The prevailing paradigm is ‚rule‘, not ‚environment‘!
      Learners are executing along minor adaptations what instructional designers (mostly teachers) have foreseen.
      No real support for learning environment design (= constructing and maintaining learning environments).
    • PLEs
    • Personal Learning Environments
      Not yet a theory and no longer a movement
      In the revival of the recent years: starting as opposition to learning management systems
      Common ground:
      all projects envision an empowered learner capable of self-direction for whom tightly- and loosely-coupled tools facilitate the process of defining outcomes, planning their achievement, conducting knowledge construction, and regulating plus assessing(van Harmelen, 2008)
    • History of PLEs
      Early Work: Focus on user- and conversation centred perspective (Liber, 2000; Kearney et al., 2005)
      personal space
      used for developmental planning
      and aggregating navigational as well as conversational traces
      Next Phase: interoperability issues (RSS/ATOM, service integration via APIs, …) (Downes, 2005; Wilson, 2005; Wilson, 2005; Wilson et al., 2007)
      Today: heterogeneous set of implementation strategies
    • PLE Implementation Strategies
      Coordinateduse
      e.g. with the help of browser bookmarks to involved web apps
      Simple connectors for data exchange and service interoperability
      Abstracted, generalised connectors that form so-called conduits
      e.g. those supported by the social browser Flock
      e.g. by the service-oriented PLE Plex
      (Wilson et al., 2007)
    • Augmented Landscapes: VLE+PLE
      individualsuse subsets oftools and services
      providedby institution
      actors can choosefrom a growingvariety of options
      gradually transcendinstitutional landscape
      actors appear asemigrants orimmigrants
      leave and joininstitutional landscape for particular purposes
    • EUD
    • End-User Development
      Deals with the idea that
      end-users
      design their environments
      for the intended usage
      Evolve systems from ‘easy to use’ to ‘easy to develop’
      For example: Excel Scripting
      Forexample: Apple Script
    • End-User Development
      Shifting the locus of control from developer to (power) user
      Coming from modern project management and software development methods (agile, XP, ...)
      Via User-centred design from HCI: dates back at least to the 1970ies: dedicates extensive attention to the user in each step of the design process, but no development
      ... and a rather recent research stream (cf. Lieberman et al., 2006)
    • Mash-Up?
      The ‘Frankensteining’ of software artefacts and data
      Opportunistic Design (Hartmann et al., 2008; Ncube et al., 2008)
      ‘Excel Scripting for the Web’
      Various Strategies (cf. Gamble & Gamble, 2008)
    • End-User Development
      Let’s activate the long tail of software development: let’s develop applications for five users!
    • AT
    • ActivityTheory
      Structuring Change with Activities
      Activity is shaped by surroundings
      E.g. tools have affordances (like a door knob lends itself to opening)
      Activity shapes surroundings!
      Activities can result in construction of a tool
      Long tradition (Leont’ev, 1947; Scandinavian AT: Engeström, 1987)
    • MUPPLE
    • Layers of Interoperability
      (Wild, 2007)
    • Web-Application Mash-Up
      { do s.th. } { for an output }
      share bookmarks
      { using http://… }
      using distance.ktu.lt/scuttle
      RSS feed
      summarize papers
      using teldev.wu-wien.ac.at/xowiki
      find papers
      using www.objectspot.org
    • Mash-Up PLE (MUPPLE)
      PLE: change in perspective, putting the learner centre stage again, empower learners to construct and maintain their learning environment
      Mash-Ups: Frankensteining of software artefacts and data
    • Mash-Up PLE (MUPPLE)
      Set of Web-Based Tools for learning,client-sided aggregation (= ‘web-application mashup’)
      Recommend tools for specific activities
      through design templates
      through data mining
      Scrutable: give learner full control over learning process
      Track learner interaction & usage of tools and refine recommendations
      “Mupples were small furry creatures that were imprisoned at the Umboo Lightstation when Mungo Baobab, C-3PO and R2-D2 rescued them. Some considered Mupples a delicacy.”
      -- http://starwars.wikia.com/wiki/Mupple
      Mash-UP
      Personal
      Learning
      Environments
    • Layers of Interoperability (2)
      (iX 10/2008, Enterprise Mashups, p. 99)
    • Example Mash-Up PLE
    • RENDERING
    • Rendering Engine
      OpenACS module based on XoWiki and Prototype Windows library
      Combine tool mashup and Wiki content
      Provide templates for pre-defined learning activities
    • SCRIPTING with LISL
    • LISL Design Decisions
      Natural Language Like, Learnabilitylearners do not need to know a lot about the syntax
      Extensibilitylearners may define and use own actions
      Semantics, Recommendationsfor each activity the system offers a landscape of tools
      Scrutability, Controllabilitylearners receive information about system decisions,but can always change and customize
      Interoperability, Exchangeabilitylearners can export parts of their ‘learning script’ to hand it over to others
      Loggingtool interactions can be tracked using ‘invisible’ logging commands
    • SemanticModel
      MUPPLE loves LISL !
    • LISL Interpreter
    • LISL Demo Script
      1> define actioncompose with urlhttp://[...]?action=create
      2> define actionbrowse with urlhttp://[...]/%%peers%%
      3> define actionbookmark
      4> define action‘self-description’
      5> define object ‘peers’ with value ‘group_a’
      6> define object ‘selected descriptions’
      7> define tool VideoWikiwith urlhttp://videowiki.icamp.eu
      8> define tool Scuttle with urlhttp://scuttle.icamp.eu
      9> connect toolVideoWikiwith tool Scuttle
      10> compose object ‘self-description’ using tool VideoWiki
      11> browse object ‘peers’ using tool VideoWiki
      12> bookmark object ‘selected descriptions’ using tool VideoWiki
      13> drag toolVideoWikito column 1
    • Statements
      Support Statements:
      ‚Define‘ Statements: useplaceholdersto bind objectvaluesto a url
      ‚Lay-Out Interaction‘ Statements:
      ‚Connect‘ Action: usingtheFeedBackSpecificationtoconnecttools
      ‚Action‘ Statements: Always a naturallanguage ‚sentence‘:
      (I will) browse bookmarksusingscuttle
      (Subject) (predicate) (object) (instrument)
    • Side Note: FeedBack
      OFFER
      1
      REQUEST
      update
      notifications
      2
      NOTIFY
      3
      => „Buffered Push“
    • Side Note: BlogofolioProcess
    • Prototype mupple.org
    • Example: Collaborative Paper Writing
    • Sharing Patterns
    • Details: Pattern Sharing
    • Accessibility
      A questionofwhichactivityyouwanttopursueandwhatoutcomeyouwanttohave
      Not a questionofthetoolyouuse
      Patterns canbeadaptedbyexchangingtools
      Not everyactivitycanbereplacedlossless
      But gracefuldegradationispossible
    • Conclusion
    • Conclusion
      Learning environments and their construction as well as maintenance makes up a crucial part of the learning process and the desired learning outcomes.
      Learning environment design is the key to solve shortcomings of today’s theory and practice.
      ... and mash-up personal learning environments are one possible solution for this.
    • EOF. ACK?