Mash-Up Personal Learning Environments


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Talk given at the TENcompetence winter school 2009.

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Mash-Up Personal Learning Environments

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