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Mash-Up Personal Learning Environments
 

Mash-Up Personal Learning Environments

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Fridolin Wild at TENCompetence Winter School, Innsbruck, February 2009

Fridolin Wild at TENCompetence Winter School, Innsbruck, February 2009

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    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 Wild Vienna University of Economics  and Business Administration
    • (created with http://www.wordle.net) <2>
    • Structure of this Talk Preliminaries Critique: Flaws of Personalisation Problem Personal Learning Environments (F.I) Fundamentals End-User Development (F.II) Activity Theory (F.III) A Mash-Up PLE (MUPPLE.org) Solution The Rendering Engine The Scripting Language The Prototype An Example Activity Sharing Patterns Conclusion <3>
    • Preliminaries <4>
    • Learning Environments = Tools that bring together people and content artefacts in activities that support in constructing and processing information 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. <5>
    • 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 <6>
    • 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 <7>
    • 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! <8>
    • 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 <9>
    • Flaws of Personalisation <10>
    • Flaws of Personalisation Claim: Instructional design theories and adaptive & intelligent technologies do not support or even violate these assumptions! <11>
    • Instr. Des. Theories <12>
    • 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 <13>
    • 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) <14>
    • 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 <15>
    • 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) <16>
    • 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! <17>
    • ADAPTATION TECHN. <18>
    • Adaptation Technologies Varying degree of control: Adaptive ← fluent segue → Adaptable System adapts ← → User adapts (Oppermann, Rashev, & Kinshuk, 1997; Dolog, 2008) Three important streams: Adaptive (educational) hypermedia Learning Design Adaptive Hypermedia Generators <19>
    • 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) <20>
    • 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) <21>
    • 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 <22>
    • Learning Design Services postulated to be known at design time (LD 1.0 has four services!) Services have to be instantiated through formal automated procedures But: Van Rosmalen & Boticario: runtime adaptation (distributed multi-agents added as staff in the aLFanet project) But: Olivier & Tattersall (2005): integrating learning services in the environment section of LD <23>
    • LD continued Targets mainly instructional designers (see guidelines, see practice) But: Olivier & Tattersall (2005) predict application profiles that enhance LD with service provided by particular communities, though interoperability with other players than is no longer given But: Extensions proposed (cf. Vogten et al., 2008): formalisation, reproducability, and reusability of LDs can also be catalyzed through the PCM that facilitates development of learning material through the learners themselves. <24>
    • LD Shortcomings Services != Tools Perceivable surface of a tool makes a difference (cf. e.g. Pituch and Lee (2004): the user interface of tools influences the processes pursued with them Agreement on sharing services can always only be the second step after innovating new services Specifying services at design time is inflexible <25>
    • 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) <26>
    • 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 <27>
    • 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). <28>
    • PLEs <29>
    • 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) <30>
    • 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 <31>
    • PLE Implementation Strategies Coordinated use 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) <32>
    • Augmented Landscapes: VLE+PLE individuals use subsets of tools and services provided by institution actors can choose from a growing variety of options gradually transcend institutional landscape actors appear as emigrants or immigrants leave and join institutional landscape for particular purposes <33>
    • EUD <34>
    • 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 For example: Apple Script <35>
    • 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) <36>
    • 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) <37>
    • End-User Development Let’s activate the long tail of software development: let’s develop applications for five users! <38>
    • AT <39>
    • Activity Theory 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) <40>
    • MUPPLE <41>
    • Layers of Interoperability (Wild, 2007) <42>
    • 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 <43>
    • 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 <44>
    • Mash-Up PLE (MUPPLE) Set of Web-Based Tools for learning, “Mupples were small furry creatures that were imprisoned at the Umboo Lightstation when client-sided aggregation Mungo Baobab, C-3PO and R2-D2 rescued (= ‘web-application mashup’) them. Some considered Mupples a delicacy.” Recommend tools for specific activities -- http://starwars.wikia.com/wiki/Mupple through design templates through data mining Mash-UP Scrutable: give learner full control over learning process Personal Track learner interaction & usage of tools and refine Learning recommendations Environments <45>
    • Layers of Interoperability (2) (iX 10/2008, Enterprise Mashups, p. 99) <46>
    • Example Mash-Up PLE <47>
    • RENDERING <48>
    • Rendering Engine OpenACS module based on XoWiki and Prototype Windows library Combine tool mashup and Wiki content Provide templates for pre-defined learning activities <49>
    • SCRIPTING with LISL <50>
    • LISL Design Decisions Natural Language Like, Learnability learners do not need to know a lot about the syntax Extensibility learners may define and use own actions Semantics, Recommendations for each activity the system offers a landscape of tools Scrutability, Controllability learners receive information about system decisions, but can always change and customize Interoperability, Exchangeability learners can export parts of their ‘learning script’ to hand it over to others Logging tool interactions can be tracked using ‘invisible’ logging commands <51>
    • Semantic Model MUPPLE loves LISL ! <52>
    • LISL Interpreter <53>
    • LISL Demo Script 1> define action compose with url http://[...]?action=create 2> define action browse with url http://[...]/%%peers%% 3> define action bookmark 4> define action ‘self-description’ 5> define object ‘peers’ with value ‘group_a’ 6> define object ‘selected descriptions’ 7> define tool VideoWiki with url http://videowiki.icamp.eu 8> define tool Scuttle with url http://scuttle.icamp.eu 9> connect tool VideoWiki with 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 tool VideoWiki to column 1 <54>
    • Statements Support Statements: ‚Define‘ Statements: use placeholders to bind object values to a url ‚Lay-Out Interaction‘ Statements: ‚Connect‘ Action: using the FeedBack Specification to connect tools ‚Action‘ Statements: Always a natural language ‚sentence‘: (I will) browse bookmarks using scuttle (Subject) (predicate) (object) (instrument) <55>
    • Side Note: FeedBack 1 OFFER REQUEST 2 update notifications 3 NOTIFY => „Buffered Push“ <56>
    • Side Note: Blogofolio Process <57>
    • Prototype mupple.org <58>
    • Example: Collaborative Paper Writing <59>
    • Sharing Patterns <60>
    • Details: Pattern Sharing <61>
    • Accessibility A question of which activity you want to pursue and what outcome you want to have Not a question of the tool you use Patterns can be adapted by exchanging tools Not every activity can be replaced lossless But graceful degradation is possible <62>
    • Conclusion <63>
    • 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. <64>
    • EOF. ACK? <65>