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Fridolin Wild at TENCompetence Winter School, Innsbruck, February 2009
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Mash‐Up Personal Learning Environments
TENcompetence
Winter School, February 2nd, 2009, Innsbruck
Fridolin Wild
Vienna University of Economics
and Business Administration
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(created with http://www.wordle.net)
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Structure of this TalkPreliminaries
Critique: Flaws of Personalisation
Personal Learning Environments (F.I)
End-User Development (F.II)
Activity Theory (F.III)
A Mash-Up PLE (MUPPLE.org)The Rendering EngineThe Scripting LanguageThe PrototypeAn Example ActivitySharing Patterns
Conclusion
Problem
Fundamentals
Solution
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Preliminaries
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... 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= Tools that bring together people and content artefacts in activities that support in constructing and processing information and knowledge.
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Assumption I.
Learning environments are inherently networks:encompass actors, artefacts, and toolsin various locations with heterogeneous affiliations, purposes, styles, objectives, etc.
Network effects make the network exponentially more valuable with its growing size
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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 artefactsSetting up and maintaining a learning environment is part of the learning work: future experiences will be made through and with itknowing tools, people, artefacts, and activities (=LE) enables
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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 knowledgeConstruction != Transfer!
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Assumption IV.
Designing for Emergence... is more powerful than programming by instruction
Emergent behaviour: observable dynamics show unanticipated activitySurprising: 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
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Flaws of Personalisation
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Flaws of Personalisation
Claim:
Instructional design theories andadaptive & intelligent technologies do not support or even violate these assumptions!
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Instr. Des. Theories
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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
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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)
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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
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Weak AI Position (2)
Furthermore: planned adaptation takes away experiences from the learner:
External planning reduces challengesThus reduces chances to become competentLearners are not only sense-makers instructed by teachers along a predefined pathLearners need to actively adapt their learning environments so that they can construct the rich professional competences necessary for successful learning (cf. Rychen & Salganik, 2003)
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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!
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ADAPTATION TECHN.
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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) hypermediaLearning DesignAdaptive Hypermedia Generators
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Adaptive (Educational) Hypermedia
Generic Types: adaptive navigation support: path and link adaptationadaptive presentation support: presentation of a content subset in new arrangements
Education Specific Types:Sequencing: adaptation of the navigation path through pre-existing learning materialProblem-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)
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Adaptive (Educational) Hypermedia
Main Problems of AEH:Lack of reusability and interoperabilityMissing standards for adaptation interoperabilityprimarily navigation through content (=represented domain-specific knowledge)Processing and construction activities not in focusEnvironments are not outcomes, do not support environment design
(cf. Henze
& Brusilovsky, 2007; Holden & Kay, 1999; Kravcik, 2008; Wild, 2009)
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Learning Design
Koper & Tattersell (2005): learning design = instructional design
Specht & Burgos (2007): adaptation possibilities within IMS-LD:
Only pacing, content, sequencing, and navigational aspectsenvironment 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
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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
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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.
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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
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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)
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Adaptive Hypermedia Generators
WebML + UML-Guide: client-side adaptation of web applications (Ceri et al., 2005)WebML: follows hypertext modelUML-Guide (modified state diagrammes): user navigation through a system can be modelledBoth together can generate personalised apps
But: restricted to content and path design,
And: expert designer recommended
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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).
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PLEs
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Personal Learning Environments
Not yet a theory and no longer a movementIn 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)
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History of PLEsEarly 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
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PLE Implementation Strategies
Coordinated usee.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)
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Augmented Landscapes: VLE+PLE
individuals use subsets of
tools and servicesprovided
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
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EUD
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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
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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)
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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)
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End-User Development
Let’s activate the long tail of software development: let’s develop applications for five users!
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AT
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Activity Theory
Structuring Change with Activities
Activity is shaped by surroundingsE.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)
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MUPPLE
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Layers of Interoperability
(Wild, 2007)
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Web-Application Mash-Up
using distance.ktu.lt/scuttle
using teldev.wu-wien.ac.at/xowiki
using www.objectspot.org
{ using http://… }share bookmarks
find papers
summarize papers
{ do s.th. } { for an output }
RSS feed
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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
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Mash-Up PLE (MUPPLE)Set of Web-Based Tools for learning,client-sided aggregation (= ‘web-application mashup’)
Recommend tools for specific activitiesthrough design templates
through data mining
Scrutable: give learner full control over learning process
Track learner interaction & usage of tools and refine recommendations
Mash-UPPersonal
Learning
Environments
“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
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Layers of Interoperability (2)
(iX
10/2008, Enterprise Mashups, p. 99)
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Example Mash-Up PLE
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RENDERING
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Rendering Engine
OpenACS module based on XoWikiand Prototype Windows libraryCombine tool mashup and Wiki contentProvide templates for pre-defined learning activities
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SCRIPTING with LISL
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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
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Semantic Model
MUPPLE loves LISL !
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LISL Interpreter
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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
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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)
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Side Note: FeedBack
1 OFFER
2REQUESTupdate notifications
3 NOTIFY
=> „Buffered Push“
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Side Note: Blogofolio Process
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Prototype mupple.org
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Example: Collaborative Paper Writing
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Sharing Patterns
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Details: Pattern Sharing
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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
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Conclusion
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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.
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EOF. ACK?
