The document discusses flaws in personal learning environments and existing adaptive technologies, and proposes a mash-up personal learning environment (MUPPLE) that allows learners to combine various web tools and services to design their own learning environment through a scripting language called LISL. MUPPLE includes a rendering engine to combine tools and wiki content, and the LISL scripting language is designed to be natural language-like and give learners full control over customizing their learning process.

1. Mash‐Up Personal Learning
Environments
TENcompetence Winter School,
February 2nd, 2009, Innsbruck
Fridolin Wild
Vienna University of Economics
and Business Administration

2. (created with http://www.wordle.net)
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3. 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
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4. Preliminaries
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5. 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.
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6. 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
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7. 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
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8. 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!
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9. 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
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10. Flaws of Personalisation
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11. Flaws of Personalisation
Claim:
Instructional design theories and
adaptive & intelligent technologies
do not support or even violate
these assumptions!
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12. Instr. Des. Theories
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13. 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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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. Searle’s Chinese Room)
(from: modernlove.comicgenesis.com)
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15. 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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16. 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)
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17. 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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18. ADAPTATION TECHN.
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19. 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
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20. 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>

21. 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>

22. 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
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23. 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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24. 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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25. 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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26. 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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27. 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
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28. 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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29. PLEs
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30. 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)
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31. 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
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32. 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)
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33. 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
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34. EUD
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35. 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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36. 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>

37. 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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38. End-User Development
Let’s activate the long tail of
software development:
let’s develop applications
for five users!
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39. AT
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40. 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)
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41. MUPPLE
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42. Layers of Interoperability
(Wild, 2007)
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43. 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>

44. 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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45. 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
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46. Layers of Interoperability (2)
(iX 10/2008, Enterprise Mashups, p. 99)
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47. Example Mash-Up PLE
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48. RENDERING
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49. Rendering Engine
OpenACS module
based on XoWiki
and Prototype
Windows library
Combine tool
mashup and Wiki
content
Provide templates
for pre-defined
learning activities
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50. SCRIPTING with LISL
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51. 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
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52. Semantic Model
MUPPLE
loves LISL !
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53. LISL Interpreter
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54. 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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55. 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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56. Side Note: FeedBack
1 OFFER
REQUEST
2 update
notifications
3 NOTIFY
=> „Buffered Push“
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57. Side Note: Blogofolio Process
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58. Prototype mupple.org
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59. Example:
Collaborative Paper Writing
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60. Sharing Patterns
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61. Details: Pattern Sharing
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62. 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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63. Conclusion
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64. 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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65. EOF. ACK?
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