Talk given at the TENcompetence winter school 2009.
- 1.Mash-Up Personal Learning Environments
TENcompetence Winter School, February 2nd, 2009, Innsbruck
Fridolin WildVienna University of Economics and Business Administration
2. (createdwith http://www.wordle.net)
3. Structure of this Talk
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
An Example Activity
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.
= Toolsthat bring together people and content artefacts in activities that support in constructing and processing information and knowledge.
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
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
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!
9. Assumption IV.
Designing for Emergence
... is more powerful than programming by instruction
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
10. Flaws of Personalisation
11. Flaws of Personalisation
Instructional design theories and
adaptive & intelligent technologies
do not support or even violate these assumptions!
12. Instr. Des. Theories
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
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. Searles Chinese Room)
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
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)
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!
18. ADAPTATION TECHN.
19. 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
Adaptive Hypermedia Generators
20. Adaptive (Educational) Hypermedia
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
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)
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
23. 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
24. 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.
25. 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
26. Adaptive Hypermedia Generators
LAG: language for expressing information on
of intelligent adaptation applications
Rule-based path adaptation
(Cristea, Smits, & De Bra, 2007)
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
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).
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
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)
31. History of PLEs
Early Work: Focus on user- and conversation centred perspective(Liber, 2000; Kearney et al., 2005)
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., 2