Mash-Up Personal Learning Environments

  • Published on

  • View

  • Download

Embed Size (px)


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
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 (
The Rendering Engine
The Scripting Language
The Prototype
An Example Activity
Sharing Patterns
4. Preliminaries
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.
Learning Environments
= 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
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
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!
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
Learning Design
Adaptive Hypermedia Generators
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)
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
and strategies
plus procedures
of intelligent adaptation applications
Hypertext Structure
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).
29. PLEs
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)
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
32. PLE Implementation Strategies
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)
33. Augmented Landscapes: VLE+PLE
individualsuse subsets oftools and services
providedby institution
actors can choosefrom a growingvariety of options
gradually transcendinstitutional landscape
actors appear asemigrants orimmigrants
leave and joininstitutional landscape for particular purposes
34. EUD
35. End-User Development
Deals with the idea that
design their environments
for the intended usage
Evolve systems from easy to use to easy to develop
For example: Excel Scripting
Forexample: Apple Script
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)
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)
38. End-User Development
Lets activate the long tail of software development: lets develop applications for five users!
39. AT
40. ActivityTheory
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 (Leontev, 1947; Scandinavian AT: Engestrm, 1987)
42. Layers of Interoperability
(Wild, 2007)
43. Web-Application Mash-Up
{ do } { for an output }
share bookmarks
{ using http:// }
RSS feed
summarize papers
find papers
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
45. Mash-Up PLE (MUPPLE)
Set of Web-Based Tools for learning,client-sided aggregation (= web-application mashup)
Recommend tools for specific activities
through design templates
through data mining
Scrutable: give learner full control over learning process
Track learner interaction & usage of tools and refine recommendations
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.
46. Layers of Interoperability (2)
(iX 10/2008, Enterprise Mashups, p. 99)
47. Example Mash-Up PLE
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
51. 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
52. SemanticModel
53. LISL Interpreter
54. LISL Demo Script
1> define actioncompose with urlhttp://[...]?action=create
2> define actionbrowse with urlhttp://[...]/%%peers%%
3> define actionbookmark
4> define actionself-description
5> define object peers with value group_a
6> define object selected descriptions
7> define tool VideoWikiwith url
8> define tool Scuttle with url
9> connect toolVideoWikiwith 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 toolVideoWikito column 1
55. Statements
Support Statements:
Define Statements: useplaceholdersto bind objectvaluesto a url
Lay-Out Interaction Statements:
Connect Action: usingtheFeedBackSpecificationtoconnecttools
Action Statements: Always a naturallanguage sentence:
(I will) browse bookmarksusingscuttle
(Subject) (predicate) (object) (instrument)
56. Side Note: FeedBack
=> Buffered Push
57. Side Note: BlogofolioProcess
58. Prototype
59. Example: Collaborative Paper Writing
60. Sharing Patterns
61. Details: Pattern Sharing
62. Accessibility
A questionofwhichactivityyouwanttopursueandwhatoutcomeyouwanttohave
Not a questionofthetoolyouuse
Patterns canbeadaptedbyexchangingtools
Not everyactivitycanbereplacedlossless
But gracefuldegradationispossible
63. Conclusion
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 todays theory and practice.
... and mash-up personal learning environments are one possible solution for this.
65. EOF. ACK?