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

Mash-Up Personal Learning Environments

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Talk given at the TENcompetence winter school 2009.

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Mash-Up Personal Learning EnvironmentsTENcompetence Winter School, February 2nd, 2009, Innsbruck

Fridolin WildVienna University of Economics and Business Administration

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(created with http://www.wordle.net)

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Structure of this Talk Preliminaries 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 Engine The Scripting Language The Prototype An Example Activity Sharing 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 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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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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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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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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Flaws of Personalisation

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Flaws of Personalisation

Claim:

Instructional design theories and

adaptive & 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 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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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) hypermedia Learning Design Adaptive Hypermedia Generators

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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)

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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)

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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 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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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 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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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 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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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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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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Augmented Landscapes: VLE+PLE

individualsuse subsets of

tools and servicesprovided

by institution

actors can choosefrom a growing

variety of options

gradually transcendinstitutional landscape

actors appear asemigrants or

immigrants

leave and joininstitutional 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 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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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 activities through design templates through data mining

Scrutable: give learner full control

over learning process

Track learner interaction

& usage of tools and refine

recommendations

Mash-UP

Personal

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 XoWiki and Prototype Windows library

Combine tool mashup and Wiki content

Provide 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, Recommendations for 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?