ECEL 2009 Keynote J. Minguillón

Learner-centered learning
object repositories:
personalization and
interaction issues
Julià Minguillón
Universitat Oberta de Catalunya

ECEL 2009, 8th European Conference on eLearning, Bari, Italy

Table of contents
• E-Learning is / is not …
• Virtual Learning Environments
• Learning Object Repositories
• Case of study: Statistics
• Improving interaction
• Introducing personalization
• Current project status
• Summary
J. Minguillón ECEL 2009, 8th European Conference on eLearning, Bari, Italy

E-Learning is not…
• …e + learning
• …reproducing traditional learning
• …leaving learners alone with technology
• …technology replacing teachers
• …delivering content through LCMS
• …a collection of tools / services
• …mail, chat or blog
• …self-learning

E-Learning should be…
• …promoting communication
• …enabling deeper reflection
• …technology supporting users
• …personalized and adaptive
• …interactive and engaging
• …overcoming time / space barriers

• Learning anytime and anywhere!

Adopting E-Learning
• Three dimensions (Bates, 2005):
– Methodological
– Technological
– Organizational

• Not completely orthogonal: interconnected

• Challenge: European Higher Education Area


The “new” EHEA (I)
• Methodological / organizational changes:
– ECTS
– Learner centered model
– Competence aimed instead of content driven

• Technological requirements:
– Efficient management of educational resources
– Virtual learning environments


The “new” EHEA (II)
• Competence (not content) is the King:
– Evaluate already acquired competences
– Competence development through activities
– Activities involve the use of learning resources
– Content becomes infrastructure

providing the learner with the appropriate
learning environment for acquiring and
developing the desired competences


Learning as a journey
Degree ≈ Continent
Course ≈ Country
Activity ≈ City
Resource ≈ Map, guide
Device ≈ Vehicle
Learning path ≈ Itinerary
Previous experience ≈ Already visited places
VLE ≈ GPS
Teacher ≈ Expert assistant

Virtual Learning Environments
• E-Learning is de facto web-based learning
• VLEs enable learner centered models

Learning
Process


Data
VLE Mining

Learning
Object
Repository

LO
User
Interaction
LO LO


Learning
Process ontologies
evidences

user itineraries
profile
LOs

LD+ player

LOR

default itinerary
personalized itineraries

Managing resources
Competences

Activities

Resources

Learning
Object Content is infrastructure
Repository

Learning Object Repositories
• Two main goals:
– Ensure preservation
– Promote reutilization

• Other goals:
– Dissemination → positioning (institutional)
– Personal information management (users)

• These goals are somehow contradictory!

Key factors for LOR success (I)
• Three dimensions (McNaught, 2006):
– Resources: what?
– Actions: how?
– Users: who?

• LOR design should include them all

• Top-down vs bottom-up approaches


Key factors for LOR success (II)
• Genuine need of a community
• Enthusiastic promoters
• Clear direction and focus
• Feedback from the community
• Good management processes
• Open access
• Easy addition of new resources
• Critical mass
• Suitable granularity


Critical issues of LOR design
• Methodological:
– Learning is more than just content
• Technological (back-end + user interface):
– Learning is more than just accessing LOs
• Organizational:
– Workflow
– Licenses
– Metadata
– Policies

Traditional repositories
• Library centered:
– Books, journals, works, … (mostly textual)
• Everything has a unique title
• Everything has one or more authors
• Everything has a creation date
• Almost everything is a PDF file

• Main goal: easily finding a resource by
using a minimum set of common descriptors


Learning Object Repositories
• LOs are diverse:
– Exercises PDF, QTI, …
– Examples PDF, PPT, ODP, …
– Multimedia elements JPEG, MP3, MOV, …
– Simulations Applets, Flash
– Source code C, Java, …
– Data XLS, SPSS, …
– Other (equations, …) LaTeX, MathML, …

• Title, author and year are not enough and useless

Ideal LOR
• Basic premise: the act of browsing and/or
searching for resources should be a
learning experience in itself
– Contents are not isolated pieces
– “Traveling” requires knowing “from” and “to”
– Users should be able to organize contents
– Connectivism (Siemens, 2005)

• Ideal UI: conceptual map + “social layer”

What users want from a LOR
• More exercises and examples (55.7%)
• More simulations and interactive LOs (36.7%)
• Submitting questions about a LO (50.6%)
• Ranking LOs (43.0%)
• Correcting small mistakes (41.8%)
• Adding the LO as favorite (36.7%) by using:
– delicious (11.4%)
– Other (51.9%)
– None (26.6%)
• Just browsing and searching (16.4%)

System architecture

PIM

UI
Institutional Social
LOR layer

PIM
PIM


DSpace pros and cons
• Why DSpace? → already in use at UOC
• Pros: • Cons:
– Solid, stable – Ugly user interface
– Large community – 1.0 philosophy
– Persistent handles – Dublin Core
– Preservation – Multilingualism
– Customizable – Intricate
– OAI PMH – Mainly for e-prints


Enhancing DSpace
• Main idea:
– Use DSpace as an invisible back-end
– Access LOs through persistent handles
– Create a new user interface
– Add 2.0 functionalities
– Gather usage data

• Goal: allow learners to take control over LOs
without using DSpace directly

Case of study: LOR on Statistics
• Why Statistics?
– Basic competences for the Information Society
– Compulsory course for several degrees
– Thousands of students each semester (≈ 4000)
– Large collection of heterogeneous resources
• Known problems:
– “There are too many resources”
– “I don’t know how to start”
– “I can’t link concepts and tools”

Improving interaction
• Avoid Google-like searches
• Contextualized browsing
• Refine search results while being built
• Return only a few relevant LOs
• Visualize related LOs
• Allow learners to use web 2.0 services
• Widget-ize available services


New user interface
• Three complementary elements:
– List of competences
Competences
– Tag cloud of keywords
– Visual taxonomy
LO
Ontology
• Additional filters: LO
– Resource type LO

– Language Keywords Taxonomy


Introducing personalization
• Don’t constrain, always recommend
• Tag cloud parameters:
– Which keywords
– Sorting
– Color
– Size
• Context-aware (right-button pop-up)
• Web 2.0 services: delicious, annorate, …

Competences Taxonomy Keywords

• To compare two proportion
proportions
• To select the right
test and hypothesis test box-plot
• To design a survey
• To estimate the mean
parameters of an
unknown population Student’s t test
• To create graphics
from data variance

Filtering Results
Type LO LO

Language LO
LO LO LO


Current project status (I)
• DSpace repository online http://oer.uoc.edu
• 200 resources (500 soon, >1000 expected)
• Pilot course with 400 students (CS degree)
• List of specific competences for Statistics
• Visual taxonomy created with prefuse
• Tag cloud created with tagcrowd
• First stages of user-centered design


Current project status (II)
• But:
– Different technologies (Java, PHP, HTML, …)
– Complex process for adding resources
– Our learners (and teachers) are not so “2.0”
– Engaging activities must be designed
– Accessibility issues

• Ideas, money and students are welcomed!!!

Summary
• LOs cannot be isolated pieces
• Learners need to contextualize LOs
• Learners need to “adopt” LOs
• LORs cannot be just lists of LOs
• LORs = back-end + front-end
• DSpace as back-end: the “pyramid”
• UI as front-end: the (personal) “museum”


Thank you!

• Contact information:

Julià Minguillón
jminguillona@uoc.edu


ECEL 2009 Keynote J. Minguillón

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