Next Generation eLearning Can Technology Learn from the Learners: The case for Adaptive Learning...
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Transcript of Next Generation eLearning Can Technology Learn from the Learners: The case for Adaptive Learning...
Next Generation eLearningCan Technology Learn from the Learners:The case for Adaptive Learning Objects
Vincent WadeResearch Director,
Knowledge & Data Engineering Research Group
Computer Science Dept.Trinity College Dublin
www.cs.tcd.ie/kdeg
DirectorCenter for Learning Technology
Trinity College Dublinwww.tcd.ie/clt
© Vincent P. Wade Adaptive Personalised eLearning 2
Student Centric e-Learning
Goal of Adaptive, Personalised e-Learning:
“to provide e-learning content, activities and collaboration,
adapted to the specific needs and influenced by specific preferences and context of the student,
based on the sound pedagogic strategies”
© Vincent P. Wade Adaptive Personalised eLearning 3
What does it offer the learner?
What does it offer the teacher?
Some Questions?
How difficult is it to achieve?
Does it need an army of engineers, developers And subject matter experts?
What is Adaptive, Personalised eLearning?
What record of success does it have?
© Vincent P. Wade Adaptive Personalised eLearning 4
Motivation
• ‘One size doesn’t fit all’!– Different people have different needs, likes,
preferences, skills, abilities– Are in different locations, using different devices,
With different connectivity – Are in different circumstances, using service for
different reasons ……
• Large variety of Users, very variable circumstances, large ‘hyper’space
© Vincent P. Wade Adaptive Personalised eLearning 5
Motivation
• Digital Content very expensive to develop=> need to ensure re-use
• Need to automate ‘transformation’ process of digital content - to ensure greater usability
Adapt to Learner’s …
Learner
Prior Knowledge & ExpertisePrior Knowledge & Expertise
Cognitive &Cognitive &
Learning StyleLearning Style
Learning History
Aims and GoalsAims and Goals
Preferences &
Learning Culture
Communication
Style & Needs
Some Examples …...
© Vincent P. Wade Adaptive Personalised eLearning 8
Benefits of Personalised e-Learning
• Pedagogic
– Improved quality & effectiveness (no two students are identical)
– Improved Relevancy
– Reduced cognitive overload, reduced learning time
– Improve retention
– Empower learner (take more responsibility, more active participation)
© Vincent P. Wade Adaptive Personalised eLearning 9
Benefits of Personalised e-Learning
• Management
– Promote Resource (content) Reuse / Reduced Costs
– Ability to introduce Multiple courses across same content repository
– Enable further e-learning opportunities
© Vincent P. Wade Adaptive Personalised eLearning 10
Adapting to What?
• Knowledge about the subject• Knowledge about the system • Goals• Interests• Culture• Language• Capabilities• (Dis)Abilities• Preferences
Learner
© Vincent P. Wade Adaptive Personalised eLearning 11
Case Study: Trinity College Dublin
• Engineering Faculty: Dept. of Computer Science
• 7 Different Degrees
– Computer Engineering,Computer Science, Info. Technology etc.
• Various ‘Databases’ courses taught on different degree, to different student years (1st - 4th ), with varying learning objectives & syllabi
© Vincent P. Wade Adaptive Personalised eLearning 12
Multi-model, Metadata Driven Approach
• Metadata to describe Adaptive Resources
• Multi-model
• Two versions of the approach– 3 Models – Content, Learner and Narrative (PLS)– N Models – At least one Narrative, the rest are
metadata based (APeLS)
• User Trial and Feedback
© Vincent P. Wade Adaptive Personalised eLearning 13
The Learner Model
• The Learner Model contains information about the Learner’s …– Pre-knowledge (Prior Knowledge)– Objectives and Goals– Cognitive and Learning Style
LearnerModel
Pre-knowledge
Objectives
Learning Style
© Vincent P. Wade Adaptive Personalised eLearning 14
The Content Model (Learning Objects)
• The Content Model must accurately represent the unit of material (a fine grained LO)
• The model must represent each LO from three perspectives…– General Information– Pedagogical Information– Technical Information
© Vincent P. Wade Adaptive Personalised eLearning 15
The Narrative Model (cont.)
• The Narrative Model representS relationships between CONCEPTS
• These relationships include…
– Pre-requisites
– Suggested optional concepts
Narrative Model
Start PointsRelationships
Adaptive Service
Adaptive Personalised Learning Service (APeLS) Architecture
LearningObjectsModel
Learner
LearnerModel
LearningObject Mdl
Narrative LearnerModels
Learn
er P
orta
l
NarrativeModels
© Vincent P. Wade Adaptive Personalised eLearning 17
The Personalised Learning Service - Reconciling the Models
• The Adaptive Engine must determine the core and optional material for the learner
LearnerModel
Pre-knowledge
Objectives
Learning Style
Learning ObjectModel
Keywords
Content Type
Supported Learning Style
Narrative Model
Start PointsRelationships
© Vincent P. Wade Adaptive Personalised eLearning 18
Authoring AdaptivePersonalised eLearning
Course Design = Model Design + Learning ObjectAuthoring
• Development of Models– Concept Space (ontological approach)
– Narrative Model (pedagogic/Instructional design based models) e.g Case Based, Web Quests, Didactic, Learning Spaces etc.
– Adaptive Property selection
– Content (Learning Objects)
• Course Design = Model Design + Leaning ObjectAuthoring
• Development of Models– Concept Space (ontological approach)
– Narrative Model (pedagogic/Instructional design based models) e.g Case Based, Web Quests, Didactic, Learning Spaces etc.
– Adaptive Property selection
– Content Piglets
Authoring AdaptivePersonalised eLearning Le
arn
er
Mod
el Le
arni
ng
Objec
t Mod
el
Narr
ati
ve M
od
el
ConceptModel
Context
Model
Learner
© Vincent P. Wade Adaptive Personalised eLearning 20
Evaluation
• APeLS used to deliver RDBMS course to 120 final year students (two degrees)
• Pre-test instrument for VARK & prior knowledge in DBMS
• Learners able to rebuild their personalized course via instrumentation
• Highly popular with student body
• Continual refinement & re-personalization by student for various reasons
© Vincent P. Wade Adaptive Personalised eLearning 21
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1998 1999 2000 2001* 2002 2003
Year
Qu
es
tio
n S
co
re (
ou
t o
f 2
0)
Related Questions
Unrelated Questions
Average Question Scores on Database Examinations 1998 – 2003
© Vincent P. Wade Adaptive Personalised eLearning 22
Student Opinions
• Very high satisfaction rating of course (87%)
• All students used the ‘adaptive’ controls to take responsibility for their e-learning
• 60% satisfied with level of control offered by the ‘adaptive’ controls
• Some interesting observations– frequent student re-personalisation for specific time
objective
© Vincent P. Wade Adaptive Personalised eLearning 23
the story so far …
• Adaptive Hypermedia Services facilitates:– graceful enhancement and scalability of content
service– support multiple courses & learning experiences– empower user (learner)– interpretative Semantic Web driven approach allows
evolution of adaptivity
© Vincent P. Wade Adaptive Personalised eLearning 24
Thank you…………
any questions ………