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Next Generation eLearning Can Technology Learn from the Learners: The case for Adaptive Learning Objects Vincent Wade Director Center for Learning Technology Trinity College Dublin www.tcd.ie/clt Research Director, Knowledge & Data Engineering Research Group Computer Science Dept.

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Next Generation eLearningCan Technology Learn from the Learners:The case for Adaptive Learning Objects

Vincent Wade

Director

Center for Learning Technology

Trinity College Dublin

www.tcd.ie/clt

Research Director,

Knowledge & Data Engineering

Research Group

Computer Science Dept.

Trinity College Dublin

www.cs.tcd.ie/kdeg


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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”

Adaptive Personalised eLearning


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Some Questions?

What is Adaptive, Personalised eLearning?

What does it offer the learner?

What does it offer the teacher?

How difficult is it to achieve?

Does it need an army of engineers, developers

And subject matter experts?

What record of success does it have?

Adaptive Personalised eLearning


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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

Adaptive Personalised eLearning


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Motivation

  • Digital Content very expensive to develop

    => need to ensure re-use

  • Need to automate ‘transformation’ process of digital content - to ensure greater usability

Adaptive Personalised eLearning


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Prior Knowledge & Expertise

Aims and Goals

Cognitive &

Learning Style

Communication

Style & Needs

Preferences &

Learning Culture

Learner

Learning History

Adapt to Learner’s …



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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)

Adaptive Personalised eLearning


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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

Adaptive Personalised eLearning


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Learner

Adapting to What?

  • Knowledge about the subject

  • Knowledge about the system

  • Goals

  • Interests

  • Culture

  • Language

  • Capabilities

  • (Dis)Abilities

  • Preferences

Adaptive Personalised eLearning


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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

Adaptive Personalised eLearning


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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

Adaptive Personalised eLearning


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Learner

Model

Learning Style

Pre-knowledge

Objectives

The Learner Model

  • The Learner Model contains information about the Learner’s …

    • Pre-knowledge (Prior Knowledge)

    • Objectives and Goals

    • Cognitive and Learning Style

Adaptive Personalised eLearning


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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

Adaptive Personalised eLearning


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Start Points

Relationships

Narrative Model

The Narrative Model (cont.)

  • The Narrative Model representS relationships between CONCEPTS

  • These relationships include…

    • Pre-requisites

    • Suggested optional concepts

Adaptive Personalised eLearning


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Learner

Model

Learning

Object Mdl

Learner Portal

Learner

Narrative

Adaptive Service

Adaptive Personalised Learning Service (APeLS) Architecture

Narrative

Models

Learning

Objects

Model

Learner

Models


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Learner

Model

Learning Object

Model

Learning Style

Keywords

Content Type

Pre-knowledge

Objectives

Supported Learning Style

Start Points

Relationships

Narrative Model

The Personalised Learning Service - Reconciling the Models

  • The Adaptive Engine must determine the core and optional material for the learner

Adaptive Personalised eLearning


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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)

Adaptive Personalised eLearning


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Learner

Model

Learning

Object Model

Narrative Model

Learner

Concept

Model

Context

Model

Authoring AdaptivePersonalised eLearning

  • 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


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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

Adaptive Personalised eLearning


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Average Question Scores on Database Examinations 1998 – 2003

Adaptive Personalised eLearning


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Student Opinions 2003

  • 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

Adaptive Personalised eLearning


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the story so far … 2003

  • 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

Adaptive Personalised eLearning


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Thank you………… 2003 any questions ………

Adaptive Personalised eLearning


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