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

2. © Vincent P. WadeAdaptive Personalised eLearning2 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”

3. © Vincent P. WadeAdaptive Personalised eLearning3 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?

4. © Vincent P. WadeAdaptive Personalised eLearning4 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

5. © Vincent P. WadeAdaptive Personalised eLearning5 Motivation Digital Content very expensive to develop => need to ensure re-use Need to automate ‘transformation’ process of digital content - to ensure greater usability

6. Adapt to Learner’s … Learner Prior Knowledge & Expertise Cognitive & Learning Style Learning Style Learning History Aims and Goals Preferences & Learning Culture Communication Style & Needs

7. Some Examples …...

8. © Vincent P. WadeAdaptive Personalised eLearning8 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)

9. © Vincent P. WadeAdaptive Personalised eLearning9 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

10. © Vincent P. WadeAdaptive Personalised eLearning10 Adapting to What? Knowledge about the subject Knowledge about the system Goals Interests Culture Language Capabilities (Dis)Abilities Preferences Learner

11. © Vincent P. WadeAdaptive Personalised eLearning11 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 (1 st - 4 th ), with varying learning objectives & syllabi

12. © Vincent P. WadeAdaptive Personalised eLearning12 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

13. © Vincent P. WadeAdaptive Personalised eLearning13 The Learner Model The Learner Model contains information about the Learner’s … –Pre-knowledge (Prior Knowledge) –Objectives and Goals –Cognitive and Learning Style Learner Model Pre-knowledge Objectives Learning Style

14. © Vincent P. WadeAdaptive Personalised eLearning14 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

15. © Vincent P. WadeAdaptive Personalised eLearning15 The Narrative Model (cont.) The Narrative Model representS relationships between CONCEPTS These relationships include… –Pre-requisites –Suggested optional concepts Narrative Model Start PointsRelationships

16. Adaptive Service Adaptive Personalised Learning Service (APeLS) Architecture Learning Objects Model Learner Model Learning Object Mdl Narrative Learner Models Learner Portal Narrative Models

17. © Vincent P. WadeAdaptive Personalised eLearning17 The Personalised Learning Service - Reconciling the Models The Adaptive Engine must determine the core and optional material for the learner Learner Model Pre-knowledge Objectives Learning Style Learning Object Model Keywords Content Type Supported Learning Style Narrative Model Start PointsRelationships

18. © Vincent P. WadeAdaptive Personalised eLearning18 Authoring Adaptive Personalised 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)

19. 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 Adaptive Personalised eLearning Learner Model Learning Object Model Narrative Model Concept Model Context Model Learner

20. © Vincent P. WadeAdaptive Personalised eLearning20 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

21. © Vincent P. WadeAdaptive Personalised eLearning21 Average Question Scores on Database Examinations 1998 – 2003

22. © Vincent P. WadeAdaptive Personalised eLearning22 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

23. © Vincent P. WadeAdaptive Personalised eLearning23 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

24. © Vincent P. WadeAdaptive Personalised eLearning24 Thank you………… any questions ………