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

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 eLearning Can Technology Learn from the Learners: The case for Adaptive Learning Objects

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  1. 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

  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” Adaptive Personalised eLearning

  3. 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

  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 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 Adaptive Personalised eLearning

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

  7. Some Examples …...

  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) 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 Adaptive Personalised eLearning

  10. Learner Adapting to What? • Knowledge about the subject • Knowledge about the system • Goals • Interests • Culture • Language • Capabilities • (Dis)Abilities • Preferences 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 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 Adaptive Personalised eLearning

  13. 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

  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 Adaptive Personalised eLearning

  15. 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

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

  17. 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

  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) Adaptive Personalised eLearning

  19. 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

  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 Adaptive Personalised eLearning

  21. Average Question Scores on Database Examinations 1998 – 2003 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 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 Adaptive Personalised eLearning

  24. Thank you………… any questions ……… Adaptive Personalised eLearning

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