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Towards Context-specific Personalised
          Help in Moodle
    Michael O’Mahony - Clarity Centre, UCD
       Mark Melia – Enovation Solutions
Context – Percolate Project
• e-Learning Industry working with academic
  researchers
• Industry defined challenging use cases
• Challenge is to apply research to use case
• Third level use case – helping students with
  problems
Industry                    Academia
The Problem
• Students working online needs help
• Finding wood from trees difficult on WWW

• I need help with X now!
  – Understand subject
  – Pedagogically sound
  – Personalised to me


                             photo by London College of Fashion
The Problem
• NDLR (DSpace) Moodle search

• One size fits all
• Celebrate individuality
  – Prior knowledge
  – Type of resources
  – Course context
Recommender system
• Hooks Moodle into a learning resource
  repository
• Search understands what peers found useful
• Search understands the conceptual structure
  of subject
• Uses conceptual structure to understand
  learners needs
• Results organised according to a pedagogical
  strategy
Software Testing
How does it work?
• Semantic search
  – Based on conceptual structure of subject area
• Social search
  – Based on whether “similar” learners found a
    resource useful
• Composition engine
  – Compose resources from search into learning
    episode
User Trial
                       DCU Trial

• 18 DCU 4th year Mechanical Engineering students
• 6 week trial (complete)
• Students used application in the context of the
  following assignment:

      “You are a process engineer for a multi-international
      institution that wishes to introduce an advanced
      manufacturing technology for one of their new advanced
      material based products…”
User Trial
                              DCU Trial
• 527 completed search sessions
• 419 results selected (130 distinct results)
• User feedback:
     – 36 results rated or tagged
     – 34 post-confidence scores for concepts
                                   “A wider range of information needs to be
“Good system, still in its early     uploaded in order for it to accomodate
    stages” [Student A]             the academic objectives of the materials
                                              module” [Student B]
Next Steps
•   Percolate  LTC
•   Better UI
•   Manual effort
•   Non-intrusive methods of gaining info
Questions?
Acknowledgements:

Dr. Dermot Brabazon – DCU
Catherine Bruen – NDLR

We kindly acknowledge Enterprise Ireland for their
support for this project.

     Contact: mark.melia@enovation.ie
    Contact: mark.melia@enovation.ie

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Towards Context-specific Personalised Help in Moodle

  • 1. Towards Context-specific Personalised Help in Moodle Michael O’Mahony - Clarity Centre, UCD Mark Melia – Enovation Solutions
  • 2. Context – Percolate Project • e-Learning Industry working with academic researchers • Industry defined challenging use cases • Challenge is to apply research to use case • Third level use case – helping students with problems Industry Academia
  • 3. The Problem • Students working online needs help • Finding wood from trees difficult on WWW • I need help with X now! – Understand subject – Pedagogically sound – Personalised to me photo by London College of Fashion
  • 4. The Problem • NDLR (DSpace) Moodle search • One size fits all • Celebrate individuality – Prior knowledge – Type of resources – Course context
  • 5. Recommender system • Hooks Moodle into a learning resource repository • Search understands what peers found useful • Search understands the conceptual structure of subject • Uses conceptual structure to understand learners needs • Results organised according to a pedagogical strategy
  • 7.
  • 8. How does it work? • Semantic search – Based on conceptual structure of subject area • Social search – Based on whether “similar” learners found a resource useful • Composition engine – Compose resources from search into learning episode
  • 9. User Trial DCU Trial • 18 DCU 4th year Mechanical Engineering students • 6 week trial (complete) • Students used application in the context of the following assignment: “You are a process engineer for a multi-international institution that wishes to introduce an advanced manufacturing technology for one of their new advanced material based products…”
  • 10. User Trial DCU Trial • 527 completed search sessions • 419 results selected (130 distinct results) • User feedback: – 36 results rated or tagged – 34 post-confidence scores for concepts “A wider range of information needs to be “Good system, still in its early uploaded in order for it to accomodate stages” [Student A] the academic objectives of the materials module” [Student B]
  • 11. Next Steps • Percolate  LTC • Better UI • Manual effort • Non-intrusive methods of gaining info
  • 12. Questions? Acknowledgements: Dr. Dermot Brabazon – DCU Catherine Bruen – NDLR We kindly acknowledge Enterprise Ireland for their support for this project. Contact: mark.melia@enovation.ie Contact: mark.melia@enovation.ie

Notas do Editor

  1. UCD, Trinity College
  2. Saw the talk yesterday – we have worked with the NDLR to allow for searching NDLR in MoodleWe wanted to see if the search could be specific to learner needs Move away from one size fits all -> celebrating differences in learners [prior knowledge, type of resources] deliver resources that are
  3. Saw the talk yesterday – we have worked with the NDLR to allow for searching NDLR in MoodleWe wanted to see if the search could be specific to learner needs Move away from one size fits all -> celebrating differences in learners [prior knowledge, type of resources] deliver resources that are
  4. Search looks for concept in conceptual structureSystem tries to establish if concept not known due to missing pre-requisite knowledgeReturns material based on learner intent, knowledge levels of concepts and associated metadata on learning resources Generates a learning episode– composition engine using a pedagogical strategy – intro, examples, summary