Towards Context-specific Personalised Help in Moodle
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Towards Context-specific Personalised Help in Moodle

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This presentation outlines work done by Enovation and research partners in the Percolate project to investigate allowing for personalised JIT help in Moodle.

This presentation outlines work done by Enovation and research partners in the Percolate project to investigate allowing for personalised JIT help in Moodle.

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  • UCD, Trinity College
  • 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
  • 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
  • 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

Towards Context-specific Personalised Help in Moodle Presentation Transcript

  • 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 problemsIndustry 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
  • 6. Software Testing
  • 7. 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
  • 8. 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…”
  • 9. 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]
  • 10. Next Steps• Percolate  LTC• Better UI• Manual effort• Non-intrusive methods of gaining info
  • 11. Questions?Acknowledgements:Dr. Dermot Brabazon – DCUCatherine Bruen – NDLRWe kindly acknowledge Enterprise Ireland for theirsupport for this project. Contact: mark.melia@enovation.ie Contact: mark.melia@enovation.ie