Rec systel 2012 competency based recommendation
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Rec systel 2012 competency based recommendation






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  • Donner des exemples (à l ’oral) pour chaque type de recherche.
  • Voisinage ‘proche’ au sens qu’on ne descend pas la hiérarchie des classes, propriétés, etc.…

Rec systel 2012 competency based recommendation Rec systel 2012 competency based recommendation Presentation Transcript

  • RecSysTEL Workshop 2012 Saarbruecken September 18, 2012Competency Comparison Relations for Recommendation in Technology Enhanced Learning Scenarios Gilbert Paquette, Délia Rogozan, Olga Marino Research Chair in Instructional and Cognitive Enginerring (CICE) LICEF Research Center
  • Background Add semantic references to scenario components: actors, tasks and resources to educational modeling languages such as IMS-LD (2003) – Paquette and Marino, 2005 “Include the improved modeling of users and items, and incorporation of the contextual information into the recommendation process” – Adomavicus and Tuzhilin (2005) The “Adaptive Semantic Web” opens new approaches for recommenders systems: use of folksonomies and ontological filtering of resources – Jannach et al, 2011
  • Recommendation (assistance) Principles Epiphyte – grafted on the scenario process but external to it; no scenario modification Multi-agent system: agents are associated to tasks at different levels in the scenario Flexible association: one, some or all of the tasks are assisted. Delegation between a task agent towards its super tasks agents; tree topology
  • Insertion of recommenders(assistance agents): an example
  • The implemented recommender model  Recommender = {rules}  Rule = <actor, event, condition, action >  Event = – Activity transition (started, terminated, revisited,…) – Time spent (activity, global …) – Resources opened, reopened,…  Condition = boolean expression comparing: – Target actor progress in the scenario + knowledge and competencies acquired + evidence => User persistent model – Resources: prerequisite and target competencies – Activities: prerequisite and target competencies  Action = advice, notification, model update
  • Semantic Referencing of Resources Of what – Actors, activities, documents, tools, models, scenarios … Why – Help select resources at design time for better quality scenarios – Inform users of the resources’ content at design or delivery time – Assist users according to their knowledge and competencies How – Associate formal semantic descriptors to resources from a domain ontologies and/or competencies based on ontology references
  • Knowledge DescriptorsClasses and instances (From OWL-DL domain ontologies)General properties: –Domain – Data Properties –Domain – ObjectProperty – RangeInstanciated properties (facts): –Instance – Property –Instance – Property – Value
  • Competency Descriptors Knowledge descriptors Competency descriptors – (K, S, P) triples  K: Knowledge descriptor K=Planet K=Planet – From a OWL domain ontology  S: Generic Skill S=Apply S=Apply – From a 10-level taxonomy (Paquette, 2007)  P: Performance level P=Expert P=Expert – A combination of P-values (Paquette, 2007)
  • Referencing Process in the TELOS Implementation Ontology Resource Semantic11 contruction 2 2 selection 3 3 Referencing or import Of resources … and/or competencies
  • Semantic Search Methods Type de recherche Type de résultatSimple Exact matchUsing key words from the ontologyAdvanced Exact matchUsing knowledge and competency Semanticallyboolean query near match Exact matchResource Pairing SemanticallyUsing semantic comparison betweenqueried ressource and other resources near match → Rests on knowledge and competency comparison
  • Knowledge Comparison (K1 et K2) Based on the structure of the ontology where the knowledge descriptors are stored Compare the neighbourhoods of K1 and K2 Possible results – K2 near and more specialized / general than K1
  • Competency Comparison C1=(K1, S1, P1) et C2=(K2, S2, P2) Based on knowledge comparison (K) Base on the distance between skills’ levels (H) and performance levels distances(P) Possible results  C2 veryNear / Near C1  C2 stronger / weaker than C1  C2 more specialized / general than C1
  • Competency Comparison
  • Competency comparison within rule conditions A competency-based condition is a triple: – ObjectCompetencyList is the list of prerequisite or target competencies of another actor, a task or a resource to be compared with user’s actual competency list – Relation is one of the comparison relations : Identical, Near, VeryNear, MoreGeneric, MoreSpecific, Stronger, Weaker, or any combination of these. – Quantification takes two values: HasOne or HasAll EX: HasAll /NearMoreSpecific / Target competencies for Essay EX: HasOne/Weaker/Target competency for Build Table activity
  • Recommendation example
  • Notification example
  • User model update
  • Achievements in this project Extension of the TELOS Technical Ontology for semantic referencing of resources, search and recommendation Definition of a Typology of semantic descriptors (ontology descriptors and competenciers) Search methods for resources ‘identical’ ou ‘near’ sémantically Recommendation Model: based on competency comparison between actors, tasks and resources New integrated suite of tools: Semantic referencer, Semantic search tools, Competency and Ontology editors, Integration to recommanders scenarios, Recomenders’ rule editor.
  • Future steps More experimental validation to refine the semantic relations between OWL-DL references, i.e adding weights to the various comparison cases Investigate recommendation methods for groups in collaborative scenarios (permitted by our model of multi-actor learning scenarios) Improve the practical use of the approach, partly automate tasks, improve the ergonomics Investigate the integration of other recommendation methods (e.g. user analytics) “Free” the suite of tools from TELOS to extend its usability on the Web of data.
  • RecSysTEL Workshop 2012 Saarbruecken September 18, 2012 Questions ? Comments ? Gilbert Paquette, Délia Rogozan, Olga Marino; Research Chair in Instructional and Cognitive Enginerring (CICE) LICEF Research Center