Competency Comparison Relations for Recommendation in Technology Enhanced Learning Scenarios

Gilbert Paquette, Délia Rogozan, Olga Gilbert Paquette, Délia Rogozan, Olga MarinoMarino

www.licef.ca/cice

Canada Research Chair in Instructional and Canada Research Chair in Instructional and Cognitive Enginerring (CICE)Cognitive Enginerring (CICE)

LICEF Research CenterLICEF Research Center

Télé-universitéTélé-université

RecSysTEL Workshop 2012RecSysTEL Workshop 2012Saarbruecken September 18, 2012Saarbruecken September 18, 2012

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) Recommendation (assistance) PrinciplesPrinciples

Epiphyte – grafted on the scenario process Epiphyte – grafted on the scenario process

but external to it; no scenario modificationbut external to it; no scenario modification

Multi-agent system: agents are associated to Multi-agent system: agents are associated to

tasks at different levels in the scenariotasks at different levels in the scenario

Flexible association: one, some or all of the Flexible association: one, some or all of the

tasks are assisted.tasks are assisted.

Delegation between a task agent towards its Delegation between a task agent towards its

super tasks agents; tree topologysuper tasks agents; tree topology

InsertionInsertion of recommenders of recommenders (assistance agents): an example(assistance agents): an example

The implemented recommender The implemented recommender modelmodel

Recommender = {rules}Recommender = {rules} Rule = <actor, event, condition, action >Rule = <actor, event, condition, action > Event = Event =

– Activity transition Activity transition (started, terminated, revisited,…)(started, terminated, revisited,…)– Time spent (activity, global …) Time spent (activity, global …) – Resources opened, reopened,…Resources opened, reopened,…

Condition = boolean expression comparing: Condition = boolean expression comparing: – Target actor progress in the scenario + Target actor progress in the scenario + knowledge and knowledge and

competencies acquired + evidence => competencies acquired + evidence => User persistent modelUser persistent model

– Resources: prerequisite and target competenciesResources: prerequisite and target competencies

– Activities: prerequisite and target competenciesActivities: prerequisite and target competencies

Action = advice, notification, model updateAction = 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, S, P) triples K: Knowledge descriptorK: Knowledge descriptor

– From a OWL domain ontologyFrom a OWL domain ontology

S: Generic SkillS: Generic Skill– From a 10-level taxonomy From a 10-level taxonomy (Paquette, 2007)(Paquette, 2007)

P: Performance levelP: Performance level– A combination of P-values A combination of P-values (Paquette, 2007) (Paquette, 2007)

S=ApplyS=ApplyS=ApplyS=Apply

P=ExpertP=ExpertP=ExpertP=Expert

K=PlanetK=PlanetK=PlanetK=Planet

Referencing Process in the TELOS Implementation

OntologyOntologycontructioncontructionor importor import

… and/or competencies

ResourceResourceselectionselection1111 2222

SemanticSemanticReferencingReferencingOf resourcesOf resources

3333

Semantic Search Methods

Type de rechercheType de recherche Type de résultatType de résultat

Simple Using key words from the ontology

AdvancedUsing knowledge and competency Using knowledge and competency boolean queryboolean query

Resource PairingUsing semantic comparison between queried ressource and other resources

→ → Rests on knowledge and competency comparisonRests on knowledge and competency comparison

Exact matchExact match

Exact matchExact match

Semanticallynear match

Semanticallynear match

Exact matchExact match

Knowledge Comparison (K1 et K2)

Based on the Based on the structure of the ontology where the of the ontology where the knowledge descriptors are storedknowledge descriptors are stored

Compare the Compare the neighbourhoods of K1 and K2of K1 and K2

Possible resultsPossible results– K2 K2 near and more and more specialized / / general than K1 than K1

Competency Comparison

Based on knowledge Based on knowledge comparison ((KK))

Base on Base on the distance between skills’ levels (between skills’ levels (HH) ) and and performance levels distances(performance levels distances(PP))

Possible resultsPossible results C2C2 veryNear / Near C1 C1 C2C2 stronger / weaker than C1than C1 C2 more C2 more specialized / general than C1than C1

C1=(K1, S1, P1) et C2=(K2, S2, P2)C1=(K1, S1, P1) et C2=(K2, S2, P2)

Competency ComparisonCompetency Comparison

Competency comparison Competency comparison within rule conditionswithin 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 exampleRecommendation example

Notification exampleNotification example

User model updateUser 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 stepsFuture 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.

Questions ?Comments ?

Gilbert Paquette, Délia Rogozan, Olga Gilbert Paquette, Délia Rogozan, Olga MarinoMarino

www.licef.ca/cice; www.licef.ca/gp

Canada Research Chair in Instructional and Canada Research Chair in Instructional and Cognitive Enginerring (CICE)Cognitive Enginerring (CICE)

LICEF Research CenterLICEF Research Center

Télé-universitéTélé-université

RecSysTEL Workshop 2012RecSysTEL Workshop 2012Saarbruecken September 18, 2012Saarbruecken September 18, 2012