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Monique Grandbastien1,

Benjamin Huynh-Kim-Bang1,

Tomas Pariente Lobo2,

Sihuhe Aroyo3

1 LORIA-UHP Nancy I, France

2 ATOS Research & Innnovation, Madrid, Spain

3 University of Alcala de Henares, Spain

The LUISA framework for

enabling semantic search of learning

resources

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LUISA project objectives (European project)

• To demonstrate the feasibility and the added value of applications making an intensive use of existing semantic web technologies in a given sector, and thus:

• To provide a general framework for managing learning resources and to demonstrate the significant push that these technologies can bring, compared to existing solutions

• To provide and test two customizations of the framework in different settings:

• An industrial environment with EADS/Airbus• An academic environment with UHP Nancy1

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Retrieving learning resources today

• Potential availability of an increasing number of resources

• Achievements in standards proposals for allowing

interoperability

• However, queries in Learning Object Repositories mostly

textual and key-word based

• Semantic based solutions have not been yet fully deployed in this field

• But….

• Is it feasible ? Which cost? What can be shared and reused?

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LUISA Global Architecture

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Focus on the knowledge layer in LUISA

• Among the semantic web technologies, a strong knowledge layer including:

• Common knowledge framework• Learning Resource Core Ontology

• Based on LOM• Competency Core ontology

• Based on GCO

• Customized knowledge framework• Specialisation of the Core Ontologies• Domain specific ontologies

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Industrial use case

• Focuses on Training management mission• support the development and maintenance of the right range of

skills and competences needed for the employees’ jobs

• Industrial use case is about Training course design• support the finding and selection of Learning Objects and their

combination within a course so to improve the adequacy between an individual competence profile and her job context requirements

doesn’t deal with digital content production but rather with course’s design

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Industrial Use case

During annual interview the Engineer and her Manager agree on a training plan (LO selection & combination) to complete the Engineer profile with regard to her job position requirements

• Annual interview

The Training Manager shall be able to Search/build training offers with regard to some competency indicators, having a collective viewpoint

• Training Management

A User shall be able to select/organize relevant LOs with regard to any target competence, profession or job position

• Life long learning

The Manager shall be able to find people having relevant competencies with regard to a target profession, possibly modulo individual training needs

• Staffing

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Academic use case

• Focuses on providing on line resources to campus based students

• supports the personalization of learning material• Allows teachers to select and retrieve teaching

material for their students • suppors the finding and selection of fine grained

Learning Objects and their combination to improve the adequacy between students’ profiles and needs and available resources

deals with digital content production (to some extent)

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Academic Use case

Mandatory before obtaining any bachelor degree in French Universities

• Informatics and Internet competencies

Could get resources suitable for their students, possibly after reworking them.

• Academic staff in charge of students

A student shall be able to get a given C2i competency or to improve it or to check whether he/she is ready for taking the exam

• Updating a given competency

Could train themselves according to their own needs, distance students as well as on campus students

• Students on their own

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LUISA customized for the academic case

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Academic Use Case : C2i competencies

• C2i competences are organized into a set of main competencies, each of them being subdivised into subcompetencies

• Main competencies complete list :• General and transversal competencies

• A1: Be aware of the evolution of IT.• A2: Comprehend the ethical issues.

• Specific and instrumental competencies• B1: Control his environment of work.• B2: Research of information.• B3: Save, secure and back-up his data in a local place or

on a network.• B4: Realize documents for printing.• B5: Realize presentations of his work offline and online.• B6: Communicate remotely.• B7: Produce a joint project.

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Academic Use Case ontologies

cLom gcs

uhpClom uhpGcs

uhpComputerLiteracy uhpFieldOfStudy

about

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GCO/C2I : CD concept level

CompetencyDefinition

CompetencyElementDefinition

KnowledgeElementDefinition SkillDefinition AttitudeDefinition

requiresdetails

requiresCED

prerequisites

OntoTerm

about

prerequisites

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LUISA in action (demo)

• Several steps:

• Resource annotation with the LUISA annotation tool

• Resources search and retrieval in the LORs

• Work with selected resources

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How is the selection performed

• A three steps process

• Step 1 • Using competency based and topic based criteria

• Step 2• Taking into account preference constraints

• Step 3 • Taking into account learning and teaching criteria

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QR step 1: Resources search

• For each target competency do• Competency-based search

• Add the related competency elements and build

«list_of_comp»

• Search for resource metadata including competency

elements from «list_of_comp»(resource instance)

• Topic-based search (optional)• Build the topic list « list_of_topics » from the about

relation in the knowledge-element class

• Search for resource metadata including topics from the

« list_of_topics »

• Step result = the R-list of candidate resources

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GCO/C2I : CED instance level

CD:B4

CD:B4 textprocessor CD:B4 spreadsheet

KED: k_basic_textprocessor

KED: k_advanced_textprocessor KED: k_advanced_spreadsheet

KED: k_basic_spreadsheet

details

requires requires

prerequisites prerequisites

« TextProcessor » instance (ComputerLiteracy. Onto)

« Spreadsheet » instance (ComputerLiteracy. Onto)

aboutabout about about

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QR step 2 : Constraint checking

• For each element from R-list do• If one of the preference constraints is not satisfied,

delete from R-list• If one of the prerequisite constraint (coming from the

prerequisite relation in the knowledge-element class from the GCO-C2I ontology) is not satisfied, delete from R-list (GCO/C2I concept / instance level)

• Step result = the « to be proposed » P-list

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QR step 3: Final selection criteria

• Up to step 2, resource selection was based on competency and topic criteria.

During step 3, a final selection is based on teaching, learning and

personalizing criteria. These criteria can be chosen and used from different

perspectives, giving birth to different solutions for the learner

• Criteria used for the scenario

• package composition choice : for each target competency, provide

a « package » including at least a presentation and an exercise

• material coherence : prefer resources from the same author or

targeting the same students or authored in the same institution

• discipline coherence (when applicable): if the resource relates to

some data, prefer data belonging to the discipline of the student’s profile

• peer rating: prefer resources which were well rated by peer students

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QR step 3: criteria application

• The previously mentioned criteria are given a weight for computing a global « criteria weight » for each resource

• For each target competency• Select the « best »presentation (higher weight)• Select the « best » exercise• Put them in the selection basket• Put the others in the « alternate » basket.

• Step result = resource selection

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Annotation interface

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Luisa in action - Query phase : Preferences

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Luisa in action - query phase : query

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Luisa in action : Proposed resources

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Luisa in action : working phase

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Conclusions (1)

• About customizing the framework• Has been successfully customized for two very different

environments• Ontologies

• GCO-ontology and LOM ontology easy to specialize• Domain specific ontoogies

• Difficulty to find ontologies for re-use

• Difficulty for updating» The end-user should be able to create new instances

through controlled interfaces

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Conclusions (2)

• About observed benefits• Annotation tool flexibility (application profiles)

• Rich annotations provided for a cost which is completely similar to a LOM editor based annotation process

• Dynamic computation of competencies• Allows the user to reflect about his competencies

• Personalization of the final selection• Towards rules explaining the proposed selection

• From the technical side• Queries are still too long• But the completely distributed framework works!• Performance improvements will be on the agenda when

the basic functions are implemented

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Key innovations examplified

• Queries grounded on ontologies• C2i competencies, dynamic competency gap computation• Preferences

• Best sources• Not detailed in the presentation, but the search could be performed in

several LORs

• LO composition• Exemplified with the “pack”

• Back to the user• The learner can select competencies he already owns• Not detailed in the presentation, but when no resource is selected

through the described process, the user is asked to modify his request

• Several selection and composition strategies• Step 3 of the selection process shows possible alternate resolutions

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•Thank you

•Questions?

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