2010-04-14 educon emadrid umh (upm) oscar martínez

An Approach for Description ofOpen Educational

Resources based on SemanticTechnologies

Nelson Piedra, Janneth Chicaiza, Jorge LópezEscuela Ciencias Computación, Universidad Técnica Particular Loja,

EcuadorOscar Martínez Bonastre, Centro de Investigación Operativa

Universidad Miguel Hernández, EspañaEdmundo Tovar Caro, Facultad de Informática, Universidad

Politécnica de Madrid, España

IEEE EDUCON Education Engineering 2010 – The Future of Global Learning Engineering Education

Outline

• Introduction• OER: Using metadata and Ontologies• OER-CC ontology: Development process• OER-CC: Instantiation and Retrieval

– Instantiation process

– Information Retrieval

• Conclusions

• IEEE EDUCON Education Engineering 2010 – The Future of Global Learning Engineering Education

Introduction

• Open Educational Resources (OERs) initiativespromote their global exchange with the aim ofincreasing the human intellectual capacity.

• Technology in general, and specifically Web, provides an interesting opportunity for people toget key competences while they´re using oreven also sharing digital contents.


Introduction• OER are teaching, learning, research digital resources and tools,

which are available on the public domain or have been releasedunder an intellectual property license, i.e., they allow their free use or even also be reusable by others.


IntroductionWe hypothesized that growth of OER data repository shouldmake difficult to find out information of value, i.e, it red uces possibilities of sharing educational resources.

Consequently, we evaluated some solutions like

• Using semantic technologies to describe OER could enable anyagent (human or software-based) to process and understandcontent (applying inference rules to structured knowledge)

• Metadata standards could be used to annotate OER, i.e., th ey´dimprove their interoperability and discovery of knowledge.


OER: Using metadata andOntologies

• The Open Educational Resources Initiatives are based on Open Access (OA) movement.

• OER Projects can be classified according toused model (funding, technical, content andstaffing) to ensure their sustainability.

– producer-consumer models

– Co-production models



• Using semantics in OER Production

– Semantic technologies would automate or semi-automate certain educative tasks through “synergybetween human and machines”

– People are the producers and customers: they are thesource of knowledge and they request informationalso.

– Machines are the enablers: they store and rememberdata, search and combine data, draw mathematicaland logical inferences, etc.”




– Semantic technologies could automate certain educative tasksthrough “synergy between human and machines”




– To achieve these objectives, OER should be described by a standardschema in a way that any agent (human or software-based) couldunderstand and processing its content.

• Metadata Standards for Educational Material standards:

– The IEEE LTSC (Learning Technology Standard Committee) developed IEEE LOM (Learning Object Metadata).

– The IMS GLC (Global Learning Consortium) proposes IMS Learning ResourceMetadata.

– Dublin Core Metadata Initiative.

• Ontologies:

– METHONTOLOGY

– On-To-Knowledge• IEEE EDUCON Education Engineering 2010 – The Future of Global Learning Engineering Education



– Gruber’s principles:

• Clarity, coherence, extendibility, minimal encoding bias and minimalontological commitment; the idea is to ensure the “knowledge sharing”.

• To fulfill these criteria, ontology evaluation should be performed duringdevelopment process.

• Different types of evaluation:

– Syntactic evaluation, to check languages specification

– Semantic evaluation, focused on detecting if ontologies have inconsistencies andredundancies respectively

– Lexical evaluation, it refers to the vocabulary used to represent concepts anddomain relationships.




– Describing Objects and Learning Resources: Metadata Standards.

• Semantic representation of LO: LOM2OWL ontology . Itsstructure allows to describe LOs using IEEE LOM standard.

• Description of works licensed under Creative Commons: CC published the metadata standard ccREL (Creative CommonsRights Expression Language). It aims to "make licensedworks more reusable and easy to find".


OER-CC ontology: Developmentprocess

• Main goals:

– To describe OER and CC resources using a common vocabulary by users and producers, i.e., an implemented ontology offered tostudents and lecturers within an educationalcontext.

– To automate execution of tasks such as information retrieval using semantic webtechniques.



• Process and tools to build OER-CC ontology

– OER ontology development.

– CC ontology development.

– Merging CC and OER ontologies.

As a result, we unified concepts, terminology, definitionsand constraints from both ontologies successfully.




– Development process was based onMETHONTOLOGY guidelines.

• This method proposes an ontology building lifecycle based on evolving prototypes.

• It allows adding, changing and removing termsthrough each new version (prototype).




– Once we had decided how to describe metadata, we selected well-referenced toolsto model OER-CC ontology.

– Concretely, we used CMaptools to representand connect both knowledge domains, i.e., OER and CC respectively.


OER-CC ontology: Concept Map




– Cmaptools Ontology Editor (COE) was used for constructing, sharing and viewing modeled ontology based on CmapTools.

– We selected COE to model OER-CC ontology using OWL language.

– Protégé was used for implementing our ontology formally. Additionally, all prior results were imported to this ontologyeditor.

– We used SWRL for deductive and reasoning capabilities andSPARQL for retrieval of educational resources metadata.



• Instantiation process and tools

– Population process “creates a knowledge base including instances of the ontology concepts andinstances of the ontology relations”

– This process can occur in 3 different ways:

• Manual

• Semi-automatic,

• Automatic,



• OER-CC Ontology Instantation

– To instantiate our OER-CC ontology, we considered someresources generated in the Computer Science School (CSS) ofthe Technical University of Loja (Universidad Técnica Particular de Loja23, UTPL-Ecuador)

– At this point, manual process was selected to populate OER-CC ontology with the CSS' educational resources,

– Instantiation of CC domain was considered using different typesof Creative Commons Licenses with jurisdiction at Ecuador.



• Information Retrieval– We used SPARQL language for knowledge

retrieval of our OER-CC ontology. – Thus, we developed some queries to retrieve

information, e.g., properties of OERs and CC licenses. Initial results showed that our OER-CC ontology has been deployed successfully, i.e., using metadata from initial instantiation ofeducational objects.



• Information Retrieval– Implemented query using SPARQL: Which are direct

contributions of the UTPL at OERs production? andwhich are indirect contributions through theirteachers/students?


Conclusions

– As a main contribution, we have introduced OER-CC ontologyto model knowledge from OER and CC domainsrespectively .

– Secondly, we have been able to inference knowledge using ourOER-CC ontology, i.e., through classification of educationalobjects.

– About work in progress, we continue improving OER-CC ontology. We are deploying methods to promote accessibilityoriented to OER user requirements, e.g., the content of aneducational resource could be ranked within the OER-CC ontology according to student or lecturer profile respectively.


Thank you for your attention

Questions


2010-04-14 educon emadrid umh (upm) oscar martínez

Education

development

cc ontology

cc ontology

universidad

metadata standards

educative

build oer

describe oer