Improving LOM-Based Interoperability of Learning Repositories Germán M. Rivera†, Bernd Simon‡, Juan Quemada†, Joaquín Salvachúa† †
Universidad Politécnica de Madrid, ETSI Telecomunicación Ciudad Universitaria s/n, 28040 Madrid, Spain {rivera, jquemada, jsr}@dit.upm.es ‡ Wirtschaftsuniversität Wien, Information Systems Department Augasse 2-6, 1090 Vienna, Austria
[email protected]
Abstract: This paper analyses the use of LOM Application Profiles for learning object repository interoperability. Based on an exemplifying use case the paper presents a case study, which aims at developing a LOM Application Profile to realized Smart Spaces for Learning. Finally, a schema is designed which selects the necessary LOM elements and makes a LOM-conformant extension to represent usage conditions and learning activities. This work is part of the Elena project, which focuses on integrating advanced educational mediators in a network of federated services.
1
Introduction
Interoperability between learning object repositories – also referred to as repositories for learning, learning repositories, or knowledge pools – still remains a pending issue in information systems research. Repositories for learning hold information on learning objects and make them available according to pre-defined processes. Taking advantage of proprietary user interfaces, users are able to access learning objects in order to perform learning, organize courses, or develop content. Since many of these repositories lack interoperability with other systems, the learning objects accessible are restricted to those stored in the repository. This paper aims to contribute to interoperability research by providing an illustrative case study on an interoperable network of learning object repositories that has been set up on the basis of a Learning Object Metadata (LOM) Application Profile. The paper first defines the application profile notion and relates it to the ontology concept. In this section also guidelines for designing application profiles are discussed. In Section 3 an introduction to the ELENA project, which aims to build Smart Spaces for Learning is given. From this vision requirements for a LOM application profile are derived. The application profile and its design process are presented in Section 4. The application profile is based on a classification of learning objects that distinguishes between learning material and learning activities and customizes the LOM conceptual model according to this differentiation. The paper concludes in Section 5 where the limitations of the current approach are discussed.
2 2.1
LOM Application Profiles Application Profile Definition and Ontologies
Application profiles are defined as an assemblage of metadata elements selected from one or more metadata schemas and combined in a compound schema [1]. They provide the means to express principles of modularity and extensibility. The purpose of an application profile is to adapt or combine existing schemas into a package that is tailored to the functional requirements of a particular application, while retaining interoperability with the original base schemas. This paper deals with LOM Application Profiles. The LOM standard [2] is an accredited IEEE standard and was first released in 2002. Since then its significance has constantly increased. By being referenced also by other specifications such as IMS Metadata and SCORM, it has adopted a key role in the context of learning metadata. Its conceptual model is well known and has been deeply discussed in the e-learning community. So, in order to achieve interoperability between heterogeneous learning repositories, LOM is a good starting point to integrate the information of diverse metadata profiles. Additionally, from a pure business point of view, a (meta)data model that is based on an existing standard such as LOM is attractive for implementers, since it reduces the risk of sunk costs. With the dawn of the Semantic Web the notion of ontology has increasingly become a topic of interest in computer science. In this context, an ontology can be defined as an engineering artefact, constituted by a specific vocabulary used to describe a certain reality, plus a set of explicit assumptions regarding the intended meaning of the vocabulary [3]. According to this definition an application profile can be viewed as a kind of ontology, although application profiles like the one described herein are not represented in an ontology language [4]. Following the classification of ontologies proposed by Guarino [5], an application profile can be considered as an ‘application ontology’ taking advantage of concepts defined in higher level ontologies such as the LOM Standard or Dublin Core. A LOM Application Profile adapts the LOM conceptual model in order to be able to capture all the specific information that an application needs while keeping it as simple as possible. The application ´profile design process requires the following measures: 1. 2. 3. 4. 5. 6. 2.3
Refinement of the learning object notion Selection of LOM elements Semantics refinement Specification of multiplicity constraints Specification of value restrictions Introduction of required extensions
What makes a LOM Application Profile?
The IEEE LOM Standard depicts a rich conceptual model intended for describing heterogenous learning objects. However, for a specific application scenario it may be convenient to restrict the number of elements used in order to reduce the complexity
and scattering of the descriptions. This is crucial in case this profile must serve as a common core model for the learning object descriptions of existing heterogeneous repositories. The profile may also describe how each LOM element is interpreted, but always consistently with the original definitions. Additionally, multiplicity including if the element is optional or mandatory should be specified. However, only a minimum set of elements indispensable for the service should be required otherwise the barrier of providing learning objects might be too high. In order to maintain LOM compatibility, originally non-multiple elements must not be made multiple. In some cases it might turn out to be useful to restrict the value spaces of elements (These value spaces are also referred to as ‘permissible values’). For example, in the presented case study vCard properties also need to be restricted (vCard elements are permissible element values for the LOM Element 2.3.2 Entity). In addition, LOM vocabularies can be restricted selecting the words permitted for each element with the same purpose as of element selection. It is possible that the LOM conceptual model does not cover all aspects of all the types of learning objects managed in an application. In such a case, extensions are needed. The extension mechanism described in the IEEE LOM Standard can be used. LOM can be extended in two ways, elements and vocabulary. New elements semantics must not intersect with the original LOM semantic model. Words added to a vocabulary must be associated with their own source and, of course, must not interfere with the original vocabulary. This action reduces considerably the semantic interoperability of the element with LOM, so implementers must make sure that it is really worthwhile for the conceptual model of the profile. It is recommendable to take non-LOM elements from other standards. Also, mappings of the rest of the elements of the profile to these standards may be specified. This facilitates the conversion of learning object descriptions between standards and so increases the utility of the profile. 2.4
Requirements for LOM Backwards Compatibility
It can be useful for an application to be able to process not only descriptions based on its own application profile, but also pure LOM descriptions. This can be defined as “LOM backwards compatibility” of the application. Such an application and its application profile must fulfill the following conditions: − No extended elements should be mandatory. Obviously, that would discard every pure LOM description. Otherwise, the application should be able to conclude the value of such element when it receives an original LOM description. − LOM elements not present in the application profile must be ignored by the application. That means that every parsing system must overpass those elements and no application must modify nor delete them. − The application must extract as much information as possible from LOM elements. If the complete value is not interpretable, then the element must be ignored. In LOM, there exist some vocabulary elements (semantics-adjusting elements) that alter the meaning of the value of other related elements. For example, Element 2.3.1.
Role modifies the semantics of the Element 2.3.2 Entity. In such a case, the application should discard all related elements if it does not recognize the value of the vocabulary element. An example of a partially interpretable value could be Element 2.3.2 Entity again, whose value (vCard) is supposed to contain only a subset of the properties specified in the vCard 3.0 Standard.
3.
The ELENA Smart Space for Learning
3.1
The Need for Interoperability in Smart Spaces for Learning
The LOM Application Profile presented in this paper has been initiated by research carried out in the context of the IST Project ELENA and is further extended by ISTfunded Network of Excellence ProLearn. The objective of the ELENA Project is to design, implement, and test the applicability of Smart Spaces for Learning [6]. A Smart Space for Learning is defined as a network (space) of learning repositories, which supports the personalized (smart) mediation of heterogeneous learning objects. ELENA is primarily targeting corporate learners. Today’s corporate learners are served by Internet access through their desktop and mobile phone, business-unit specific knowledge repositories and course(ware) databases available through intranets. Leading business organisations are offering its workforce a heterogeneous set of learning objects ranging from traditional seminars over knowledge management activities such as sharing of best practices to e-learning content. While such a sophisticated learning environment creates competitive advantage by intellectually empowering a company’s workforce, some shortcomings limit the benefits, mainly from the perspectives of decision effectiveness, process administration, and IT infrastructure management. The lack of interoperability of learning object repositories, for instance, does not allow for one, unique view on the learning objects offered. As a result, with each repository introduced to the environment, a user’s search costs increase (multiple searches have to be submitted) and the transparency of learning objects offered suffers (some potential target repositories are not considered in a search). This need for interoperability is further supported by a series of qualitative interviews of personnel managers who also would like to provide corporate learners an integrated view on the learning objects available in a corporation [7]. For example, whenever a learning need has been identified, learning objects going beyond traditional courses shall be accessible to the learner. In such a scenario, even company-external repositories shall be considered in a query for learning objects (e.g. an online bookshop, a course broker). Interoperability achieved through a common (meta)data model manifested in a LOM Application Profile is the enabler for an integrated view on heterogeneous learning object repositories. 3.2
Requirement: Semantic Search
Beyond achieving interoperability, a LOM Application Profile also lays down the foundations for a new type of search across multiple targets. Current document-based
search engines, such as the ones typically available on the Web, are mainly supporting navigational search [8], hereby answering queries such as: “Provide me with the URL of the web site mentioning concept X”. However, a network of repositories based on a common LOM Application Profile, can also support other kinds of searches, such as: - Duration-restricted searches, e.g. find me all courses offered between today and the 20th of December, - Location-restricted searches, e.g. find me all courses offered in Madrid and Vienna, - Cost-restricted searches, e.g. find me all learning objects with a net price below 100 EUR. Only such kind of semantic searches allows a system to locate services and activities - opposed to documents and materials - in a user-friendly and reliable manner. A semantic search in this context refers to a search, which is based on some kind of ontology or common data model that connects several systems. 3.3
Differentiating Learning Material from Learning Activity
In Smart Spaces for Learning the distinction between two different types of learning objects, namely Learning Materials (LM) and Learning Activities (LA), is essential and also drives the requirements for the application profile. Learning materials are available asynchronously and can be consumed independent of time and location. An LM is manifested in a physical or digital good. Learning activities, on the other hand, are live events that are delivered according to a schedule at physical or virtual location. An LA is manifested in a synchronous service, which is frequently supported by information technology nowadays. A course constitutes an example of a learning activity, a book an example of a learning material. The distinction between learning activities and learning material is not new [9]. The IMS Learning Design Specification [10], for example, provides means to express LAs as components of an educational experience (called “learning design”) that are based on a pedagogical strategy. The creation of the Learning Activity Management System (LAMS), a tool for creating sequences of learning activities, has even been inspired by this specification [11]. Similarly, in the SCORM Sequencing and Navigation Book [12] LAs are considered as elements of a learning flow that must be delivered to the learner in a pre-defined way. In order to achieve this, the SCORM Sequencing and Navigation Book defines a proper representation model for activities delivered via Learning Management Systems. However, both standardization initiatives present the learning activity notion in a particular context and do not treat an LA as an autonomous entity, whose description is intended to be exchanged between repositories. In order to support searching for LAs and LMs, different concepts are needed for specifying the search as well as the learning object descriptions the search aims to match. Obviously, the time when a LA is offered is an important selection criterion. Beyond schedule information, the location where the activity is offered is considered to be important. In case of an electronic learning material, users also might want to specify the media format. Language and price are common attributes of learning activity and learning material.
A Smart Space for Learning, which is based on an application profile aligned to requirements mention above, is able to provide and can be tested against the following search capabilities: - Only learning objects meeting a user’s language preferences are selected - Only learning objects within a specific price range are selected - Only learning material meeting user’s media format preferences are selected - Only learning activities meeting a user’s location preferences are selected - Only learning activities that are available in the preferred time period The application profile is not only determined by the concepts used for specifying the query, but also by the concepts used when presenting search results. In a Smart Space for Learning the following concepts are presented to the end user: - Title, - Provider, - Description, - Contributors (e.g. Author, Instructor, etc.), - Price Yes/No, - Restrictions Apply Yes/No, - URI to full description - URI to access. A result list including the data like the one above meets following requirements: - Users can determine from the results list if a learning objects is for free, - Users can determine from the results list if usage restrictions apply - Users can see “who is behind a learning object”.
4.
A LOM Application Profile for a Smart Space for Learning
This paper will not describe the LOM Application Profile in detail, but will document the most important decisions concerning its design. The latest version of the application profile specification is available at the Learning Object Repository Interoperability Site [13]. 4.1
Application Profile Design
Refinement of the Learning Object Notion. The application profile introduces two major aspects in to the learning object notion that will guide the design of the semantic model of the profile. On the one hand, it distinguishes between two fundamentally different types of learning objects: learning activities and learning material as defined in Section 3.3. On the other, the brokerage aspect is considered to be predominant. Hence, the application profile provided shall be expressive enough to provide all data that a potential consumer needs to know from a contractual point of view.
Driven by these requirements a learning object in a Smart Space for Learning can be defined as any kind of material or activity that is made available by a learning object provider under specified conditions for the purpose of facilitating learning. Figure 1 shows the classes of the semantic model and its relations. LM information fits properly in LOM conceptual model. That is not the case of LAs and usage conditions, which are partially described in LOM, but inevitably involve some extensions. Contributor roles differ between LA and LM (e.g. author for LM, instructor for LA, etc.). A contributor can either be a person or an organization.
Fig. 1. Simple UML class diagram of the semantic model
Selection of LOM Elements. The elements of the application profile shall be sufficient to allow users to make a selection decision in favor or against a particular learning object. LM and LAs share a set of characteristics that are described by a common group of elements. Most of the LM-specific elements were selected from LOM, although in some cases it has been necessary to refine the value that LOM specifies by inserting a new element. Additionally, LOM covers also some aspects of LAs and usage conditions, what increases the number of LOM elements included in the schema. Based on the requirements specified in Section 3 LOM elements such as title, language, description, cost, copyright and other restrictions were selected. Semantics Refinement. The Application Profile provides a description of how the elements will be interpreted in the context of a Smart Space for Learning. For instance, it specifies whether a LOM element applies to LM, LAs or both. Additionally, LOM elements such as learning resource type is driven by the semantic requirements of the Smart Space for Learning use case, which results in a narrower definition as set forth by LOM itself. Specification of Multiplicity Constraints. While in LOM the occurrence of every element is optional, the application profile differentiates between mandatory elements, essential for efficient resource management, and conditional elements, needed for semantic coherence. In the case of vocabulary elements, multiplicity needs to be specified for each value. For instance, if the application profile element educational objective can only be specified once, LOM Element 9.1 Purpose can only adopt the value “educational objective” once.
Specification of Value Restrictions. In the proposed application profile restrictions are applied to two types of values: LOM Value Spaces and vCard Elements. In the case of “non semantics-adjusting” vocabularies (see Section 2.2) values are restricted in order to limit the value range of a concept, for example in case of LOM Element 5.2 Learning Resource Type. Restrictions of “semantics-adjusting” vocabularies, in the case of LOM Element 2.3.1 Role for example, restrict the possible meanings of the value of its related element, so it produces a similar effect to element selection as it reduces the number of concepts of the schema. vCard Elements considerably increase the complexity of the parser that analyses the LOM (or LOM Application Profile) description due to its particular syntax and rich set of properties. In order to maintain the application profile close to the LOM conceptual model, the syntax is not changed, but vCard admissible properties are restricted to a subset that allows describing essential identification and contact information for both people and organizations. Introduction of Extensions. The application profile has extended LOM both in vocabulary and elements. Apart from LOM Element 5.2 Learning Resource Type, the application profile extends the vocabulary of 2.3.1 Role, which enumerates the possible roles of LM contributors. Extended elements have been included into the respective LOM category. Usage conditions extended elements all correspond to rights category, following the LOM criterion. No extensions have been included exclusively for LMs, but educational category has been extended to describe shared information between activities and materials (apart from usage conditions). On the other hand, LAs are described using extended elements of categories educational and lifecycle, like delivery, described next. 4.1
Overview of Selected Elements and Mappings
LOM distinguishes between nine element categories. Here are presented the top elements (not their selected or extended sub-elements) in each category with a short description of some of them. The schema specification indicates whether each (sub-)element or vocabulary value applies to LA, LM or any learning object (both LA and LM). The general category comprises identifier, title, language and description, used for basic identification of a learning object. Lifecycle comprises contribute, extended to capture all the contributors to LA and LM; version for LM, and delivery, a new element that captures the schedule and location of a LA whose datatype is iCalendar (Dawson & Stenerson, 1998). The third category, metametadata, includes language and contribute, which captures the creation date of the metadata of a learning object. The technical format and requirements of a LM are expressed with the elements format, requirement and other platform requirements of the technical category. In the educational category, learning resource type value space has been widely extended to designate all kinds of LA and LM. Also, five non-LOM elements have been included: the essential learning resource class, whose possible values are learning activity and learning material, minimum and maximum participants for LA, and target learner and target learner profile which, with the LOM element intended
target learner and target learner profile which, with the LOM element intended end user role, designates the target group of a learning object. All the LOM elements from rights category have been selected and they have been complemented with price, VAT, valid thru and special conditions new elements to be able to describe in detail the cost of the use of the learning object and its restrictions. The application profile will also take advantage of the versatility of the classification category and will use restrictively its four LOM elements to express the objective of a LA and the subject and prerequisites of any learning object. On the contrary, since each object is considered isolated from others and will be provided individually, the relation category is not used. Two elements from educational category are selected. Finally, annotation category is worthless for the purposes of the schema. The mappings between LOM and DC specified in the IEEE Draft Standard for Learning Object Metadata are also applicable to the LOM elements included in the application profile with one exception: LOM Element 5.2 Learning Resource Type, which IEEE maps to DC.Type. Since LOM Learning Resource Type refers to LM type in the Common Schema, DC.Type must map to the highest-level classification element of type of the object in the schema: Learning Resource Class. Twelve of the fifteen DC elements can be mapped to Common Schema. Since many of the extended elements have been directly extracted from Open-Qcat [14] standard for education and training services, mappings for those extended elements and LOM included elements (where possible) have been also specified.
5.
Conclusion
This paper illustrated the design of a LOM Application Profile for the purpose of building Smart Spaces for Learning. A generic design process for building LOM Application Profiles is presented and applied. The application profile documented in this paper is driven by a conceptual model, which differentiates learning objects in learning materials and learning activities. Learning objects are described for brokerage proposes, which requires a significant extension of LOM with usage restriction aspects. Learning object repository interoperability is discussed only from a pure data model perspective. Questions related to the design of application program interfaces, such as the Simple Query Interface [15], are not covered by the paper. The model presented herein does not cover how single learning object descriptions can be aggregated using for example IMS Content Packaging specification or the SCORM Content Aggregation Model. Issues related to the actual bindings of the model in XML or RDF constitute additional avenues for further research.
Acknowledgements This work was supported by the IST project ELENA (http://www.elena-project.org/) and the IST Network of Excellence PROLEARN (http://www.prolearn-project.org/), which are partly sponsored by the European Commission. The authors would like to express their gratitude to the following consortium members who actively participated in the model discussion Barbara Kieslinger, David Massart, Dirk Heckler, Erik Duval, Frans van Assche, Nikos Papazis, Peter Dolog, Muth, Sam Guinea, Sandra Aguirre, Stefaan Ternier, Stefan Brantner, Tomaz Klobucar, Wolfgang Nejdl and Zoltán Miklós.
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