An international analysis of the extensions to the IEEE LOMv1.0 metadata standard

https://doi.org/10.1016/j.csi.2013.04.006Get rights and content

Highlights

  • We analyzed 44 works using the IEEE LOMv1.0 standard and found 15 types of extensions made to it.

  • Due to Mexico interoperability difficulties, we compared its extensions with the rest of the world.

  • We found that local extensions do not help to increase the system's interoperability ability.

  • We found the action most important after implementing extensions is to publish them.

Abstract

Metadata is an important element for achieving interoperability between Learning Objects systems; it facilitates the process of describing, searching, selecting and recovering of Learning Objects. The IEEE LOMv1.0 is a metadata standard for describing Learning Objects. Recently found evidence shows that the standard does not fulfill all requirements of its users, therefore they have extended it. In order to know the impact that extensions have into the interoperability, we made an international study where the use of the LOMv1.0 standard in forty-four works was analyzed. As a result we found fifteen types of extensions implemented to the standard.

Introduction

Metadata itself is information about an object, service or resource, whether it is physical or digital. In the e-Learning context metadata is used for describing the features of digital resources, such as aspects about its content, learning objectives, type of resource, size, among others. This information allows deciding whether an information resource is recovered or not for its use. When it comes to e-Learning, digital resources are commonly known as Learning Objects, which are defined as independent and self-standing units of learning content that are predisposed to be reused in multiple instructional contexts [41]. Learning Objects are stored in repositories (LORs), or in a specific module of learning environment (LE). In this paper LOR and LE will be referenced as Learning Objects environments (LOEs).

Reusability is an inherent property of Learning Objects, according to its definition, reusability is the property that allows Learning Objects to be used more than once in multiple instructional contexts, whether to be part of a larger Learning Object, or to be part of a course. In order to enable the reusability of Learning Objects it is necessary to achieve the interoperability, which Geraci [18] define as the ability of two or more systems or components to exchange information and to use the information that has been exchanged. The above applied into the e-Learning context is translated as the ability of two or more LOE to exchange Learning Object and reuse them.

Learning Objects and LOE are the fundamental elements that organizations should have when attempting to reach the interoperability. On the other hand, in [31] it is mentioned that adopting the use of technologies, specifications and standards for the interoperability is an essential practice to increase the chances of achieving this characteristic. The adoption is recommended in the development of LOE, development of Learning Objects, description of Learning Objects, packaging of Learning Objects, communication among heterogeneous systems, and content search.

Using common technologies, specifications and standards in each one of these activities is very important due to the fact that it represents an agreement on how to develop, communicate and share resources, models and systems, with the objective of achieving the interoperability between at least two organizations. Therefore it is important to analyze very closely indeed the scope and limitations of technologies, specifications and standards intended for supporting these activities. On this paper we will focus on the usage of standards for describing Learning Objects, specifically using the IEEE LOMv1.0 metadata standard. We decided to take this approach for the following four reasons:

  • 1.

    We are interested on the interoperability between LOE topic, and as mentioned above, metadata represents an important element for reaching this characteristic.

  • 2.

    The IEEE LOMv1.0 standard is the most used model for describing Learning Objects on the educational field [4].

  • 3.

    Despite the fact that the IEEE LOMv1.0 standard is the most referenced in the literature, the recent amount of work done on extensions of the standard in the form of metadata profiles shows evidence that the standard does not cover all requirements of its users. Therefore users have decided to use the standard differently to what's been previously established by it. In other words, they have implemented extensions to the LOMv1.0 standard in order to fulfill their requirements in metadata terms.

  • 4.

    Derived from the previous point, for us it is important to identify the different ways in which the standard has been used, and of course, their impact on interoperability.

The objective of this paper is to present an analysis of the extensions made to the IEEE LOMv1.0 metadata standard which have been reported in specialized literature of works from around the world. We will describe each extension, its justification and its main characteristics. Also as a case of study, we will present a comparative analysis of the extensions made in Mexican works and the ones implemented on works from the rest of the world. All of the above intend to highlight the implications of the extensions for achieving the interoperability.

It is important to mention that we decided to take Mexico as a case of study because of its difficulties to successfully achieve the interoperability between its LOE. Nevertheless we believe that these problems could be present on any other country with comparable circumstances to Mexico from Latin American region, in such things like language, culture, education, science and technology, economy, social organization and politics.

The remainder of this paper is organized as follows: In the rest of Section 1 the importance of using metadata in order to achieve the interoperability is presented, afterwards the IEEE LOMv1.0 metadata standard is described. After that the topics of metadata application profiles and the mechanisms for extending the IEEE LOMv1.0 standard are described. As a last point in Section 1, the necessity of extending the LOMv1.0 standard is stressed. In Section 2 the analysis of the extensions made to the IEEE LOMv1.0 standard is presented. In Section 3 the case of study is presented. In Section 4 a summary of the extensions implemented is presented. In Section 5 some recommendations are described, and at the end in Section 6 the conclusions reached are exhibited.

National Information Standards Organization (NISO) defines metadata as structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use and manage an information resource [37]. Barker and Campbell [5] argue that this definition highlights two important parts, the first one distinguishes metadata from unstructured textual descriptions and the second one highlights that metadata exists to facilitate a range of activities among these discovery information stresses.

Describing Learning Objects with metadata is suitable, since it actually facilitates the discovery of relevant information. In addition to that, metadata can help to organize electronic resources, facilitate interoperability and legacy resource integration, provide digital identification, and support archiving and preservation of the resources [37].

Metadata becomes an essential element for interoperability between LOE because it represents the first approach towards the reusability of Learning Objects, since metadata facilitates searching, selecting, and recovering of resources. The reusability process begins by searching metadata that describe the resources. Once the attributes of Learning Objects and the physical location of the resources are known, it is possible to decide whether the Learning Objects are recovered or not for its use and its possible reuse. The first part of the reusability process is only possible in a scenario where a common metadata model is presented, so that metadata of a LOE “A” can be interoperate with a LOE “B” [35].

The above scenario stresses the importance of the use of a common metadata schema between LOEs for searching for learning objects that will generate useful results to show the characteristics of learning objects stored in the LOEs. The counterpart of the above takes place when the metadata schemas known by LOEs are different. In this case the LOEs do not share the search parameters and consequently the chances of retrieving the information describing Learning Objects, when a search is done, are few. This is because the search response could be empty, null, or wrong. A scenario of the previous situation is the following: “A” and “B” are two LORs which work with different metadata schemas. “A” launches a query for searching Learning Objects into “B”, the query is received but as mentioned before this LOR does not use the same metadata schema as “A”, therefore it is not able to interpret the query and its response to the request is empty (in the best case scenario). In this case “A” does not retrieve metadata describing the Learning Objects from “B”, hence it does not know the Learning Objects features then the possibility of “A” retrieving resources from “B” decreases.

Metadata schemas are sets of metadata elements designed for a specific purpose, such as describing a particular type of information [37]. IEEE LOMv1.0 is a multi-part standard that specifies a conceptual data schema that defines the structure of a metadata instance for a Learning Object, to describe the characteristic of Learning Object which applies [20]. IEEE LOMv1.0 metadata schema is divided into nine categories: General, Lifecycle, Meta-metadata, Technical, Educational, Rights, Relation, Annotation and Classification. These categories of grouped elements allow storing the attributes that characterize a Learning Object (e.g. title, description, learning resource level, size, etc).

Bourda and Delestre [6] exposes that IEEE LOMv1.0 is not the only standard used to describe Learning Objects. Nevertheless Al-Khalifa and Davis [4] cite that it is the most used model since it is the first technical e-Learning standard and it's widely accepted in the e-Learning communities. As mentioned previously we will focus on the IEEE LOMv1.0 standard and the different ways in which it has been used.

In Duval, Smith and Coillie [14] it is mentioned that the goal of standardization is for producing an acceptable specification that does not impose restrictions that could limit its general adoption. Likewise, it is mentioned that nature of standards dictated that they must cover every conceivable circumstance. Thus standard implementers focus on the needs of their particular interest and therefore choose subsets of possible options and interpretations while conforming to the underlying standards, may limit potential for interoperability in future. For example, a standard might have provisions for multi-languages but if a specific community uses a common language they may be able to simplify their implementation considerably.

“The application profile itself is best expressed as a conceptual data model. This should take the form of a text document or table, and should include an explanation of the overall structure, coverage and target audience for the application profile along with an exhaustive listing of all the data elements included. Each data element should be described using one or more attributes. A simple textual representation such as this makes the application profile accessible to as wide a constituency as possible”.

Rachel and Manjula [43] argue that a profile application is defined as data elements drawn from one or more namespace schemas combined together by implementers and optimized for a particular local application. Duval et al. [14] expose that defining application profiles represents the normal way of addressing the needs for interoperability between systems and organizations, because it fulfills specific requirements of a particular community of practice while retaining interoperability with the base schema and they defined new needs openly.

The general underlying principle when producing an application profile is that it should either be based on one or more standards, or on one or more existing application profiles of those standards and it should not compromise interoperability by breaking conformance with the existing standards. The general steps for defining an application profile according to Duval et al. [14] and Manouselis et al. [34] are the following:

  • 1.

    Start from your own requirements. The basic goal of an application profile is to support specific requirements of a particular context through a profile of a generic standard. In order to bootstrap this process, it is important to have an explicit understanding of those specific requirements.

  • 2.

    Select data elements that will be part of profile. Once the requirements are clarified, a first important decision in the actual development of metadata application profiles is the selection of data elements that the application profile will be built from.

  • 3.

    Deal size and smallest permitted maximum. Values for some data elements may be allowed to be present multiple times in one metadata instance. LOM defines the cardinality of data elements through the size of the data element. Defining the size of data elements consists on establishing how many instances of each element are allowed.

  • 4.

    Add local data elements. Besides mixing and matching data elements from several base standards, an application profile may also include local data elements.

  • 5.

    Specify the use of data element. Once the full set of metadata elements to be included in the application profile has been decided upon, the status of these data elements can be defined. Typical values for the status are mandatory, conditional, recommended, and optional.

  • 6.

    Define value space of data elements. In parallel with, or after the last step, the data elements' value space must be defined. The value space defines the set of values where the data element defines its value. The application profile could be more restrictive about the value space of a data element than the base standard, however it cannot be less restrictive.

  • 7.

    Establish relationships and dependencies between data elements. More complex inter-relationships and dependencies between data elements can also be defined in an application profile. The application profile could be more restrictive about such inter-relationships than the base standard, however it cannot be less restrictive.

  • 8.

    Profile data type. In the LOM standard, the data type “indicates whether the values are LangString, DateTime, Duration, Vocabulary, CharacterString, or Undefined”. In effect, the data type in LOM is a metadata schema in its own right. All the rules defined above for application profiles of metadata schemas are thus also applicable to data types.

  • 9.

    Define application profile binding. The general rule on the level of a binding of an application profile (e.g. in XML or RDF) is to make sure that any instance that conforms to the relevant binding of the base standard also conforms to the binding of the application profile. For instance, in XML bindings it is important to make sure that application profile data element names are either the names from the corresponding data elements in the base standard, or declared explicitly as subclasses of these data elements.

The IEEE LOMv1.0 specification document [20] and guidelines for producing metadata application profiles [14] identified the following four allowed mechanisms for implementing extensions to the LOM standard.

  • 1.

    Using the Classification category. The IEEE LOMv1.0 metadata standard provides the Classification category as an extension mechanism, which allows referencing local classification systems without breaking the standard schema. This makes it possible to access additional information that does not have a place on the IEEE LOMv1.0 structure (due to convenience or limitations of the standard) via relation of Learning Object to other metadata structures. When this mechanism is used, it is important to keep in mind that if an external consumer system does not have access to the classification systems, then it cannot search Learning Objects through metadata from alternative systems.

  • 2.

    Adding New Metadata Elements. Adding metadata elements to the original standard metadata schema is another way of associating information of a Learning Object. This extension mechanism of the IEEE LOMv1.0 standard has the characteristic that metadata elements added just have local scope, since the elements only have value for the organization that implements the extension. As such, the possibility that elements added may be shared with an external system is limited since as mentioned before, the elements have a local scope and it is not possible to launch searches on them.

  • 3.

    Extending the value list of the vocabulary. This mechanism is similar to the last one, the difference is that instead of adding metadata elements, values to the vocabulary list are added.

  • 4.

    Specifying the usage of metadata elements. All metadata elements of LOM are optional, e.g. the users can decide whether they are used or not when a Learning Object is labeled. Using these extensions mechanism allows to set the order of use of the metadata elements, indicating whether the use of metadata elements is necessary, recommended, or optional.

The adoption of the IEEE LOMv1.0 metadata standard ensures in part, that if a common conceptual data schema is specified, then the consumer systems of Learning Object metadata should have a high degree of semantic interoperability [20]. Despite this promise several situations that limit the system's interoperation ability have been detected. The most common case takes place when a user of LOMv1.0 identifies that it does not have necessary elements for describing Learning Objects according to its requirements thus he decided to modify it. This situation has led LOM's users to use it on a different way than the originally established, creating a customized version of the standard, in other words the user is extending the standard.

Implementing extensions is an action entirely valid, justifiable and beneficial, since it allows meeting specific needs of the standard users. Nevertheless, there are four troublesome situations that may be presented when a LOR that uses the IEEE LOMv1.0 original schema, sends to another one a search for retrieving the metadata describing a Learning Object that was labeled with a LOM extended metadata schema:

  • 1.

    Impossibility for retrieving the additional information describing Learning Objects. In this case extra data are added to the search results because of the extended schema.

  • 2.

    Impossibility for retrieving the metadata of a Learning Object due to incompatibility between search parameters.

  • 3.

    Lack of mechanisms for understanding and interpreting the metadata that are not part of LOM.

  • 4.

    Difficulty for displaying the requested resources in the external LOR.

The first situation occurs when the extensions are fully implemented according to the IEEE LOMv1.0 specification document and guidelines for producing metadata application profiles. In this case, all information added to the standard schema, such as metadata elements, value list vocabulary, and local classification systems, are available only on an internal or local ambit. Therefore it is no possible to an external LOR to search Learning Objects using this information.

The other three problematic situations are presented when extensions are implemented in a different way as stipulated by both IEEE LOM specification document and its guidelines for producing metadata application profiles. The extensions will be described in the next section.

Section snippets

Analysis of the extensions made to the IEEE LOMv1.0 metadata standard

The study of the extensions made to the IEEE LOMv1.0 metadata standard covers from 2009 to 2011. We analyzed the use of the standard on forty-four reported works from around the world (see Table 1), where some of these are technical reports of metadata application profiles and other ones are works reporting the use of LOMv1.0 standard for labeling Learning Objects. We were looking for differences with the LOMv1.0 schema, extensions, as well as any other use of LOM standard for profiling

Case of study: A comparative analysis of the international extensions and the implemented in Mexico

For the comparative analysis between the international extensions and a case of study, we have selected Mexico for two main reasons: the first one because we have first hand information about Mexican works, and the second one is because Mexico is an example of a country that has shown problems to achieve interoperability between LOE. Nevertheless, it could have been possible to choose any other country with similar features to Mexico, such as, educational technology advances, technologic

Summary of extensions implemented

In this paper we described the extensions that several organizations from around the world have made to the IEEE LOMv1.0 metadata standard. The extensions were obtained as a product of reviewing and analyzing forty-four works about using the standard. As a result of the above, fifteen types of extensions were found (see Table 2). According to their characteristics, the extensions were classified into five categories, Addition of Elements, Exclusion of Elements, Modification to Standard Schema,

Recommendations

In this section we will present some recommendations we reached after making the study presented in this paper.

Conclusions

Metadata is a key element for achieving interoperability between LOE, it facilitates the actions of searching, selecting, and recovering of Learning Objects. Also metadata represents the first approach towards reusability of these resources because metadata Learning Object's attributes are known and from these it may decide whether the resources are not retrievable for their use. Therefore, it is important that LOE exchange resources work with a common metadata schema and thus the processes of

Acknowledgments

This work was partially supported through the fellowship number 205224 for the first author granted by The National Council on Science and Technology (CONACyT) (http://www.conacyt.mx).

Lorena Castro-García received her Master's degree in Computer Sciences from Universidad Autonoma de Baja California, Mexico in 2009. She is currently a PhD student in Computer Sciences from Master and Doctorate on Sciences and Engineering Program of Universidad Autonoma de Baja California, Mexico. Her research interest includes e-Learning, Standards for e-Learning, Interoperability between Learning Systems, and Metadata topics.

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    Lorena Castro-García received her Master's degree in Computer Sciences from Universidad Autonoma de Baja California, Mexico in 2009. She is currently a PhD student in Computer Sciences from Master and Doctorate on Sciences and Engineering Program of Universidad Autonoma de Baja California, Mexico. Her research interest includes e-Learning, Standards for e-Learning, Interoperability between Learning Systems, and Metadata topics.

    Gabriel Lopez-Morteo holds a PhD degree from the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada (CICESE). He is working as a researcher at Universidad Autonoma de Baja California in Mexicali, Mexico since 2004. His research interest includes Learning Objects, Learning environments, standards for e-Learning and Mobile Learning.

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