Recent Research Developments in Learning Technologies (2005) 1
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Resource descriptions for semantic learning element repositories: addressing flexibility
M.A. Sicilia*,1, S. Sánchez-Alonso1, and J. Soto2
1 Information Engineering Research Unit, Computer Science Department, University of Alcalá, Ctra.
Barcelona km. 33.6 28871 Alcalá de Henares (Madrid), Spain
2 Faculty of Computing, Pontifical University of Salamanca, Paseo Juan XXIII, 3, 28040 Madrid, Spain Recent efforts in the standardization of learning technology have resulted in the emergence of specific terminologies that are used to name and classify learning actors, activities and artifacts. This has raised the need for repositories of learning-oriented entities that provide a high degree of flexibility in their characterizations of learning objects and related concepts. Nonetheless, metadata is oriented to enable functions, and in consequence, there exist a tension between flexibility of description and the specificity and detail required for concrete uses of metadata. This becomes more important in the case that the functions that are desired to be supported are automated fully or partially. This paper describes an ontological schema that attempts to serve as a tool for research on achieving flexibility in description, while retaining specific degrees of orientation to enabling automated functions or delegating tasks to agents. The ontological scheme described can be extended or modified, but it provides a foundation for further analysis and research.
Keywords learning objects; learning object repositories; ontologies
1. Introduction
Recent efforts in the standardization of learning technology have resulted in the emergence of specific terminologies that are used to name and classify learning actors, activities and artifacts (elements in a general sense). McGreal [1] has recently attempted to unify existing concepts into a single definition.
Furthermore, Downes has introduced the notion of resource profiles as multi-faceted, wide ranging description of a resource [2]. These recent essays reflect the fact that repositories of learning-oriented entities would require a high degree of flexibility in their characterizations of learning objects and related concepts. Nonetheless, metadata is oriented to enable functions, as included in Greenberg s definition of metadata as structured data about an object that supports functions associated with the designated object . In consequence, there exist a tension between flexibility of description and the specificity and detail required for concrete uses of metadata. This becomes more important in the case that the functions that are desired to be supported are automated fully or partially. The concepts of scenario [6] and learning object contract [5] have been recently proposed as conceptual frameworks to delineate the execution semantics of these delegated functions. The concept of normative descriptions [8] of learning objects follows the same direction of clearly orienting metadata to the full or partial automation of some tasks.
Formal ontology as a discipline [3] is aimed at studying possibilia, so that it can be used to compare learning element representations according to the flexibility of their coverage and term subsumption properties. This paper describes an ontological schema that attempts to serve as a tool for research on achieving flexibility in description, while retaining specific degrees of orientation to enabling automated functions or delegating tasks to agents. The ontological scheme described can be extended or modified, but it provides a foundation for further analysis and research.
The rest of this paper is structured as follows. Section 2 describes the main definitions of a flexible ontological schema that integrates the different definitions summarized by McGreal [1]. Then, a discussion on how these different characterizations impact on the implementation of concrete automation
* Corresponding author: e-mail: [email protected]
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scenarios is provided in Section 3. Finally, Section 4 provides conclusions and some directions for further research.
2. Main definitions of the ontological schema
According to McGreal [1] in his study of existing learning object characterizations, there are four types of definitions moving from the general to the specific:
1. Anything and everything.
2. Anything digital, whether it has an educational purpose or not.
3. Anything that has an educational purpose.
4. Only digital objects that have a formal educational purpose.
5. Only digital objects that are marked in a specific way for educational purposes.
According to the first definition, the use of an object is what determines whether or not the object becomes a learning object, and thus everything that exist (the universal concept) is considered a learning object. Nonetheless, in software-based representations, the only objects that in practice are considered as existing (in the sense of being able to talk about them) are those that are represented. In our case, the scope of representation is that of the different elements of the ontology. Taking OpenCyc (the open source version of the large Cyc knowledge base [7]) as a case of ontological representation, the term Thing subsumes anything that may eventually be considered a learning object. This definition has the obvious drawback of not adding any defining characterization to the concept.
Learning can be considered an Event, and then everything that is linked to representation of learning activities, or that is declared to have educational purpose in some way should be considered a learning object. In addition, some axioms could automatically classify some things as learning objects.
For example, every Book classifies as a learning object . These are examples of concrete characterization of classes of learning objects, which can be used for practical applications.
In consequence, the first definition may be interpreted in the following way: [1] LearningObject- AsAnything: learning objects are things that either have been used in learning events or have been provided with descriptions that specify possible usages in learning . The latter part of the sentence still requires much clarification, but it can be used provisionally till more detailed clarifications are proposed.
Figure 1 depicts this definitions and the rest that will be commented in what follows.
The second definition introduces the concept of digital object in an attempt to further specifying that learning objects are artefacts. OpenCyc s ComputerFileCopy can be used as a possible characterization of the concept, since it requires unique identification, and is not restricted to data but it subsumes programs in a general sense. Thus the following definition can be used [2] LearningObject- AsAnythingDigital: learning objects are LearningObject-AsAnything instances that are subsumed by ComputerFileCopy .
The third definition introduces a consideration of purpose. In this case, the purpose should be interpreted as something that was present in the act of Designing the learning object, which entails the associated restriction of learning objects to be Artefacts, i.e. products of some Agent that purposefully created them, which separates them from natural things. This leads to the definition [3]
LearningObject-AsAnythingWithEducationalPurpose: learning objects are LearningObject- AsAnything instances that have somewhat a record of the educational purpose put in the object in the act of its Designing .
Since the purpose in the design is an intellectual process, here a notion of record of that purpose should be introduced. Of course that such purpose may be internal to the learning object, e.g. the objectives section in a Web page, but it can also be tacit, i.e. when it takes a form that is easily recognizable as an educational artefact. This may be the case of presentations in PowerPoint. That notion of record of the purpose is deliberately kept open to divergent interpretations.
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Figure 1. Representation of different overlapping characterizations for learning objects Definitions (2) and (3) provide two characteristics that are essential to every learning technology specification as SCORM or IMS LD. They are concerned with Web contents (i.e. they are digital entities, even though some of them may be considered as digital surrogates to non-digital things), and they have some recorded metadata that is oriented to describe their educational purpose. Note that even in the case that no educational-specific metadata is provided, the mere existence of the metadata record in a standardized form is a sign of the fact that the digital entity was conceived for education or learning. In terms of the IEEE LOM standard this is to say that even if we leave the Educational metadata category empty (which is conformant with the standard), the Web contents can still be considered as learning objects. This finally leads to the definition [4] LearningObject-OtherSpecificAccounts: learning objects are entities subsumed both by LearningObject-AsAnythingDigital and LearningObject- AsAnythingWithEducationalPurpose that have in addition some specific form in their records and contents. This definition can be roughly considered to cover definitions four and five of McGreal, even though some other interpretations of McGreal text could also be possible.
The important point in the just described characterizations is that flexible learning object repositories should cover all these characterizations and even other that mix aspects of the described ones under a unique ontological schema.
3. Enabling functions through metadata
Greenberg (2003) defined metadata as structured data about an object that supports functions associated with the designated object . The fact that metadata is created to support some specific function is sometimes overlooked or vaguely acknowledged. Even though some functions are tacit in metadata, e.g.
a subject metadata element is obviously intended for the function of discovery, or cost is intended for a purchase activity, metadata creators are often not concerned with the concrete details of the requirements of the functions that will eventually make use of the metadata records they generate. Each of the above described learning object characterizations entails a different kind of requirement both on the form of the meatadata used to describe the object and also on the kind of functions that can be enabled through them. Table 1 summarizes the main considerations about this issue.
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Table 1 Type of functions enabled by the different characterizations of learning objects discussed.
Characterization Required metadata Type of functions enabled
[1]LearningObject-AsAnything None Human consumption
[2]LearningObject-AsAnythingDigital None Human consumption; tacit semantics
[3]LearningObject-
AsAnythingWithEducationalPurpose
Something Human consumption; tacit semantics with purposeful data fields.
[4]LearningObject-OtherSpecificAccounts Mandated by an Specification
Human consumption; tacit semantics with purposeful and previously agreed data fields, including common vocabularies Ontology-based descriptions conformant
with specifications
Mandated by an ontology that represents the Specification
All the above plus formal semantics, with provide room for inference.
If we consider definition (1) then the concept of required (i.e. mandatory) metadata is not applicable.
Consequenly, the type of functions necesarily enabled are either those oriented to human-consumption.
This is not to say that all the resources in that categorization are not machine-processable, but the freedom in description and denomination of what a learning object is does not guarantee it.
Defnition (2) adds the digital requirement, thus introducing the possibility of exploiting tacit semantics [9], i.e. those that can be extracted by mining and processing the contents of the objects. This includes the use of existing text summarization, keyword extraction and indexing that are used in information retrieval. Nonetheless, it should be noted that this kind of semantics, despite being useful, do not provide any significant novelty to the tools that are today commonplace in search engines.
Definition (3) introduces the requirement for some kind of description. This represents an advance in that actual metadata has to be provided. Nonetheless, this can be simply an annotation in free form, which does not provide much in the general case in terms of opportunities for automated processing.
Definition (4) goes an step further in the formalization of metadata by considering that metadata records must be conformant to some previously agreed Specification. This is in fact the current state of learning technology specifications as LOM and SCORM, and provides much improved room for the exploitation of metadata due to higher levels of structure.
The last row in Table 1 represents an step further in the degree of structure. Concretely, it mandates that metadata records are provided in ontological terms, but not only as mere translations of the specifications. It is required that the descriptions are connected to large existing ontological structures, which provides the increased opportunities needed for inferencing and exploitation of knowledge [10].
For example, the Coverage metadata descriptor in LOM should be expressed in terms of ontologies as the Getty TGN1, which provides a comprehensive and coherent representation of geographical entities.
This enables formal semantics [9] and formal inference in addition to the reuse of knowledge inherent to descriptions connected to large domain ontologies.
4. Conclusions
The different conceptions of learning objects as summarized by McGreal [1] lead to different ontological characterizations of learning objects. If a repository of learning object is to cover such different notions, it requires associated definitions for these different characterizations. This paper has sketched a possible schema for that purpose. In addition, the kinds of functions that are entailed by each of the characterizations have been discussed.
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Acknowledgements This work has been supported by the UAH-PI2005-070 project of the University of Alcala.
References
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[5] M.A. Sicilia and S. Sánchez. On the concept of learning object "Design by Contract". WSEAS Transactions on Systems, 2(3), 2004, pp. 612-617.
[6] M.A. Sicilia and M. Lytras. Scenario-Oriented Reusable Learning Object Characterizations. International Journal of Knowledge and Learning, 1(3) (to appear 2005).
[7] D.B. Lenat. Cyc: A Large-Scale Investment in Knowledge Infrastructure. Communications of the ACM 38(11), 1995, pp. 33 38.
[8] S. Sánchez and M.A. Sicilia. Normative Specifications of Learning Objects and Learning Processes: Towards Higher Levels of Automation in Standardized e-Learning. International Journal of Instructional Technology and Distance Learning 2(3), 2005, pp. 3-12.
[9] A. Seth et al. Semantics for the Semantic Web: The Implicit, the Formal and the Powerful. Intl. Journal on Semantic Web and Information Systems 1(1), 2005, pp. 1-18.
[10] M.A. Sicilia and E. García. On the Convergence of Formal Ontologies and Standardized e-Learning. Journal of Distance Education Technologies 3(2), 2005, pp. 13-29.
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