Current standards and concepts for educational metadata focus on content-centred approaches and models of instructional design. Scenarios that concentrate on how to structure and organize access to learning objects are mirrored in concepts such as content packaging. Standards focus on search, exchange and re-use of learning material, often called content items, learning objects or training components. The Learning Object Metadata specification, in particular, aims at metadata to facilitate the generation of consistent lessons composed of de-contextualised and distributed learning objects (e.g., consistence in the level of difficulty). Its vision is to enable computer agents to automatically and dynamically compose personalized lessons for an individual learner. The IMS Learning Design specification goes a step further, by providing a conceptual model that enables authors to describe processes and activities including social interaction. The MASIE Centre Report (MASIE Centre, 2002) identifies four main uses of metadata today: categorisation of content, generation of taxonomies, reuse, and dynamic assemblies. All uses are directly or indirectly relevant to adaptation / personalisation.
As already mentioned, current, generic ALEs that support adaptive course delivery require an additional level of information about the entities that make up a course, namely the interrelationships between the entities (Brusilovsky, 2003). The primary goal in seeking standardisation in this dimension is to make it possible to have declarative definitions of relationships and concepts, leaving their procedural interpretation and implementation to each ALE. Using these, different systems may choose to provide different adaptive features or support different types of personalisation, much in the same way that systems differ in how they present standardised modules.
(De Bra et al., 2002a), for example, address the definition of higher-level concept relationship types and the automatic translation of instances of such types into lower-level adaptation rules for the AHA! adaptive e-Learning system. Some of the relationship types discussed therein denote direct relationships between concepts and learning elements (e.g., concept A is a prerequisite for concept B, element X exemplifies concept C), while others bear a clear adaptation / knowledge inference flavour to them (e.g., element Y when read provides knowledge towards concept D, or, element Y when read indicates interest in concept E).
At a lower level than De Bra, we also need to be able to define “assets” associated with “learning objects / elements” which can have standardised relationships to each other and to the enclosing object. Consider, for example, two mutually exclusive elaborations of a given concept, one being brief and the other detailed; contrast that with two complementary elaborations of a given concept, the first being a required brief reading, while the second being an auxiliary amendment to the first. This also implies the possibility to define learning elements that are (more or less) atomic chunks of learning material, distinct from “pages” and with arbitrary granularity (e.g., a paragraph).
Currently, defining relationships such as the ones described above, can be achieved through the use of Learning Object Metadata, if the following conditions are met:
A “vocabulary5” is developed defining the relationships between concepts, as well as the characteristics of these relationships (e.g., transitivity), so that their interpretation by application software is not open to interpretation.
Every learning entity that is an individual “concept” has an associated LOM-compliant metadata record.
The entity’s metadata specify the entity’s relationships with other entities, using the aforementioned relationship vocabulary and the entities’ identifiers.
This approach has the benefit of compliance with current standards, and requires only the introduction of a new, adaptation oriented vocabulary for relationships. A similar approach would be to introduce dedicated (optional) adaptation-specific constructs in the main course description. The latter, however, would evidently require modifications to standards commonly used to define courses, which may be considered a much higher (as compared to the above approach) “entry cost” for introducing adaptation in e-Learning standards. A third option would of course be to keep adaptation-related information / metadata separately than the description of the course itself. This has the benefit of rendering the two rather independent, but would most likely prove problematic in terms of course maintenance. This is especially the case as far as “synchronisation” between the two is concerned.
Thus far we have discussed the case of characterising relationships between existing course objects / elements. However, as pointed out in (Brusilovsky, 2003), some types of adaptation require a model that is different than (although connected to) the main course model. For example, a model of course concepts and their semantic relations may need to be maintained “separately” from the model of physical course-material organisation (e.g., files, navigation hierarchy). Apparently, whether the two are separate or not, there must exist associations from one to the other, so that the system knows which concepts correspond to given resources, and vice versa. Standardisation in this direction would evidently necessitate new standards: such concerns are beyond the traditional approaches to organising and describing course material and activities.
Examples of ALEs that extend existing standards to support adaptive course delivery include OPAL, OLO and KOD, among others. OPAL (Conlan et al., 2002), which delivers content personalized to the learner’s cognitive and presentation learning preferences using aggregation models based on ADL SCORM. OLO (Rodriguez et al., 2002) and KOD (Karagiannidis et al., 2001) (see Figure 1, (a) and (b) respectively) both address the topic of extending the metadata that accompanies “packaged” learning objects, with the intention to facilitate adaptation. Although the projects take considerably different routes, they are largely motivated by the same objective, to augment the “traditional” metadata with additional elements that are vital when one is to decide upon, and apply course-oriented adaptations. Furthermore, both projects attempt to “integrate” adaptation metadata with the traditional course information (e.g., KOD incorporates the adaptation logic –rules– in an extension to the organization element of IMS CPS).
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