What is the need?
In recent years we have witnessed an increasingly heightened awareness of the potential benefits of adaptivity in e-Learning. This has been mainly driven by the realization that the ideal of individualized learning (i.e., learning tailored to the specific requirements and preferences of the individual) cannot be achieved, especially at a “massive” scale, using traditional approaches. Factors that further contribute in this direction include: the diversity in the “target” population participating in learning activities (intensified by the gradual attainment of life-long learning practices); the diversity in the access media and modalities that one can effectively utilize today in order to access, manipulate, or collaborate on, educational content or learning activities, alongside with a diversity in the context of use of such technologies; the anticipated proliferation of free educational content, which will need to be “harvested” in order to “assemble” learning objects, spaces and activities; etc.
There exist currently several systems which employ adaptive techniques to enable or facilitate different aspects of learning (Brusilovsky, 1999). An important observation one can make going over the related literature is that a dichotomy appears between typically commercial, standards-based e-Learning systems on the one hand, and (typically research prototypes of) adaptive learning environments (ALEs) on the other, with little, if any, standards compliance. It is argued that this dichotomy is, in part, due to the lack of sufficient support for adaptive behaviour in existing e-Learning standards.
In support of this argument, this paper explores the concept of adaptivity in the context of computational learning environments. Furthermore, it attempts a high-level assessment of the sufficiency of existing e-Learning standards for driving the convergence of the two strands of systems outlined above. The intention is to provide a preliminary assessment of the adequacy of existing e-Learning standards for specifying, and guiding the implementation of, adaptive behaviour within learning environments.
The motivation for seeking standardization in adaptive e-Learning is directly linked to cost factors related to the development of ALEs and adaptive courses thereof (e.g., higher initial investment, higher maintenance costs) and the low level of reuse possible in the field today (due to proprietary models and representations of system knowledge, adaptation logic, etc.) (Conlan et al., 2002a). Our rationale can be briefly outlined as follows:
To protect the high investment necessary for the development of adaptive learning material, one has to ensure that the latter is not bound by proprietary standards and formats. This is a main prerequisite for enabling the transfer of such material to new environments.
Taking this concept one step further, one may need to ensure that different learning environments can inter operate in the context of adaptation. A typical exemplary setup might involve one environment holding an individual user’s model and interaction / learning history, and another acting as a content repository.
At the same level, but worth individual mention, is the case of content discovery and aggregation. This introduces an entirely new dimension, as content “characterization” through metadata provided by its initial author / designer, can now be augmented with aspects relating to the use of that content by individuals and groups, and collected as part of the adaptation “cycle”. Furthermore, by combining findings from several compatible systems, which serve the same adaptive course to a multitude of users, it would be possible to make improvements to the course itself. These could be effected wither in a fully automated way, or in a “semi-automated” one, in cases where it would be preferable that no modifications are made to courses without prior approval by human experts.
Departing from the “traditional” treatment of the learner as a solitary, mostly passive receptor of information, one would also need to account for adaptive support in the context of collaborative learning activities. Such activities may be carried out from within the same or “compatible” learning environments, which, in turn, points to a different level of inter operation requirements between such environments.
What is adaptive Learning?
The term “adaptive” is associated with a quite range of diverse system characteristics and capabilities in the e-Learning industry, thus making it is necessary to qualify the qualities one attributes to a system when using the term. In the context of this paper, a learning environment is considered adaptive if it is capable of: monitoring the activities of its users; interpreting these on the basis of domain-specific models; inferring user requirements and preferences out of the interpreted activities, appropriately representing these in associated models; and, finally, acting upon the available knowledge on its users and the subject matter at hand, to dynamically facilitate the learning process. The preceding informal definition should differentiate the concept of adaptivity from those of tailorability / configurability, flexibility / extensibility, or the mere support for intelligently mapping between available media / formats and the characteristics of access devices. Please note that in several places in this paper, the term “adaptation” is used as a synonym for “adaptivity”.
Adaptive behaviour on the part of a learning environment can have numerous manifestations. Instead of attempting to exhaustively enumerate all of these, we will provide a high-level categorization, which suffices for the analysis in the following section. The broad and partially overlapping categories that we will be referring to are: adaptive interaction, adaptive course delivery, content discovery and assembly, and, finally, adaptive collaboration support. Each of these categories is briefly qualified below, followed by an overview of the models and processes that are typically instated in adaptive e-Learning systems.
In recent years we have witnessed an increasingly heightened awareness of the potential benefits of adaptivity in e-Learning. This has been mainly driven by the realization that the ideal of individualized learning (i.e., learning tailored to the specific requirements and preferences of the individual) cannot be achieved, especially at a “massive” scale, using traditional approaches. Factors that further contribute in this direction include: the diversity in the “target” population participating in learning activities (intensified by the gradual attainment of life-long learning practices); the diversity in the access media and modalities that one can effectively utilize today in order to access, manipulate, or collaborate on, educational content or learning activities, alongside with a diversity in the context of use of such technologies; the anticipated proliferation of free educational content, which will need to be “harvested” in order to “assemble” learning objects, spaces and activities; etc.
There exist currently several systems which employ adaptive techniques to enable or facilitate different aspects of learning (Brusilovsky, 1999). An important observation one can make going over the related literature is that a dichotomy appears between typically commercial, standards-based e-Learning systems on the one hand, and (typically research prototypes of) adaptive learning environments (ALEs) on the other, with little, if any, standards compliance. It is argued that this dichotomy is, in part, due to the lack of sufficient support for adaptive behaviour in existing e-Learning standards.
In support of this argument, this paper explores the concept of adaptivity in the context of computational learning environments. Furthermore, it attempts a high-level assessment of the sufficiency of existing e-Learning standards for driving the convergence of the two strands of systems outlined above. The intention is to provide a preliminary assessment of the adequacy of existing e-Learning standards for specifying, and guiding the implementation of, adaptive behaviour within learning environments.
The motivation for seeking standardization in adaptive e-Learning is directly linked to cost factors related to the development of ALEs and adaptive courses thereof (e.g., higher initial investment, higher maintenance costs) and the low level of reuse possible in the field today (due to proprietary models and representations of system knowledge, adaptation logic, etc.) (Conlan et al., 2002a). Our rationale can be briefly outlined as follows:
To protect the high investment necessary for the development of adaptive learning material, one has to ensure that the latter is not bound by proprietary standards and formats. This is a main prerequisite for enabling the transfer of such material to new environments.
Taking this concept one step further, one may need to ensure that different learning environments can inter operate in the context of adaptation. A typical exemplary setup might involve one environment holding an individual user’s model and interaction / learning history, and another acting as a content repository.
At the same level, but worth individual mention, is the case of content discovery and aggregation. This introduces an entirely new dimension, as content “characterization” through metadata provided by its initial author / designer, can now be augmented with aspects relating to the use of that content by individuals and groups, and collected as part of the adaptation “cycle”. Furthermore, by combining findings from several compatible systems, which serve the same adaptive course to a multitude of users, it would be possible to make improvements to the course itself. These could be effected wither in a fully automated way, or in a “semi-automated” one, in cases where it would be preferable that no modifications are made to courses without prior approval by human experts.
Departing from the “traditional” treatment of the learner as a solitary, mostly passive receptor of information, one would also need to account for adaptive support in the context of collaborative learning activities. Such activities may be carried out from within the same or “compatible” learning environments, which, in turn, points to a different level of inter operation requirements between such environments.
What is adaptive Learning?
The term “adaptive” is associated with a quite range of diverse system characteristics and capabilities in the e-Learning industry, thus making it is necessary to qualify the qualities one attributes to a system when using the term. In the context of this paper, a learning environment is considered adaptive if it is capable of: monitoring the activities of its users; interpreting these on the basis of domain-specific models; inferring user requirements and preferences out of the interpreted activities, appropriately representing these in associated models; and, finally, acting upon the available knowledge on its users and the subject matter at hand, to dynamically facilitate the learning process. The preceding informal definition should differentiate the concept of adaptivity from those of tailorability / configurability, flexibility / extensibility, or the mere support for intelligently mapping between available media / formats and the characteristics of access devices. Please note that in several places in this paper, the term “adaptation” is used as a synonym for “adaptivity”.
Adaptive behaviour on the part of a learning environment can have numerous manifestations. Instead of attempting to exhaustively enumerate all of these, we will provide a high-level categorization, which suffices for the analysis in the following section. The broad and partially overlapping categories that we will be referring to are: adaptive interaction, adaptive course delivery, content discovery and assembly, and, finally, adaptive collaboration support. Each of these categories is briefly qualified below, followed by an overview of the models and processes that are typically instated in adaptive e-Learning systems.
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