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Article Excerpt
Adaptive learning object selection and sequencing is recognized as among the most interesting research questions in intelligent web-based education. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection is based on a set of teaching rules according to the cognitive style or learning preferences of the learners. In spite of the fact that most of these rules are generic (i.e., domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make "instructional sense." Moreover, to design highly adaptive learning systems a huge set of rules is required, since dependencies between educational characteristics of learning objects and learners are rather complex. In this article, we address the learning object selection problem in intelligent learning systems proposing a methodology that instead of forcing an instructional designer to manually define the set of selection rules, it produces a decision model that mimics the way the designer decides, based on the observation of the designer's reaction over a small-scale learning object selection case.
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The high rate of evolution of e-learning platforms implies that on the one hand, increasingly complex and dynamic web-based learning infrastructures need to be managed more efficiently, and on the other hand, new types of learning services and mechanisms need to be developed and provided. To meet the current needs, such services should satisfy a diverse range of requirements, for example, personalization and adaptation (Dolog, Henze, Nejdl, & Sintek, 2004; Vasilakos, Devedzic, Kinshuk, & Pedrycz, 2004). The field of computational intelligence in web-based education can contribute towards providing web-based technologies, methods, and techniques for supporting teaching and learning in an intelligent way.
Learning object selection is the first step to adaptive navigation and adaptive course sequencing. Adaptive navigation seeks to present the learning objects associated with an online course in an optimized order, where the optimization criteria takes into consideration the learner's background and performance on related learning objects (Brusilovsky, 1999), whereas adaptive course sequencing is defined as the process that selects learning objects from a digital repository and sequences them in a way, which is appropriate for the targeted learning community or individuals (Knolmayer, 2003). Selection and sequencing is recognized as among the most interesting research questions in intelligent web-based education (McCalla, 2000; Dolog & Nejdl, 2003; Devedzic, 2003).
Although many types of intelligent learning systems are available, we can identify five key components which are common in most systems, namely, the student model, the expert model, the pedagogical module, the domain knowledge module, and the communication model. Figure 1 provides a view of the interactions between these modules.
In most intelligent learning systems that incorporate course sequencing techniques, the pedagogical module is responsible for setting the principles of content selection and instructional planning. The selection of content (in our case, learning objects) is based on a set of teaching rules according to the cognitive style or learning preferences of the learners (Brusilovsky & Vassileva, 2003; Stash & De Bra, 2004). In spite of the fact that most of these rules are generic (i.e., domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make "instructional sense" (Knolmayer, 2003; Mohan, Greer, & McGalla, 2003). Moreover, to design highly adaptive learning systems a huge set of rules is required, since dependencies between educational characteristics of learning objects and learners' characteristics are rather complex.
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In this article, we address the learning object selection problem in intelligent learning systems proposing a methodology that instead of forcing an instructional designer to manually define the set of selection rules; produces a decision model that mimics the way the designer decides, based on the observation of the designer's reaction over a small-scale learning object selection problem.
In the next section we discuss the learning object selection process as part of automatic course sequencing. The third section discusses the filtering process of learning objects used for reduction of learning objects searching space and proposes metadata elements that can be used for learning object filtering. The fourth section presents a methodology for capturing expert's decision model on learning objects selection and it constitutes the main contribution of this article. Finally, we present experimental results of the proposed methodology by comparing the resulting learning objects selected by the proposed method with those selected by experts.
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