Automatic Learning Object Selection and Sequencing in Web0Based Intelligent Learning Systems

Automatic Learning Object Selection and Sequencing in Web0Based Intelligent Learning Systems

Pythagoras Karampiperis (Piraeus University and Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece) and Demetrios Sampson (Piraeus University and Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece)
DOI: 10.4018/978-1-59140-729-4.ch003
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Abstract

Automatic courseware authoring is recognized as among the most interesting research questions in intelligent Web-based education. Automatic courseware authoring is the process of automatic learning object selection and sequencing. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection and sequencing are 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, in order to design 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 chapter, we address the learning object selection and sequencing problem in intelligent learning systems proposing a methodology that, instead of forcing an instructional designer to manually define the set of selection and sequencing 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 case.

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