Abstract

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.

Citation

Karampiperis, P. & Sampson, D. (2004). Adaptive Learning Object Selection in Intelligent Learning Systems. Journal of Interactive Learning Research, 15(4), 389-407. Norfolk, VA: Association for the Advancement of Computing in Education (AACE). Retrieved August 9, 2024 from https://www.learntechlib.org/primary/p/18898/.

© 2004 Association for the Advancement of Computing in Education (AACE)

Keywords

• artificial intelligence
• Instructional Design
• Learning Objects