Adaptive Learning Object Selection in Intelligent Learning Systems
Purchase or Subscription required for access
Purchase individual articles and papers
Subscribe for faster access!
Subscribe and receive access to 100,000+ documents, for only $19/month (or $150/year).
Already have access?
Individual Subscription
If you have an individual subscription, sign in here for access
Institutional Subscription
You don't appear to be accessing the site through a subscribing institution (your IP address is 18.217.112.20).
If your university, college, or library subscribes to LearnTechLib, you may be able access full text articles through a login page.
You can search for your instition by name or by location.
Authors
![](https://editlib-media.s3.amazonaws.com/sources/sources/JILR.jpg)
JILR Volume 15, Number 4, October 2004 ISSN 1093-023X
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/p/18898.
© 2004 AACE