Nowadays, we are witnesses of a steadily increasing research interest in design, modeling, construction and practical experiments with adaptable e-learning platforms and tools. Adaptive Web based multimedia information authoring, control and delivery tends to be more and more promising as its offer unlimited level of personalization and satisfaction of goals and preferences of particular learne...

Accurate streamflow (Qt) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering scientific fields, their application has increased ...

E-learning usability is a goal of the user centred elearning design. Its achievement depends on the determination of the requirements, characteristics and preferences of the users of an e-learning system or environment (learner, teacher and administrator of an educational process). This paper presents different aspects of e-learning usability. One of them requires an evaluation of learner’s pre...

This paper presents an approach of achieving adaptive e-learning by probabilistically evaluating a learner based not only on the profile and performance data of the learner but also on the data of previous learners. In this approach, an adaptation rule specification language and a user interface tool are provided to a content author or instructor to define adaptation rules. The defined rules ar...

In recent years, Bayesian models have become increasingly popular as a way of understanding human cognition. Ideal learner Bayesian models assume that cognition can be usefully understood as optimal behavior under uncertainty, a hypothesis that has been supported by a number of modeling studies across various domains (e.g., Griffiths & Tenenbaum, 2005; Xu & Tenenbaum, 2007). The models in these...

Constructive approach to learning focuses on the learner’s behavior, enabling a self-adapted exposure to knowledge that is precisely tailored to the learner’s needs and background. The building blocks for such adaptive knowledge construction are the evolving learning objects (LOs) which are self-contained entities that encapsulate a segment of knowledge as well as some metadata attributes and p...

In this paper we aim to show that instance-based classification can replace the classifier component of a rule learner and of maximum-entropy modeling, thereby improving the generalization accuracy of both algorithms. We describe hybrid algorithms that combine rule learning models and maximum-entropy modeling with instance-based classification. Experimental results show that both hybrids are ab...

This paper represents a new approach to manage learner modeling in an adaptive learning system framework. It considers developing the basic components of an adaptive learning system such as the learner model, the course content model and the adaptation engine. We use the overlay model and Bayesian network to evaluate learners’ knowledge. In addition, we also propose a new content modeling metho...