Towards an automatic monitoring for higher education learning design

Abstract

The development of new Information Technologies (IT) has originated new possibilities to design pedagogical methodologies that provide the necessary knowledge and skills in the higher education. This paper presents a metadata-based model representation that is used to represent, detect, and even automatically correct possible pitfalls in the schedule process of a Learning Design (LD) in e-learning environments. This metadata-based model is combined with Artificial Intelligence techniques, such as, planning and scheduling to monitor how is evolving a particular LD, and to propose solutions in those modules of the design that learning problems among the students have been found. Copyright © 2007 Inderscience Enterprises Ltd.