Comparing Curriculum Sequencing Algorithms for Intelligent Adaptive (e)-Learning

In the context of Web-Based e-Learning, the pedagogical strategy behind a course is crucial, as well as the capability of a system to automatically tailor the course to the needs and interests of each individual student. In fact Personalization and Adaptation are more and more and more sought in educational systems. In this paper we present the extension of the LS-Lab framework, supporting an automated and flexible comparison of the outputs coming from a variety of Curriculum Sequencing algorithm, applied to common student models. Our framework compares the algorithms’ outcomes, obtained from common conditions (student model and aims, repository of learning objects, characteristics of the produced learning paths to be monitored) by presenting the produced sequences and their metrics values.