Automated and Flexible Comparison of Course Sequencing Algorithms in the LS-Lab Framework

Abstract

Curriculum Sequencing is one of the most interesting challenges in learning environments, such as Intelligent Tutoring Systems and e-learning. The goal is to automatically produce personalized sequences of didactic materials or activities, on the basis of each individual student’s model. 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 algorithms over the same student models. The main aim of LS-Lab is to provide researchers or teachers with a ready-to-use and possibly extensible environment, supporting a reasonably low-cost experimentation of several sequencing algorithms. The system accepts a student model as input, together with the selection of the algorithms to be used and a given learning material; then the algorithms are applied, the resulting courses are shown to the user, and some metrics computed over the selected characteristics are presented, for the user’s appraisal.