Curriculum sequencing is one of the most appealing challenges in Web-based learning environments: the success of a course mainly depends on the system capability to automatically adapt the learning material to the student's educational needs. Here we address the problem of how to compare and to test different curriculum sequencing algorithms in order to reason about them in a self-contained and homogeneous environment. We propose LS-LAB, a framework especially designed for comparing and testing different curriculum sequencing algorithms. LS-LAB has been designed to run different algorithms, each of them provided with its own student model representation: a super student model is able to incrementally include all of them. In this framework, the learning node has to be compliant to the IEEE LOM specifications, while, through a suitable GUI, one can insert new algorithms or run already available ones. We are carrying out the implementation by using a 3-tier Java application technology, in order to make this environment available on the Internet. Finally we show an application example.
Limongelli, C., F., S., M., T., G., V. (2009). LS-Lab: a Framework for Comparing Curriculum Sequencing Algorithms. In Proceedings of The International Conference on Intelligent Systems Design and Applications ISDA 2009 [10.1109/ISDA.2009.146].
LS-Lab: a Framework for Comparing Curriculum Sequencing Algorithms
LIMONGELLI, Carla;
2009-01-01
Abstract
Curriculum sequencing is one of the most appealing challenges in Web-based learning environments: the success of a course mainly depends on the system capability to automatically adapt the learning material to the student's educational needs. Here we address the problem of how to compare and to test different curriculum sequencing algorithms in order to reason about them in a self-contained and homogeneous environment. We propose LS-LAB, a framework especially designed for comparing and testing different curriculum sequencing algorithms. LS-LAB has been designed to run different algorithms, each of them provided with its own student model representation: a super student model is able to incrementally include all of them. In this framework, the learning node has to be compliant to the IEEE LOM specifications, while, through a suitable GUI, one can insert new algorithms or run already available ones. We are carrying out the implementation by using a 3-tier Java application technology, in order to make this environment available on the Internet. Finally we show an application example.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
