LS-LAB: A Framework for Comparing Curriculum Sequencing Algorithms

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.

[1]  Vladimir Zadorozhny,et al.  Delegations guided by trust and autonomy , 2008, Web Intell. Agent Syst..

[2]  Carla Limongelli,et al.  An Application of the LS-Plan System to an Educational Hypermedia , 2009, Int. J. Web Based Learn. Teach. Technol..

[3]  S. Weibelzahl,et al.  Framework for the Evaluation of Adaptive CBR-Systems , 2001 .

[4]  Nicola Henze,et al.  Setting up a framework for comparing adaptive educational hypermedia: First steps and application on curriculum sequencing , 2002 .

[5]  Cristina Baroglio,et al.  A Service-Oriented Approach for Curriculum Planning and Validation , 2007 .

[6]  Peter Brusilovsky,et al.  Developing Adaptive Educational Hypermedia Systems: From Design Models to Authoring Tools , 2003 .

[7]  Cristina Baroglio,et al.  Reasoning about learning object metadata for adapting SCORM courseware , 2004 .

[8]  Julita Vassileva,et al.  Course sequencing techniques for large-scale web-based education , 2003 .

[9]  Carla Limongelli,et al.  Linear temporal logic as an executable semantics for planning languages , 2007, J. Log. Lang. Inf..

[10]  Natalia Stash,et al.  Creating and Delivering Adaptive Courses with AHA! , 2006, EC-TEL.

[11]  Wolfgang Nejdl,et al.  Adaptation in Open Corpus Hypermedia , 2001 .

[12]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[13]  GaetaMatteo,et al.  Adaptive course generation through learning styles representation , 2008 .

[14]  Pierre Wolper,et al.  The tableau method for temporal logic: an overview , 1985 .

[15]  Matteo Gaeta,et al.  Adaptive course generation through learning styles representation , 2008, Universal Access in the Information Society.

[16]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[17]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[18]  Carla Limongelli,et al.  LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education , 2008, AH.

[19]  Peter Brusilovsky,et al.  ELM-ART: An Adaptive Versatile System for Web-based Instruction , 2001 .

[20]  Julita Vassileva.  Dynamic CAL-Courseware Generation Within an ITS-Shell Architecture , 1992, ICCAL.