The motivation behind the proposed research work is the need for an innovative e-learning system that can adapt to the learning capability of every individual. Adaptive e-learning systems create new opportunities and at the same time have several research challenges that need to be addressed. The primary requirement of such adaptive systems is the need to create and represent adaptable content effectively. This paper presents a membrane computing model to demonstrate how adaptable content can be represented and used efficiently. The Spiking Neural P System (SNP) is a membrane computing model inspired by the way neurons communicate by means of spikes. This paper proposes the Distributed Spiking Neural P System (DSNP), a variant of the existing Distributed P System, that can be used to represent dynamic and distibuted systems. Temporal relations captured on a time line during authoring of the ecourse, can be automatically converted into an SNP system using the algorithm presented in the paper. An algorithm for the automatic generation of the DSNP from the e-course compositions represented using a linked list of SNPs is also presented in the paper along with experimental results to prove the efficiency and scalability of the proposed model.

S. V. Raghavan

Papers

An Adaptive e-Learning Environment Using Distributed Spiking Neural P Systems