An Intelligent Service-Based Layered Architecture for eLearning and eAssessment

Abstract

The rapid advancement in ICT (Information & Communication Technology) is causing a paradigm shift in eLearning domain. Traditional eLearning systems suffer from certain shortcomings like tight coupling of system components, lack of personalization, flexibility, and scalability and performance issues. This study aims at addressing these challenges through an MAS (Multi Agent System) based multi-layer architecture supported by web services. The foremost objective of this study is to enhance learning process efficiency by provision of flexibility features for learning and assessment processes. Proposed architecture consists of two sub-system namely eLearning and eAssesssment. This architecture comprises of five distinct layers for each sub-system, with active agents responsible for miscellaneous tasks including content handling, updating, resource optimization, load handling and provision of customized environments for learners and instructors. Our proposed architecture aims at establishment of a facilitation level to learners as well as instructors for convenient acquisition and dissemination of knowledge. Personalization features like customized environments, personalized content retrieval and recommendations, adaptive assessment and reduced response time, are believed to significantly enhance learning and tutoring experience. In essence characteristics like intelligence, personalization, interactivity, usability, laidback accessibility and security, signify aptness of proposed architecture for improving conventional learning and assessment processes. Finally we have evaluated our proposed architecture by means of analytical comparison and survey considering certain quality attributes.Key Words: E-Learning, E-Assessment, Architecture, Data Mining, Intelligent Agent. Section 2 provides related work in areas of MAS, SOA, personalized leaning and query processing. Section 3 provides general architecture with detailed description of the proposed system and underlying layers. Section 4 provides evaluations mechanism followed by conclusion in section 5. RELATED WORDMAS is a sub-discipline of DAI (Distributed Artificial Intelligence

Computer Simulation Based Optimization of Aspect Ratio for Micro and Nanochannels

“…Previously and presently, all the researchers working on CFD have done the ANSYS simulations on individual element [24][25]. The usage of a computer to characterize the fluid dynamics in micro/nanochannels and in other fields is wide spread [26][27][28][29].…”