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Integrating E-learning Environments with Computational Intelligence Assessment Agents

Year: 2008 Volume: 2 Issue: 7 774 - 779 Pages

• Authors:
• Christos E. Alexakos
• Konstantinos C. Giotopoulos
• Eleni J. Thermogianni
• Grigorios N. Beligiannis
• Spiridon D. Likothanassis

Abstract: In this contribution an innovative platform is being presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

• Keywords:
• Bayesian Networks
• Computational Intelligence techniques
• E-learning legacy systems
• Service Oriented Integration
• Intelligent Agents

Integrating Computational Intelligence Techniques and Assessment Agents in ELearning Environments

Year: 2007 Volume: 1 Issue: 12 3959 - 3968 Pages

• Authors:
• Konstantinos C. Giotopoulos
• Christos E. Alexakos
• Grigorios N. Beligiannis
• Spiridon D.Likothanassis

Abstract: In this contribution an innovative platform is being presented that integrates intelligent agents and evolutionary computation techniques in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting: I) various assessment agents for e-learning environments, II) a specific resource retrieval agent for the provision of additional information from Internet sources matching the needs and profile of the specific user and III) a genetic algorithm designed to extract efficient information (classifying rules) based on the students- answering input data. The agents are implemented in order to provide intelligent assessment services based on computational intelligence techniques such as Bayesian Networks and Genetic Algorithms. The proposed Genetic Algorithm (GA) is used in order to extract efficient information (classifying rules) based on the students- answering input data. The idea of using a GA in order to fulfil this difficult task came from the fact that GAs have been widely used in applications including classification of unknown data. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

• Keywords:
• Bayesian Networks
• Computational Intelligencetechniques
• E-learning legacy systems
• Service Oriented Integration
• Intelligent Agents
• Genetic Algorithms.