Role Based Multi-Agent System for E-Learning (MASeL)

Hameed, Mustafa Emad; Akhtar, Nadeem; Missen, Malik Saad

doi:10.14569/ijacsa.2016.070327

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…On the other hand, the possibility of integrating diverse types of actors with well-defined roles and their capacity to handle heterogeneous resources has been addressed principally through approaches based on multi-agent systems, where variations have been observed, such as the execution of interactions through the use of the XML standard [22], delivery of contents and distribution of roles in a dynamic and adaptive way [23]. According to Azambuja and Vicari [24], the application of multi-agent architectures allows for improvement in the interactivity of e-learning platforms, such as described in their proposal based on JADE architecture.…”

Section: F Related Workmentioning

confidence: 99%

Utilization of a Neuro Fuzzy Model for the Online Detection of Learning Styles in Adaptive e-Learning Systems

Alfaro¹,

Rivera²,

Luna-Urquizo

et al. 2018

ijacsa

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After conducting a historical review and establishing the state of the art of the various approaches regarding the design and implementation of adaptive e-learning systems-taking into consideration the characteristics of the user, in particular their learning styles and preferences in order to focus on the possibilities for personalizing the ways of utilizing learning materials and objects in a manner distinct from what e-learning systems have traditionally been, which is to say designed for the generic user, irrespective of individual knowledge and learning styles-the authors propose a system model for the classification of user interactions within an adaptive e-learning platform, and its analysis through a mechanism based on backpropagation neural networks and fuzzy logic, which allow for automatic, online identification of the learning styles of the users in a manner which is transparent for them and which can also be of great utility as a component of the architecture of adaptive e-learning systems and knowledge-management systems. Finally, conclusions and recommendations for future work are established.

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Section: F Related Workmentioning

confidence: 99%