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

Abstract

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.

Using Project-based Learning in a Hybrid e-Learning System Model

“…Identification of LS of four students throughout time. Source: [57] This phenomenon is relatively normal, given the proximity among some LS and the mixed preferences of some students. In this sense, the most feasible option would be to identify the LS during some introductory course or previous activities, before starting with the tasks of adapting and personalising the contents of the e-Learning platform, and then validating and iteratively refining the identification carried out in the following activities or courses.…”

“…Results of example query for project assignment. Source: [57] Table V shows the summary of the tests conducted and the best similitude value obtained for each one of them, utilizing the comparison by linear similitude, in which it can be appreciated that the results are highly acceptable.…”

“…This work proposes a project assignment module, which based on the results of the identification of learning styles, carried out by the Fuzzy Neuro subsystem detailed in [57], assigns projects in a personalized way, considering the learning objects that best contribute to the objectives proposed within the framework of the Adaptive e-Learning system strategies.…”

“…Neural Network architecture. Source: [57] are generic and do not require sophisticated knowledge of the users preferences, to advanced levels, which with the passage of time, as content personalization is refined, permit the attainment of the required knowledge for a more precise identification of LS.…”

“…As mentioned in [57]: "In the neural network, the hidden layer increases the processing capacity, and the number of neurons in the hidden layer directly affects the capacity of the 'neural network' for learning". In the proposed case, during the initial experimentation phase, before pre-processing the inputs, tests were conducted with distinct numbers of neurons in the hidden layer.…”