Adaptive Learning and Thinking Style to Improve E-Learning Environment Using Neural Network (ALTENN) Model - Hanan Ettaher Dagez; Ali Elghali Ambarka (International Journal of Applied Methods in Electronics and Computers, 2015)
In recent years we have witnessed an increasingly heightened awareness of the potential benefits of adaptively in e-learning. This has been mainly driven by the realization that the ideal of individualized learning (i.e., learning tailored to the specific requirements and preferences of the individual) cannot be achieved, especially at a “massive” scale, using traditional approaches. In e-learning when the learning style of the student is not compatible with the teaching style of the teacher; difficulties in academic achievement can result. Therefore, knowing what is the preferred learning style supported by thinking style for individual can help in teaching and learning process. This paper presents an adaptive e-learning system (ALTENN) to improve e-learning environment. Neural network technology has been used for implementing the model and extracts the appropriate learning style based on learner thinking style. The system structure and NN results are also presented in this paper.