On assessment of brain function adaptability in Open Learning systems using Neural Networks modeling (cognitive styles approach)

The piece of research presents a conceptual overview on diverse cognitive styles reflections in adaptable Open Learning systems. The main goal of this approach is quantitative forecasting the performance of adaptable Open Learning (equivalently e-learning) Systems using cognitive Neural Network modelling. Furthermore, analysis of interactive two diverse learners' cognitive styles with a friendly adaptable teaching environment(e-courses material). Consequently, presented paper provides e-learning systems' designers with relevant guide for learning performance enhancement. Additionally, it supports e-learners in fulfilment of better learning achievements during face to face tutoring. Accordingly, quantitative analysis of e-learning adaptability performed herein, via assessment of matching between learning style preferences and the instructor's teaching style and/or e-courses material. Interestingly, application of two realistic cognitive models using Artificial Neural Network gives an opportunity to experience well assessment of adaptable e-learning features. Such as adaptability mismatching, adaptation time convergence, and individual differences of e-learners' adaptability.