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. |
