Towards A Collaborative Model Of An Automated Adaptive Content Delivery Training Utilizing Fuzzy Logic

Rapid advances in internet technologies have opened gateways new types of collaborative systems and for specialized educational opportunities. These range from online distance learning for individuals as well as for those who want to learn a certain skill set, courses for which may not be offered in traditional educational institutions. Although these web-based courses have become major global education contributors in overcoming the basic education challenges of the traditional classroom environment, they do not consider the fact that each individual can learn differently. Some students are auditory learners, and some may be visual learners while others may be kinesthetic learners. Adaptive learning systems address this aspect of different learning styles and the need to customize learning instruction. However individual learners progress partly as a function of the content being developed by an instructor. Therefore there is a need for a collaborative system that can let an instructor know how learners are progressing and thus the instructor can modify and disperse near real-time course content if learners are struggling with current content. Such systems provide an innovative method of instruction that adapts to the learner’s unique learning style. A model of a novel approach to collaborative adaptive learning is presented that utilizes learning modalities of instruction tailored to individual needs. At the heart of this approach is a fuzzy neural network (FNN) that evaluates comprehension makes instructional modality selections and collaborates with an instructor in tailoring course content.