Adaptive Course Sequencing for Personalization of Learning Path Using Neural Network

Advancements in technology have led to a paradigm shift from traditional to personalized learning methods with varied implementation strategies. Presenting an optimal personalized learning path in an educational hypermedia system is one of the strategies that is important in order to increase the effectiveness of a learning session for each student. However, this task requires much effort and cost particularly in defining rules for the adaptation of learning materials. This research focuses on the adaptive course sequencing method that uses soft computing techniques as an alternative to a rule-based adaptation for an adaptive learning system. The ability of soft computing technique in handling uncertainty and incompleteness of a problem is exploited in the study. In this paper we present recent work concerning concept-based classification of learning object using artificial neural network (ANN). Self Organizing Map (SOM) and Back Propagation (BP) algorithm were employed to discover the connection between the domain concepts contained in the learning object and the learner’s learning need. The experiment result shows that this approach is assuring in determining a suitable learning object for a particular student in an adaptive and dynamic learning environment.