Fuzzy Neural Network Model for Intelligent Course Development in Music and Dance Education

Abstract

AbstractInteractions are mandatory for online or offline music and dance education to improve understandability and learning efficacy. The course designed for such artistic education incorporates multi-point interactions and monotonous presentations. The validation of the key factor: interactivity is thus mandatory for enhancing efficiency. This article introduces an interactivity validation method (IVM) using combined fuzzy neural network (FNN) to aid artistic course development. The output of the existing course and its evaluation criteria are considered in enhancing its grade. The fuzzy performs interaction classification as mandatory and trivial based on the student’s performance. The neural network identifies the chances for maximum performance by increasing or decreasing the interaction rate. If a saturated performance is achieved at a high or low interactivity, then the further course design is performed based on the saturated interactivity factor. The failing factors are used for training the neural network for modifying the interactivity rate from the current course development suggestion. Such a process is keen on classifying and validating the impact of interactivity over artistic course design.