Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Abstract

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.