Optimized RB-RNN: Development of hybrid deep learning for analyzing student's behaviours in online-learning using brain waves and chatbots.

• To develop a hybrid deep learning model for student behavior analysis. • To develop an Opposition-based Food Foraging-Pelican Optimizer (O-FFPO) algorithm. • To carry out optimal feature selection by utilizing developed O-FFPO algorithm. • To develop an O-FFPO to enhance the classification performance of developed model. • To adopt a Radian Basis-Recurrent Neural Networks (RB-RNN) deep learning model. With the development of the internet, e-learning is rapidly growing in worldwide, which pave more attention to the students. Further, the learning materials like video lectures, shared assignments as well as lecture slides are performed during the online courses. Moreover, these activities among the students exhibit different behaviors. Hence, the analysis of student behavior becomes complicated in the existing models. This research focuses on developing a deep learning model for analyzing student behaviour in an accurate manner. This paper aims to develop a new student behavior prediction model for E-learning by evaluating brain signals and chatbot interaction. This suggested model collects data from both chatbots and the Brain waves of students. The collected textual data from the chatbot are forwarded to the feature extraction phase by word2vector and bag-of-n grams. Then, the optimal features are selected using an Opposition-based Food Foraging-Pelican Optimizer (O-FFPO). Similarly, the second set of Brain waves of students are forwarded to the phase feature extraction stage by Short-time Fourier transforms (STFT), and then, deep features are extracted using Convolutional Neural Network (CNN). These gathered data can also be forwarded to the optimal feature selection process using the same O-FFPO. Finally, these fused features are fed to Radian Basis-Recurrent Neural Networks (RB-RNN) to get student behaviors, where the constraints are optimized by the same O-FFPO. Various performance metrics are compared and validated with the existing approaches. Moreover, the developed model shows 95% and 93% concerning accuracy and F1-score. Finally, experimental evaluation was carried out with the estimation of both brain waves and chatbot queries of students. [ABSTRACT FROM AUTHOR]