Analysis of interactive impact and student behavior of teaching online courses based on gamified learning

Abstract

The potential of gamified learning as an emerging force for educational change has been widely recognized, to address the design deficiencies in the automatic recognition methods in traditional gamified learning models and the low accuracy of learning behavior recognition. Based on the improved gamified learning model, this paper jointly proposes an optimal design and recognition model of learning mode based on a deformable convolutional type network. Based on the gamified machine learning theory and interactive feedback theory, it comprehensively analyzes the procedural improvement of the gamified interactive intelligence model to provide a theoretical and practical basis for carrying out the analysis of the interactive impact and student behavior of teaching online courses based on gamified learning. The research results show that: (1) with the increase of the number of gamified learning samples, the value of iterative loss function decreases, the accuracy of recognition improves, and gradually converges and tends to be stable when the number of training times is 500; the 800 training samples setting can fully meet the network training requirements. (2) The deformable convolutional neural learning network constructed with the proposed joint algorithm reduces the network error during the training process and introduces the radical change function to improve the data processing capability. This improved gamified learning model is not only superior in recognition accuracy but also shows great advantages in recognition time.