The Online Teacher-Student Interaction Level in the Context of a Scenario-Based Multi-Dimensional Interaction Teaching Environment

Abstract

For teachers of online courses, figuring out the features of teaching content, setting proper teaching scenarios, and mobilizing students’ learning enthusiasm via multi-dimensional interaction are necessary works. This paper analyzed the online teacher-student interaction level in the context of a Scenario-based Multi-Dimensional Interaction (SMDI) teaching environment. At first, this paper divided the evaluation indexes of the said interaction level into four aspects. Then, this paper built a teacher-student interaction behavior preference feature model based on the Graph Convolution Neural Network (GCNN) and a teacher-student interaction relationship model based on multi-task learning, thereby realizing the accurate description of the preference features of teacher-student interaction behavior. After that, this paper elaborated on the method for accurately constructing the teacher-student interaction relationship. At last, the effectiveness of the models was verified by the experimental results.