The Online Teacher-Student Interaction Level in the Context of a Scenario-Based Multi-Dimensional Interaction Teaching Environment
DOI:
https://doi.org/10.3991/ijet.v17i12.32083Keywords:
scenario-based multi-dimensional interaction (SMDI), interaction level, graph convolution neural network (GCNN), teacher-student interaction relationship, interaction behavior, preference featureAbstract
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.
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Copyright (c) 2022 Nan Zhang (Submitter); Yinchun Chen
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