A Deep Learning-Based Grouped Teaching Strategy for Experimental Training

Abstract

The diagnosis of students’ pre-class hands-on operation capacity helps to group the students scientifically to facilitate the teaching for experimental training of skill training courses, and benefits the capacity complementation and team spirit cultivation. Practically speaking, the relevant results have rarely been implemented in the practical teaching of skill training courses. No literature has analyzed the correlation between grouped teaching for experimental training, and the key competences of students for skill training courses. To solve the problem, this paper constructs a grouped teaching strategy for experimental training based on deep learning. The key competences of students for skill training courses were evaluated from four aspects, namely, general training objective, thinking training, practical training, and scientific literacy training. The evaluation results were used to diagnose the students’ pre-class hands-on operation capacity, and reasonably group the students participating in experimental training. The deep learning model was called to group students of different majors for experimental training courses. The attention mechanism was added to prevent over fitting, when there are a few samples for capacity diagnosis. The proposed model was proved effective through experiments.