Deep learning combined with the teaching practice path of computer software application courses under the perspective of “five-education integration”

Abstract

Abstract As a core course of educational technology, the teaching of application software in colleges and universities faces the problem of insufficient practical ability of learners. This paper constructs a suitable path of computer software application teaching with reinforcement learning by combining reinforcement learning algorithm with students’ cognitive diagnosis theory. Students’ practical level is diagnosed through students’ actual operation results combined with the factors of guessing and mistakes, and appropriate teaching practice paths are recommended based on students’ practical ability combined with Markov decision-making to model the environment of computer software application practice. The TOVE method was used to evaluate the computer application practice course, and the practice paths with individualization were reasoned based on the connection between students’ knowledge points. The results show that among the experimental results of different practice paths, the effectiveness of the deep learning practice path of S1 is the highest at 85%. Compared with the expert-recommended practice path, the deviation value of the path effectiveness of S5 is 1, which indicates that the deep learning practice path is similar to the expert path likewise with a certain degree of specialization. On the Analysis of classroom project practice, the deviation value of the control class is 0.5512, and the deviation value of the experimental class is 4.2662, which is significantly larger than that of the control class, indicating that there is a significant improvement in students’ practical ability under the deep learning teaching practice path.