Systematic integration of information big data and physics teaching based on deep learning algorithms

Abstract

This paper first investigates the main performance of the shackles of traditional physics teaching on students and its negative consequences and then designs a physics teaching process model based on the teaching environment of information big data and constructs a framework of the physics teaching system by utilizing data mining technology. Then, the physics teaching system is modeled based on the artificial neuron model and recurrent neural network, and the attention mechanism is used to improve the overall generalization performance of the model. Finally, the application effect of the physics teaching system is compared through empirical evidence. The results show that there is a deviation in the mean value of the two classes; the average score of the experimental class is 75.552 points, and the average score of the control class is 65.910 points, and there is a difference of 9.642 points between the two classes’ mean values. The data show that applying a physics teaching system can improve the learning status quo of students’ mechanical imitation, help students better master knowledge, enhance students’ ability to use information technology and achieve better teaching results than traditional teaching.

Keywords

• information big data
• physics teaching
• deep learning
• attention mechanism
• teaching system
• 97d60