Multi-source physical information driven deep learning in intelligent education: Unleashing the potential of deep neural networks in complex educational evaluation

Abstract

With the urgent global demand for sustainable development, intelligent education driven by multi-source physical information has attracted widespread attention as an innovative educational model. However, in the context of dual carbon, achieving sustainable and efficient development of intelligent education faces many difficulties, and one of the important challenges is how to effectively evaluate students. The application of deep neural networks in educational evaluation is an important direction for the development of educational digitization. Currently, there is an urgent need to conduct research on the value of empowering educational evaluation with deep neural networks. We first studied the principles and characteristics of deep neural network technology in educational evaluation; second, three major advantages of deep neural networks in empowering educational evaluation were pointed out: objectivity in evaluating diversified educational data, accuracy in evaluating intelligent perception of educational information, and deep mining of educational data information for evaluation; finally, the key challenges faced by empowering deep neural networks in educational evaluation were clarified from the perspectives of educational environment, educational theoretical knowledge, and interpretability. This research provides new ideas and methods for the sustainable and efficient development of intelligent education driven by multi-source physical information and lays the foundation for breaking through the traditional methods of education evaluation in the new era of dual carbon sustainable development.