Evaluating the effectiveness of online courses in international trade using deep learning

PeerJ Comput Sci. 2024 Nov 21:10:e2509. doi: 10.7717/peerj-cs.2509. eCollection 2024.

Abstract

The development of the world economy has prompted various countries to pay more attention to the teaching of online international trade courses based on deep learning. In the Internet age, online teaching has become an essential way for people to receive education. To guide the public in obtaining high-quality online teaching resources related to international trade, we propose an evaluation method for the implementation of international trade online courses based on deep learning. Firstly, by analyzing the characteristics of online education in international trade courses, we decompose the evaluation methods of online courses in international trade. Then, using deep learning technology, we propose a fusion method of multimodal evaluation features of online courses in international trade. Finally, we design a classification model to realize the effect evaluation of the course by inputting the fused features. Experiments show that our method can accurately evaluate the effect of international trade online courses, with an accuracy of 78.53%.

Keywords: Deep learning; Implementation effect evaluation; Online international trade course.

Grants and funding

The authors received no funding for this work.