In college English teaching, educators often use multimedia information technology to carry out teaching activities and present the corresponding curriculum content in detail. Based on two semesters of analysis and investigation, this paper presents an empirical study on multimedia teaching in two courses of a non-English major in one Chinese college. The results show that multimedia teaching technology can effectively enhance college students' interest in English learning, stimulate their enthusiasm, enhance the interaction between teachers and students, and cultivate their autonomous learning ability. The research suggests that colleges and universities should strengthen the hardware support and software facilities of multimedia-assisted teaching to promote the significant improvement of students' language ability.
As an indispensable part of higher education in China, independent colleges bear the responsibility of talent training in the new era. Optimizing college English teaching environment in independent colleges is important for the healthy growth of college students. To explore new ideas of college English teaching in independent colleges, this paper applies the cognitive teaching model to the entire process of college English teaching, By deepening their cognition and comprehension with the flexibility of the cognitive teaching model, this enhances college students’ experience of English learning.
This study researches the current status, problems, and development countermeasures of Chinese-foreign dual degree programs in Chinese higher education by using Microsoft Excel and geographic system information (GIS) software, including their types of degree, geographical distribution, and collaborative partners. The result shows a reasonable reginal distribution of Chinese-foreign dual degree program in higher education is a strong guarantee to promote the coordinated development of higher education. How to establish a common quality guarantee system and make the cooperation certificate-oriented is also need to further research.
Fake news spreading rapidly worldwide is considered one of the most severe problems of modern technology that needs to be addressed immediately. The remarkable increase in the use of social media as a critical source of information combined with the shaking of trust in traditional media, the high speed of digital news dissemination, and the vast amount of information circulating on the Internet have exacerbated the problem of so-called fake news. The present work proves the importance of detecting fake news by taking advantage of the information derived from friendships between users. Specifically, using an innovative deep temporal convolutional network (DTCN) scheme assisted using the tensor factorization non-negative RESCAL method, we take advantage of class-aware rate tables during and not after the factorization process, producing more accurate representations to detect fake news with exceptionally high reliability. In this way, the need to develop automated methods for detecting false information is demonstrated with the primary aim of protecting readers from misinformation.
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