Incorporating Quality Talk into the EFL College English Curriculum: Listening to Students’ Voices

Author(s):  
Mei-Lan Lo ◽  
Kason Chien
1964 ◽  
Vol 26 (1) ◽  
pp. 1
Author(s):  
Frederick L. Gwynn

2011 ◽  
Vol 217-218 ◽  
pp. 1839-1843
Author(s):  
Wen Hui Wang ◽  
Xin Sheng Zou

College English Curriculum (2004) (CE Curriculum hereafter) is issued by China’s Ministry of Education. As a top-down document, it acts as a guide for colleges and universities nation-wide to formulate a school-based curriculum in the light of their specific circumstances. Compared with the previous counterparts, it is a more balanced and democratic national curriculum. Although the present curriculum is for trial implementation, the course rationale is sound and the curriculum is of greater flexibility.


2020 ◽  
Vol 39 (4) ◽  
pp. 5559-5569
Author(s):  
Meichen Jin

At present, the field of natural language will also introduce in-depth learning, using the concept of word vector, so that the neural network can also complete the work in the field of statistics. It can be said that the neural network has begun to show its advantages in the field of natural language processing. In this paper, the author analyzes the multimedia English course based on fuzzy statistics and neural network clustering. Different factors were classified, and scores were classified according to the number of characteristics of different categories. It can be seen that with the popularization of the Internet, MOOC teaching meets the requirements of the current college English curriculum, is a breakthrough in the traditional teaching mode, improves students’ participation, and enables students to learn independently. It not only conforms to the characteristics of College students, but also improves their learning effect. In the automatic scoring stage, the quantitative text features are extracted by the feature extractor in the pre-processing stage, and then the weights of network connections obtained in the training stage are used to score the weights comprehensively. This model can better reflect students’ autonomous learning ability and language application ability.


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