The Purpose and Content of Freshman English Composition

1957 ◽  
Vol 8 (2) ◽  
pp. 109
Author(s):  
Robert O. Bowen
Keyword(s):  
English Composition ◽  
Freshman English ◽  
Freshman English Composition

scholarly journals You Can’t Get There from Here: Student Citations in an Ephemeral Electronic Environment

2007 ◽  
Vol 68 (4) ◽  
pp. 312-322 ◽  
Author(s):  
Karen Hovde
Keyword(s):  
Undergraduate Student ◽  
English Composition ◽  
Freshman English ◽  
Electronic Environment ◽  
Internet Resources ◽  
Traditional Print ◽  
Freshman English Composition

This study investigates 1,666 citations to Internet resources from 529 freshman English composition papers from two different years, 1999 and 2004. Web citations are examined in relation to traditional print resources, by type and domain, and with special attention to their persistence through time. After 7 months from submission of the papers for the 1999 sample, 38 percent of the links led to the document cited. After 12 months for the 2004 sample, 45 percent of the links accessed the document. A resubmission of the 1999 citations after 7 years reduced the number of successful links to 9 percent. The instability of Web citations in undergraduate student papers is discussed in the larger context of citation structure, with implications for future scholarship.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Sign in / Sign up

Export Citation Format

Share Document