Negative Transfer of L1 Thinking on L2 Writing Based on Iwrite Evaluation System

L1 is, generally, believed to affect the acquisition of second language negatively. Because there is likely to be a negative transfer form L1 to L2, when the learner lacks sufficient knowledge for communicating his or her ideas in L2 and then draw upon the L1. The transfer includes both positive and negative transfer. However, the role of L1 is more often viewed as negative, causing negative transfer that results in a variety of errors. Generally, English and Chinese do not have many of the shared syntactical features. Hence the syntactic transfer is predictable when using the L2. This may lead to such errors as those in the noun phrase, in the verb phrase and various omissions. This paper analyses those errors in English writing, which represent the negative syntactic transfer from Chinese to English. Moreover, it discusses in details the particular causes for that transfer and propose improving the awareness of the syntactic distinction between these two languages in Chinese students.

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Kurdish EFL learners’ conceptual transfer in L2 writing

Journal of Education Culture and Society ◽

10.15503/jecs20181.163.172 ◽

2018 ◽

Vol 9 (1) ◽

pp. 163-172

Author(s):

Jamal Ali Omar

Keyword(s):

Language Use ◽

Negative Transfer ◽

Negative Influence ◽

L2 Writing ◽

Structural Level ◽

Efl Learners ◽

English Major ◽

University Level ◽

Conceptual Transfer ◽

Relationship Of

Abstract The phenomenon of language transfer in SLA learning and use is perennial and cannot be silenced easily. In L2 writing, the phenomenon is found to affect the written products sound nonnative and, even ambiguous. It is thought that the transfer occurs at the conceptual and structural level of language use. The present paper examines Kurdish EFL learners’ writing aiming at identifying transfer types, particularly, the negative transfer. To this end, 20 university level English major students argumentative writing are analyzed focusing on the conjuncts and adjuncts to find out any track of L1 concepts. The logical clause relationship of cause-effect was the area of focus. The results of the study showed that L1 concepts have been used in forming the relations between sentences and clauses spelt out by lexical signals of sentence connectors and subordinators. It is also found that L1 concepts transferred into L2 writing. The insights gained from the results of the study reveal that there is a problem, especially the negative influence of L1, which needs to be attended.

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A proposed method for assessing the quality of sealants - the CCC Sealant Evaluation System

Community Dentistry And Oral Epidemiology ◽

10.1034/j.1600-0528.2001.290202.x ◽

2001 ◽

Vol 29 (2) ◽

pp. 83-91 ◽

Cited By ~ 9

Author(s):

Christopher Deery ◽

Hazel E. Fyffe ◽

Zoann J. Nugent ◽

Nigel M. Nuttall ◽

Nigel B. Pitts

Keyword(s):

Evaluation System

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Field evaluation system for Air Force technical training: Analysis and modification.

PsycEXTRA Dataset ◽

10.1037/e447422004-001 ◽

1979 ◽

Author(s):

Clifford P. Hahn

Keyword(s):

Air Force ◽

Evaluation System ◽

Field Evaluation ◽

Technical Training ◽

Training Analysis

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R I Crash Outcome Data Evaluation System (CODES)

PsycEXTRA Dataset ◽

10.1037/e616732007-002 ◽

2005 ◽

Author(s):

Ted Donnelly

Keyword(s):

Evaluation System ◽

Outcome Data ◽

Data Evaluation

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

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.

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