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Loncar, Michael; Schams, Wayne; Liang, Jong-Shing – Computer Assisted Language Learning, 2023
The following review incorporates a systematic selection, coding, and analysis methodology in order to compile a corpus of empirical research studies that investigate the use of technology-mediated feedback in L2 writing contexts published from 2015-2019. Trends are identified by coding and quantitatively analyzing key parameters of the corpus,…
Descriptors: Research Reports, Writing Instruction, Feedback (Response), Technology Uses in Education
Tsai, Shu-Chiao – Computer Assisted Language Learning, 2022
This study investigates the effectiveness of using Google Translate as a translingual CALL tool in English as a Foreign Language (EFL) writing, keyed to the perceptions of both more highly proficient Chinese English major university students and less-proficient non-English majors. After watching a 5-minute passage from a movie, each cohort of…
Descriptors: Computer Assisted Instruction, Translation, Second Language Learning, Second Language Instruction
Chung, Eun Seon; Ahn, Soojin – Computer Assisted Language Learning, 2022
Many studies that have investigated the educational value of online machine translation (MT) in second language (L2) writing generally report significant improvements after MT use, but no study as of yet has comprehensively analyzed the effectiveness of MT use in terms of various measures in syntactic complexity, accuracy, lexical complexity, and…
Descriptors: Translation, Computational Linguistics, English (Second Language), Second Language Learning
Guo, Qian; Feng, Ruiling; Hua, Yuanfang – Computer Assisted Language Learning, 2022
AWCF can facilitate academic writing development, especially for novice writers of English as a foreign language (EFL). Existing AWCF studies mainly focus on teacher and learner perceptions; fewer have investigated the error-correction effect of AWCF and factors related to the effect. Especially lacking is research on how successfully students can…
Descriptors: Error Correction, Feedback (Response), English (Second Language), Second Language Learning
Goh, Tiong-Thye; Sun, Hui; Yang, Bing – Computer Assisted Language Learning, 2020
This study investigates the extent to which microfeatures -- such as basic text features, readability, cohesion, and lexical diversity based on specific word lists -- affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268…
Descriptors: Essays, Writing Tests, English (Second Language), Second Language Learning
Tsai, Shu-Chiao – Computer Assisted Language Learning, 2019
This study investigates the impact on extemporaneous English-language first drafts by using Google Translate (GT) in three different tasks assigned to Chinese sophomore, junior, and senior students of English as a Foreign Language (EFL) majoring in English. Students wrote first in Chinese (Step 1), then drafted corresponding texts in English (Step…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Software
Ranalli, Jim – Computer Assisted Language Learning, 2018
Automated written corrective feedback (AWCF) has qualities that distinguish it from teacher-provided WCF and potentially undermine claims about its value for L2 student writers, including disparities in the amounts of useful information it provides across error types and the fact that inaccuracies in error-flagging must be anticipated. It remains…
Descriptors: Error Correction, Feedback (Response), Computer Assisted Instruction, Second Language Learning
Chen, M.-H.; Huang, S.-T.; Chang, J. S.; Liou, H.-C. – Computer Assisted Language Learning, 2015
Paraphrasing, or restating information using different words, is critical to successful writing. However, EFL learners have difficulty in making paraphrases to meet their writing demands, and there has been little research on developing automatic reference tools to assist these learners' paraphrasing skills for better writing quality. In this…
Descriptors: English (Second Language), Second Language Learning, Computational Linguistics, Dictionaries
Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel – Computer Assisted Language Learning, 2016
Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of…
Descriptors: Discourse Analysis, Feedback (Response), Undergraduate Students, Accuracy