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Harvey-Scholes, Calum – Computer Assisted Language Learning, 2018
Software can facilitate English as a Foreign Language (EFL) students' self-correction of their free-form writing by detecting errors; this article examines the proportion of errors which software can detect. A corpus of 13,644 words of written English was created, comprising 90 compositions written by Spanish-speaking students at levels A2-B2…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Error Correction
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Hsu, Liwei – Computer Assisted Language Learning, 2016
This study aims to explore the structural relationships among the variables of EFL (English as a foreign language) learners' perceptual learning styles and Technology Acceptance Model (TAM). Three hundred and forty-one (n = 341) EFL learners were invited to join a self-regulated English pronunciation training program through automatic speech…
Descriptors: Pronunciation, Pronunciation Instruction, Cognitive Style, Statistical Analysis
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Rachels, Jason R.; Rockinson-Szapkiw, Amanda J. – Computer Assisted Language Learning, 2018
A quasi-experimental, pretest-posttest, non-equivalent control group design was used to examine the effect of a mobile gamification application on third and fourth grade students' Spanish language achievement and student academic self-efficacy. In this study, the treatment group's Spanish language instruction was through the use of…
Descriptors: Second Language Learning, Second Language Instruction, Spanish, Telecommunications
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Yeh, Hui-Chin – Computer Assisted Language Learning, 2015
Few studies have investigated how metacognitive processes foster the application of genre knowledge to students' academic writing. This is largely due to its internal and unobservable characteristics. To bridge this gap, an online writing system based on metacognition, involving the stages of planning, monitoring, evaluating, and revising, was…
Descriptors: Metacognition, Statistical Analysis, Academic Discourse, Sampling
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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
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Bower, Jack Victor; Rutson-Griffiths, Arthur – Computer Assisted Language Learning, 2016
A strong relationship between L2 vocabulary knowledge and L2 reading and listening comprehension is well established. However, less research has been conducted to explore correlations between pedagogic interventions to increase vocabulary knowledge and score gains on standardized L2 proficiency tests. This study addresses this gap in the research…
Descriptors: Correlation, Computer Software, Scores, Language Tests
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McEnery, Tony; And Others – Computer Assisted Language Learning, 1995
Compares two approaches to teaching grammar with respect to accuracy of participant response over time. A traditional based program used the human teacher method, while Cyber Tutor, a computer-aided program, allowed students to annotate sentences while providing instant feedback and help facilities. (six references) (Author/CK)
Descriptors: College Students, Comparative Analysis, Computer Assisted Instruction, Computer Software