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Barrot, Jessie S. – Computer Assisted Language Learning, 2023
Despite the building up of research on the adoption of automated writing evaluation (AWE) systems, the differential effects of automated written corrective feedback (AWCF) on errors with different severity levels and gains across writing tasks remain unclear. Thus, this study fills in the vacuum by examining how AWCF through Grammarly affects…
Descriptors: Automation, Written Language, Error Correction, Feedback (Response)
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Chao-Jung Ko – Computer Assisted Language Learning, 2024
This study aimed to examine the impact of an online writing system (OWS) providing individualized corrective feedback (CF) on learners' self-correction of grammatical errors (GE). It consisted of two phases: the pilot and the formal phases. Four EFL (English as a Foreign Language) Taiwanese university students participated in the study at the…
Descriptors: Feedback (Response), English (Second Language), Second Language Learning, Grammar
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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
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Vakili, Shokoufeh; Ebadi, Saman – Computer Assisted Language Learning, 2022
Theoretically grounded in Vygotsky's sociocultural theory of mind, Dynamic Assessment (DA) provides researchers with the opportunity to investigate different aspects of learners' developmental trajectory, including the ways they overcome their errors. As a qualitative inquiry into the nature of errors reflecting learners' development in academic…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Assisted Testing
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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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Byrne, Timothy – Computer Assisted Language Learning, 2007
Many teachers today use Learning Management Systems (LMS), several of which are open-source. Specific examples are Claroline and Moodle. However, they are not specifically designed for language learning, and hence not entirely suitable. In this article, I will compare two uses of the Claroline LMS available at Louvain-la-Neuve within the framework…
Descriptors: Management Systems, Computer Software, Computer Assisted Instruction, Second Language Instruction
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Ogata, Hiroaki; Yano, Yoneo; Wakita, Riko – Computer Assisted Language Learning, 1998
Describes an on-line mark-up-based composition learning environment system called CoCoA (Communicative Collection Assisting System). This system allows students and teachers to engage in marked-up documents via the Internet, and its environment is very similar to a real-world one in which people use pen and paper. CCML also facilitates teachers to…
Descriptors: Computer Assisted Instruction, Error Analysis (Language), Error Correction, Essays