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Schneider, Johannes; Richner, Robin; Riser, Micha – International Journal of Artificial Intelligence in Education, 2023
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them…
Descriptors: Grading, Natural Language Processing, Computer Assisted Testing, Ethics
Hanne Roothooft; Amparo Lázaro-Ibarrola; Bram Bulté – Language Teaching Research, 2025
Second language (L2) writing research has demonstrated that young learners discuss linguistic issues, make use of feedback, and show a generally positive disposition toward writing tasks. However, many issues deserve further investigation. Regarding task implementation, few studies have been conducted with young learners writing individually, and…
Descriptors: Error Correction, Feedback (Response), Accuracy, Writing Instruction
Wen Liu – Language Teaching Research Quarterly, 2024
Automated writing evaluation feedback (AWE) has become popular in writing classrooms. However, few studies have conducted a comprehensive review of the employment of AWE in learning areas. This study aimed to provide a systematic review of the current research on AWE feedback, including its validity, effects, and students' engagement with AWE…
Descriptors: Writing Instruction, Learner Engagement, Feedback (Response), Teaching Methods
Zhang, Hong; Torres-Hostench, Olga – Language Learning & Technology, 2022
The main purpose of this study is to evaluate the effectiveness of Machine Translation Post-Editing (MTPE) training for FL students. Our hypothesis was that with specific MTPE training, students will able to detect and correct machine translation mistakes in their FL. Training materials were developed to detect six typical mistakes from Machine…
Descriptors: Computational Linguistics, Translation, Second Language Learning, Second Language Instruction
Xu, Wenwen; Kim, Ji-Hyun – English Teaching, 2023
This study explored the role of written languaging (WL) in response to automated written corrective feedback (AWCF) in L2 accuracy improvement in English classrooms at a university in China. A total of 254 freshmen enrolled in intermediate composition classes participated, and they wrote 4 essays and received AWCF. A half of them engaged in WL…
Descriptors: Grammar, Accuracy, Writing Instruction, Writing Evaluation
Gaillat, Thomas; Lafontaine, Antoine; Knefati, Anas – CALICO Journal, 2023
In this article, we focus on the design of a second language (L2) formative feedback system that provides linguistic complexity graph reports on the writings of English for special purposes students at the university level. The system is evaluated in light of formative instruction features pointed out in the literature. The significance of…
Descriptors: Language Proficiency, English (Second Language), Second Language Learning, Second Language Instruction
Valizadeh, Mohammadreza; Soltanpour, Fatemeh – Eurasian Journal of Applied Linguistics, 2021
This experimental study, using a pretest-treatment-posttest design, compared the effects of focused direct written corrective feedback and additional writing practice on L2 learners' written syntactic complexity. The participants were 60 Iranian elementary EFL learners, whose L2 proficiency as well as L2 writing syntactic complexity and accuracy…
Descriptors: Error Correction, Feedback (Response), Teaching Methods, 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
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
Werner, Valentin; Lehl, Maria; Walton, Jonathan – International Journal of Mobile and Blended Learning, 2017
Pop lyrics represent a rich, but underused resource in language teaching in both institutional and informal contexts. This is striking in view of analyses from the fields of motivational and cognitive psychology, didactics as well as linguistics, which all provide evidence for the inherent potential of pop lyrics. This paper will first take a…
Descriptors: Music, Teaching Methods, Video Technology, Telecommunications
Tono, Yukio; Satake, Yoshiho; Miura, Aika – ReCALL, 2014
This study reports on the results of classroom research investigating the effects of corpus use in the process of revising compositions in English as a foreign language. Our primary aim was to investigate the relationship between the information extracted from corpus data and how that information actually helped in revising different types of…
Descriptors: Computational Linguistics, Feedback (Response), Revision (Written Composition), English (Second Language)
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