Automated written corrective feedback: how well can students make use of it?

Ranalli, Jim

doi:10.1080/09588221.2018.1428994

Cited by 129 publications

(97 citation statements)

References 21 publications

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“…face-to-face or blended, in combination with the writing tool) or might be automatically triggered. Previous work already showed that feedback related to specific parts in the student text (specific feedback) is more effective and requires less mental effort compared to general feedback (Ranalli, 2018). Hence, to provide better explanations of the writing process, it might be good to tie the feedback to specific examples in the writing product.…”

Section: Discussionmentioning

confidence: 99%

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Conijn

Martínez-Maldonado

Knight

et al. 2020

Computer Assisted Language Learning

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Section: Discussionmentioning

confidence: 99%

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Conijn

Martínez-Maldonado

Knight

et al. 2020

Computer Assisted Language Learning

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“…Learners can revise their work freely without consulting with teachers. Ranalli [44] pointed out explicitness as one important determining factor for learners to revise their mistakes. That is, generic feedback, compared to specific feedback, can require more mental-effort expenditure but it is less clear and helpful.…”

Section: Automated Feedbackmentioning

confidence: 99%

“…The limitation of automated feedback is its one-size-fits-all nature and low accuracy rate. Because such feedback does not account for individual differences because its error types are not determined by pedagogical considerations but technological capacities [44]. Moreover, the evaluation and generation of discourse-related feedback is one big challenge for systems [45].…”

Section: Automated Feedbackmentioning

confidence: 99%

Review of Studies on Technology-Enhanced Language Learning and Teaching

2020

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In this study, we reviewed articles on technology-enhanced language learning and teaching. We aimed to summarize the content of reviewed articles in the following categories: (1) the number of articles published by journals and by year; (2) languages and skills; (3) technology used; (4) promising technologies. We reviewed 398 research articles. The highest number of articles was published in 2017 (n = 80), whereas the lowest number was published in 2014 (n = 53). The Computer Assisted Language Learning journal published the highest number of articles (n = 100), whereas the lowest number of published articles appeared in IEEE Transactions on Learning Technologies (n = 3). The most common target language was English (n = 267). Writing, speaking, and vocabulary gained the most attention in published articles. Twenty-three different technologies were identified and they were used 406 times. Based on our results, we made several implications and suggestions for future studies. This review study can serve as a guide for teaching and research communities who plan on designing language learning and teaching activities supported by technologies.

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“…Ranalli (2018) [13] used an AWCF-based error-correction task in his research which related to EFL writing. In his study, the explicitness and accuracy of the feedback was controlled and so as students' response type.…”

Section: ) Automated Written Corrective Feedbackmentioning

confidence: 99%

Review of Research on Technologies for Language Learning and Teaching

Nanjing¹

2019

JSS

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We reviewed literature from 2014 to 2018 on foreign/second language learning supported by technologies. We aimed to select empirical studies that show effect of technologies on language learning. In this review study, we describe types of technologies, their potential usage for language learning, advantage and disadvantages, and we also give some examples to understand our findings better. Besides, we compare our results with those obtained from earlier review studies, e.g. we identified several new technologies used in studies between 2014 and 2018 but never used before. We also make several suggestions for future studies in this field. Our results can be useful for teaching and research communities that plan to design technology-enhanced foreign/second language learning and teaching.

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Automated written corrective feedback: how well can students make use of it?

Cited by 129 publications

References 21 publications

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

Review of Studies on Technology-Enhanced Language Learning and Teaching

Review of Research on Technologies for Language Learning and Teaching

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