eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

Zhang, Haoran; Magooda, Ahmed; Litman, Diane; Correnti, Richard; Wang, Elaine; Matsumura, Lindsay Clare; Howe, Emily; Quintana, Rafael

doi:10.1609/aaai.v33i01.33019619

Cited by 28 publications

(27 citation statements)

References 17 publications

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“…Therefore, students may be reluctant to engage more deeply with those meandering instructions. When receiving indirect or metalinguistic corrective feedback, students may remain ignorant of how to correct their texts (Zhang et al, 2019 ). Only 76% of revisions through short grammatical descriptions were successful.…”

Section: Discussionmentioning

confidence: 99%

Peer Feedback in Translation Training: A Quasi-Experiment in an Advanced Chinese–English Translation Course

et al. 2021

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Although research on peer feedback in second language teaching and learning has been developed from various perspectives over the past three decades, less is known about feedback in translation settings. This study reports the results of a quasi-experiment with advanced second language learners in a Chinese–English translation course. It examines how effective peer feedback is in improving the quality of translations. The following data were collected from 30 students: their initial translation drafts, the drafts with the feedback of their peers, and the final corrected translations. The whole process was facilitated by computer assistance and under anonymity. It was found that most students drew on direct or indirect corrective feedback while few students drew on metalinguistic corrective feedback. Text genres were also proved to impact the types and counts of peer feedback. An analysis of the accuracy rate of corrections after peer feedback showed that it had a positive impact on translation quality. The findings shed light on the applicability of peer feedback in other pedagogical activities.

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Section: Discussionmentioning

confidence: 99%

Peer Feedback in Translation Training: A Quasi-Experiment in an Advanced Chinese–English Translation Course

et al. 2021

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“…We did not initially consider paraphrases as a desirable type of revision; yet, this code showed a significant positive correlation. While unexpected, we were not altogether surprised as two of the feedback messages (shown in Table 1) did explicitly ask for students to put ideas into their own words (see (Zhang et al, 2019) for details). Although addition of evidence and reasoning revisions demonstrated correlation to the 'improvement score', deletions and modifications did not show any intuitive correlation.…”

Section: Evaluation Of the Rer Schemementioning

confidence: 94%

Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing

Afrin¹,

Wang²,

Litman³

et al. 2020

Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

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Automated writing evaluation systems can improve students' writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the 'RER' scheme) and apply it to 5th-and 6th-grade students' argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.

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“…Deep learning architectures based on LSTM with or without attention, hierarchical CNN, and the use of pre-train language models like BERT have successfully been able to extract high-order features associated with argumentation features. Particularly, [41], [66] are examples of automated feedback systems on argumentation skills of student work.…”

Section: Argumentative Reasoningmentioning

confidence: 99%

The State of the Art in Providing Automated Feedback to Open-Ended Student Work

Larrondo

Frank

Ortíz

2021

PCEEA

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This article provides a review of the state of the art of technologies in providing automated feedback toopen-ended student work on complex problems. It includes a description of the nature of complex problems and elements of effective feedback in the context of engineering education. Existing technologies based on traditional machine learning methods and deep learning methods are compared in light of the cognitive skills, transfer skills and student performance expected in a complex problemsolving setting. Areas of interest for future research are identified.

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eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

Cited by 28 publications

References 17 publications

Peer Feedback in Translation Training: A Quasi-Experiment in an Advanced Chinese–English Translation Course

Peer Feedback in Translation Training: A Quasi-Experiment in an Advanced Chinese–English Translation Course

Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing

The State of the Art in Providing Automated Feedback to Open-Ended Student Work

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