This repository contains a list of papers about written corrective feedback in educational settings.
The focus is mostly on feedback for written language errors made by English learners and on applying feedback principles to educational technologies. This includes generating effective feedback in writing assistance and chat systems. Thus, some papers touch on grammatical error detection, classification, and correction or neural text generation.
General Inclusion and Formatting Rules:
- Entries in a list are sorted by last name of the first author, then by publication date.
- Prefer the published version of a work to a preprint.
- Link to a paper profile page rather than a PDF whenever possible.
- Links are DOIs whenever possible.
- Bitchener and Knoch (2010): Raising the linguistic accuracy level of advanced L2 writers with written corrective feedback
- Borges et al. (2023): Let Me Teach You: Pedagogical Foundations of Feedback for Language Models
- Ellis (2009): A typology of written corrective feedback types
- Ene and Upton (2014): Learner uptake of teacher electronic feedback in ESL composition
- Evans (2013): Making Sense of Assessment Feedback in Higher Education
- Ferris (1999): The case for grammar correction in L2 writing classes: A response to truscott (1996)
- Finn et al. (2018): Learning more from feedback: Elaborating feedback with examples enhances concept learning
- Goldin et al. (2017): New Directions in Formative Feedback in Interactive Learning Environments
- Hattie and Timperley (2007): The Power of Feedback
- Kim and Bowles (2019): How Deeply Do Second Language Learners Process Written Corrective Feedback? Insights Gained From Think-Alouds
- Kulhavy and Stock (1989): Feedback in written instruction: The place of response certitude
- Lee (2013): Research into practice: Written corrective feedback
- Lyster and Randa (1997): Corrective Feedback and Learner Uptake
- Mason and Bruning (2001): Providing Feedback in Computer-based Instruction: What the Research Tells Us
- Narciss (2013): Designing and evaluating tutoring Feedback Strategies for Digital Learning
- Narciss et al. (2014): Exploring feedback and student characteristics relevant for personalizing feedback strategies
- Nichol and Macfarlane‐Dick (2006): Formative assessment and self‐regulated learning: a model and seven principles of good feedback practice
- Panadero and Lipnevich (2022): A review of feedback models and typologies: Towards an integrative model of feedback elements
- Sauro (2009): Computer-mediated corrective feedback and the development of L2 grammar
- Shute (2008): Focus on Formative Feedback
- Wiggins (2012): Seven Keys to Effective Feedback
- Bitchener (2008): Evidence in support of written corrective feedback
- Brown et al. (2023): Effectiveness of written corrective feedback in developing L2 accuracy: A Bayesian meta-analysis
- Han and Hyland (2015): Exploring learner engagement with written corrective feedback in a Chinese tertiary EFL classroom
- Kang and Han (2015): The Efficacy of Written Corrective Feedback in Improving L2 Written Accuracy: A Meta-Analysis
- Pilan et al. (2020): A Dataset for Investigating the Impact of Feedback on Student Revision Outcome
- Zhang and Hyland (2022): Fostering student engagement with feedback: An integrated approach
- Fleckenstein et al. (2023): Automated feedback and writing: a multi-level meta-analysis of effects on students' performance
- Koltovskaia (2020): Student engagement with automated written corrective feedback (AWCF) provided by Grammarly: A multiple case study
- Mertens et al. (2022): Effects of computer-based feedback on lower- and higher-order learning outcomes: A network meta-analysis.
- Nagata and Nakatani (2010): Evaluating performance of grammatical error detection to maximize learning effect
- O'neill and Russel (2019): Stop! Grammar time: University students’ perceptions of the automated feedback program Grammarly
- Ranalli (2018): Automated written corrective feedback: how well can students make use of it?
- Ranalli (2021): L2 student engagement with automated feedback on writing: Potential for learning and issues of trust
- Thi and Nikolov (2022): How Teacher and Grammarly Feedback Complement One Another in Myanmar EFL Students’ Writing
- Van der Kleij et al. (2015): Effects of Feedback in a Computer-Based Learning Environment on Students’ Learning Outcomes: A Meta-Analysis
- Bryant et al. (2017): Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
- Coyne (2023) Template-guided Grammatical Error Feedback Comment Generation
- Choshen et al. (2020): Classifying Syntactic Errors in Learner Language
- Choshen et al. (2021): SERRANT: a syntactic classifier for English Grammatical Error Types
- Chujo et al. (2015): A corpus and grammatical browsing system for remedial EFL learners
- Dahlmeier et al. (2013): Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English
- Ishii and Tono (2018): Investigating Japanese EFL Learners’ Overuse/Underuse of English Grammar Categories and Their Relevance to CEFR Levels
- Lee et al. (2015): CityU corpus of essay drafts of English language learners: a corpus of textual revision in second language writing
- Nicholls (2003): The Cambridge Learner Corpus-Error coding and analysis
- Swanson and Yamangil (2012): Correction Detection and Error Type Selection as an ESL Educational Aid
- Winder et al. (2017): NTUCLE: Developing a Corpus of Learner English to Provide Writing Support for Engineering Students
- Coyne (2023) Template-guided Grammatical Error Feedback Comment Generation
- Dai et al. (2023): Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT
- Fei et al. (2023): Enhancing Grammatical Error Correction Systems with Explanations
- Grenander et al. (2021): Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
- Hanawa et al. (2021): Exploring Methods for Generating Feedback Comments for Writing Learning
- Kaneko and Okazaki (2023) Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction
- Kochmar et al. (2022): Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems
- Lai and Chang (2019): TellMeWhy: Learning to Explain Corrective Feedback for Second Language Learners
- Liang et al. (2023): ChatBack: Investigating Methods of Providing Grammatical Error Feedback in a GUI-based Language Learning Chatbot
- Morgado da Costa et al. (2020): Automated Writing Support Using Deep Linguistic Parsers
- Nagata (2019): Toward a Task of Feedback Comment Generation for Writing Learning
- Nagata et al. (2020): Creating Corpora for Research in Feedback Comment Generation
- Nagata et al. (2021): Shared Task on Feedback Comment Generation for Language Learners
- Nagata et al. (2023): A Report on FCG GenChal 2022: Shared Task on Feedback Comment Generation for Language Learners
- Song et al (2023): GEE! Grammar Error Explanation with Large Language Models
- Steiss et al. (2024): Comparing the quality of human and ChatGPT feedback of students’ writing
- Caines et al. (2020): The Teacher-Student Chatroom Corpus
- Caines et al. (2022): The Teacher-Student Chatroom Corpus version 2: more lessons, new annotation, automatic detection of sequence shifts
- Huang et al. (2021): Chatbots for language learning—Are they really useful? A systematic review of chatbot-supported language learning
- Tack et al. (2022): The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
- Tack et al. (2023: The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues)
- Tyen et al (2022): Towards an open-domain chatbot for language practice
- Wollny et al. (2021): Are We There Yet? - A Systematic Literature Review on Chatbots in Education
- Xiao et al. (2023): Conversational agents in language learning
- Yuan et al. (2022): ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction