Corrective Feedback Research Articles

Teacher’s written and verbal corrective feedback on pupils’ test anxiety

Teacher’s written and verbal corrective feedback on pupils’ test anxiety

The timing of corrective feedback in second language learning

The timing of corrective feedback (CF), alternatively called feedback timing, refers to the choice of a timepoint for providing corrections on second language (L2) errors or making comments on the appropriacy of L2 learners' verbal or nonverbal behaviors. A typical distinction related to the notion of feedback timing is between immediate and delayed feedback, but what constitutes immediate or delayed has been interpreted and defined in different ways. In one stream of research, immediate feedback is operationalized as feedback provided during a learning task and delayed feedback as feedback provided after a task is completed (Arroyo & Yilmaz, 2018*; Li Zhu & Ellis, 2016a*; Quinn, 2014*). One methodological variation in this distinction is interim feedback, which is provided after the first task is completed and before the second task is started (Li, Li, & Qian, under review). Interim feedback is relevant or possible when multiple tasks are performed. It refers to feedback provided during the interval(s) between tasks. Interim feedback is different from delayed feedback in that the latter refers to feedback provided after the task (if there is only one task) or all tasks (if there are multiple tasks) are completed and there is no further task performance following the feedback session. This way of conceptualizing feedback timing is based on the positioning of feedback during a task cycle, instead of the proximity to errors. Another way to examine feedback timing is to distinguish feedback provided immediately after an error is made and feedback delayed until a later time in the instructional cycle, such as one week later (Lavolette, Polio, & Kahng, 2015*). In this case, both immediate and delayed feedback can occur either during or after the completion of a learning task. A third way is to define feedback timing options in terms of their relation to instruction, namely whether feedback is provided immediately after explicit instruction or at a later stage after learners complete some practice activities (Fu & Li, 2022*). It should be clarified that this way of operationalizing feedback timing is markedly different from that in other studies in that it focuses on feedback's relation to instruction instead of errors. To conclude this section, it is necessary to point out that the conceptualization and operationalization of feedback timing should be reconsidered in L2 research. Feedback timing is not merely a matter of the length of interval or the distance between errors and feedback, and other parameters of the instructional system where errors occur are also involved or relevant, such as the distance between feedback and instruction, the positioning of feedback in a task cycle (such as within, after, or between tasks), and so on. These parameters are important because they contribute to the effectiveness of different timing options. Despite the variation in the operationalization of feedback timing, we argue that it is a unified construct that is theoretically justifiable, empirically examinable, and pedagogically valuable.

A sociocultural exploration of Iranian language teachers’ corrective feedback: why and how?

ABSTRACT This study aims to explore the types and reasons for teachers’ corrective feedback (CF) in English as a Foreign Language (EFL) classes from sociocultural perspectives. To do so, 12 Iranian EFL teachers were selected based on convenience sampling, and 18 sessions of their classes (one or two from each teacher) were observed to ensure that there were at least three CF occurrences in each teacher’s class. Then, the teachers attended stimulated recall interviews in which they answered questions about reasons for applying specific CFs in their classes. Different subthemes emerged from the data within the three sociocultural mediators’ themes, namely psychological, material and peer mediators. The results indicated that teachers use different CFs in their classes for various reasons such as scaffolding learning, enhancing noticing and exposure, and fostering learners’ agency. The study ends with suggestions and implications for teachers and teacher educators.

What does play have to do with it? A concrete and digital spatial intervention with 3-year-olds predicts spatial and math learning.

Spatial skills like block building and puzzle making are associated with later growth in science, technology, engineering, and mathematics learning. How these early spatial experiences-both in concrete and digital platforms-boost children's spatial skills remains a mystery. This study examined how children with low- and high-parental education use corrective feedback in a series of spatial assembly tasks. We further ask whether this spatial learning increases near- and far-transfer spatial and math skills. U.S. preschoolers (N = 331) were randomly assigned to either a "business-as-usual" control or one of six spatial training groups (comprising concrete and digital training with modeling and feedback [MF], gesture feedback, or spatial language feedback). Children were trained for 5 weeks to construct 2D puzzles that match a model using a variety of geometric shapes. Pre- and posttests evaluated 2D and 3D spatial assembly, spatial language comprehension, shape identification, and math performance. Results indicate performance enhancement in trained 2D spatial assembly across all six trainings. Digital gesture feedback transferred, boosting 3D spatial assembly performance. Both concrete and digital spatial language feedback trainings increased shape identification performance. Concrete-MF significantly (and digital-MF marginally) increased word problem math performance for children with lower parental education. Finally, collapsing across conditions, both concrete and digital training increased overall spatial skills, especially for preschoolers with lower parental education. Transfer to overall mathematics performance was far less robust. Overall, early concrete and digital spatial assembly experiences seem to support preschoolers' spatial skill development but have a minor impact on mathematics skill development. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Research on a Hybrid Correction Method for SINS/DVL Kalman Filter Integrated Navigation Based on Convergence Factor Judgment

Abstract The strapdown inertial navigation system (SINS)/doppler velocity log (DVL) integrated navigation is one of the primary methods for underwater navigation, providing autonomous underwater vehicles with precise information on position, velocity, and attitude. The core idea of the SINS/DVL integrated navigation using the Kalman filter is to establish the state and observation equations to estimate the SINS navigation error using DVL velocity. The optimal state estimation and correction strategies are critical to the accuracy of integrated navigation. To address this, this paper proposes a hybrid correction method for SINS/DVL Kalman filter integrated navigation based on a convergence factor judgment (HCKF-CFJ). First, the convergence factor for the Kalman filter is constructed using the diagonal elements of the estimated error covariance matrix corresponding to the velocity error state. Then, the filter's stability is assessed based on this convergence factor. When stability is confirmed, direct feedback correction is applied to the SINS velocity, and indirect feedback correction is applied to the SINS position. SINS velocity and position undergo feedforward correction if the stability condition is not met. Finally, ship experiments validate the effectiveness of the proposed method.

ChatGPT and L2 Chinese writing: evaluating the impact of model version and prompt language on automated corrective feedback

The rapid emergence of generative artificial intelligence (GAI) models like ChatGPT has sparked significant interest in their application for language learning, particularly for second language (L2) writing. Given the urgent need for effective tools in Chinese grammar checking to assist L2 learners, this study evaluated the impact of both model version (ChatGPT-3.5 vs 4.0) and prompt language (Chinese vs English) on the effectiveness of automated corrective feedback (ACF) for L2 Chinese writing. Utilizing a dataset of 153 erroneous single-sentence examples from a Routledge-published textbook on Chinese, we assessed error corrections and corrective feedback generated by both ChatGPT versions under different language prompts. Three experienced language teachers evaluated the output corrections for grammaticality, fluency, minimal alterations, and over-correction, and the output feedback for correctness, understandability, and detail. Findings revealed that although both model versions produced grammatically correct and fluent corrections, ChatGPT-4.0 demonstrated superior performance in generating more accurate, detailed, and understandable corrective feedback compared to ChatGPT 3.5. The results suggest that model version significantly influences ChatGPT’s effectiveness as a multilingual ACF tool, more so than prompt language. This study highlights the potential of advanced GAI, such as ChatGPT-4.0, in enhancing language instruction and error correction for languages beyond English. It advocates for further research on the application of such models in diverse linguistic and educational contexts.

Exploring Teachers' Beliefs in Written Corrective Feedback (WCF) in Kuala Lumpur Secondary Schools

Exploring Teachers' Beliefs in Written Corrective Feedback (WCF) in Kuala Lumpur Secondary Schools

Discrepancy between performance and feedback affects mathematics student teachers' self-efficacy but not their self-assessment accuracy.

Although feedback is of high importance for the professional development of student teachers, the impact of (inadequate) feedback on their self-regulated learning is still unclear. In two studies with mathematics student teachers, we investigated how discrepancies between performance and feedback affected two important aspects of self-regulated learning-self-efficacy and self-assessment accuracy regarding mathematical content knowledge. In the first study, N = 154 student teachers studying mathematics completed a knowledge test on the Pythagorean theorem and received performance feedback that was either correct or manipulated to be more positive or more negative than actual performance. The results showed that feedback that exceeded performance resulted in higher self-efficacy than feedback that fell below performance. In contrast, self-assessment accuracy in a second test on the same content was not affected by the discrepancy between student teachers' test performance and the feedback they received. In the second study, we used the think-aloud method with N = 26 participants to investigate the processes underlying the effects obtained in Study 1. We found that student teachers who had received overly positive feedback were more likely to report positive affect-related statements than participants who had received overly negative or correct feedback. At the same time, they based their self-assessments in the knowledge test more strongly on their monitoring of heuristic factors than on knowledge. The results indicate that overly positive feedback elicits positive motivational states in mathematics student teachers, but bears the risk that they neglect their knowledge as a basis for their self-assessments.

Examining EFL learners’ quantity and quality of uptake of teacher corrective feedback on writing across three different editing settings

Examining EFL learners’ quantity and quality of uptake of teacher corrective feedback on writing across three different editing settings

Challenges in Written Corrective Feedback: Young EFL Learners’ Attitudes and Perceptions of the Boomerang WCF Strategy

Challenges in Written Corrective Feedback: Young EFL Learners’ Attitudes and Perceptions of the Boomerang WCF Strategy

Correction Codes: A Tool for Enhancing Writing Qualities

Writing is essential for documenting ideas, feelings and information. Students submit assigned tasks in written forms, which teachers mark for errors using codes to prevent their recurrence in the future. The symbols teachers use to provide written feedback should help improve student writing. However, most students’ fail to understand such symbols. Consequently, they pay less attention to the feedback, and continue to make similar mistakes in their subsequent writings. This qualitative study aimed to explore various forms of codes used by 10 English language teachers teaching in grades 11/12 in diff erent schools in Kavrepalanchok district of Bagmati Province. The study found that students did not understand most of the correction codes used by teachers, nor did they contact the teachers morever for clarifi cation. This lack of interaction hindered the eff ectiveness of these codes in improving writing. Teachers did not orient students about such codes, and most students did not seek help to understand them. It was recommended that the teacher use context-based techniques for scoring and providing written corrective feedback.

Facilitator or thinking inhibitor: understanding the role of ChatGPT-generated written corrective feedback in language learning

ABSTRACT Written corrective feedback (WCF) is crucial in foreign language learning, helping learners identify and rectify writing errors. However, manual WCF can be time-consuming for educators. Artificial general intelligence (AGI), exemplified by ChatGPT, offers a promising solution via automated WCF, addressing learners’ intricate linguistic demands. While effective in English learning, the suitability of AGI-powered automated WCF for other languages with complex grammar remains uncertain due to the English-centric training of many large language models. This study examines the reliability and acceptance of ChatGPT-generated WCF within a German as a foreign language (GFL) context. Through a 200-essay analysis, we explore two questions: (1) How does ChatGPT-generated automated WCF compare to teacher-provided WCF? (2) How do GFL learners respond and interact with WCF from both sources? Our findings revealed different behavioral, cognitive, and affective engagements between ChatGPT-provided WCF and teacher-provided WCF. ChatGPT has greater potential for providing diverse lexical, grammatical, structural, and content feedback, facilitating GFL learning. However, students’ emotional perceptions of ChatGPT-generated WCF vary, sometimes even negative, hindering language learning. We further explored the advantages and challenges of integrating ChatGPT-generated WCF into foreign language learning. Our findings underscore the potential of integrating AI-powered automated WCF into foreign language learning and writing development.

Speak Up! Empowering Vocational EFL Students through Oral Corrective Feedback

Given the significance of corrective feedback in enhancing language learners’ speaking skills, especially in boosting accuracy and motivation, this study aims to examine the impact of direct oral corrective feedback (OCF) on Indonesian EFL learners. While research has shown that teacher feedback plays a crucial role in student performance, there is limited insight into how students interpret and engage with feedback—a factor essential to its effectiveness. Employing a descriptive qualitative research method, this study involved 39 vocational school students from SMK Medikacom in Bandung, Indonesia. Data were collected through classroom observations and semi-structured interviews, enabling a thorough exploration of students’ responses, behaviours, and attitudes toward Oral Corrective Feedback (OCF). Oral Corrective Feedback (OCF) improved students' speaking skills, with Recast, Clarification Request, and Repetition being the most common types and Recast being the most effective for error correction without disrupting communication. Students reported gains in pronunciation, grammar, fluency, confidence, and teacher-student rapport. Some students, however, felt discomfort with feedback, highlighting the need for a supportive, non-judgmental approach to prevent negative emotional impacts. This study suggests that while implicit feedback benefits advanced learners, lower-proficiency students may need more explicit correction. To maximise OCF’s impact, educators are encouraged to tailor feedback to individual proficiency levels and focus on fostering supportive, adaptive classroom environments.

Understanding student engagement with teacher feedback in Chinese-to-English consecutive interpreting courses

While research on student engagement with oral and written corrective feedback (CF) in second language acquisition (SLA) is well established, the study of such engagement within the context of interpreting, which requires bilingual conversion, is not as developed. Building on Ellis’ tripartite conceptualization of student engagement – comprising affective, cognitive, and behavioral responses – this study explored the engagement of 30 students with teacher feedback in three Chinese-to-English consecutive interpreting courses. Data were collected through classroom recordings, semi-structured interviews, stimulated recall sessions, and students’ in-class notes. The findings indicated that, affectively, all students highly valued and presented overall affirmative emotions towards teacher feedback. Cognitively, students employed various cognitive strategies, including active attribution, mental rehearsal, and information categorization in processing feedback information; however, they encountered challenges in fully understanding the content and process-focused feedback. Behaviorally, two predominant note-taking patterns emerged: ‘language-oriented’ and ‘skill-oriented’. The findings emphasize the critical role of interpreting practice in language acquisition and underscore the need to refine pedagogical approaches in interpreting courses to support both language learning and interpreter training.

Incorporating ChatGPT as an automated written corrective feedback tool into L2 writing class

This review focuses on the use of automated written corrective feedback (AWCF) tools, particularly ChatGPT, in second language (L2) writing instruction. Writing is essential but challenging for L2 learners, and feedback plays a crucial role in enhancing writing skills. However, traditional teacher-provided written corrective feedback (WCF) faces challenges such as time constraints, cognitive overload, and inconsistency, especially in large classes. AWCF tools like Grammarly, Criterion, and ChatGPT help overcome these limitations by providing immediate and comprehensive feedback. The review begins by discussing the role of WCF in L2 writing, while highlighting the challenges associated with traditional feedback provision methods. It then explores the benefits and limitations of AWCF tools based on existing studies, noting their ability to offer instant feedback, reduce teachers’ workload, and motivate learners. Focusing on ChatGPT, the review highlights its ability to generate contextually appropriate and personalized feedback. ChatGPT offers several advantages, including promoting learner autonomy, enhancing feedback literacy, and improving writing quality by providing immediate corrections and suggestions. Learners have also shown positive perceptions of ChatGPT’s feedback in addressing grammatical errors and improving writing complexity.

Oral Corrective Feedback in the EFL Classroom

This paper aims to see college students’ perceptions of oral corrective feedback in their EFL class. The number of respondents is 95 students from different study programs at the University of Tetova who attend EFL classes. The 10 Likert-scale questions tend to navigate through their preferences on the CF types, along with their correlation with students’ proficiency levels. Additionally, we aim to see what they perceive in terms of the most positively impacted aspect of language by oral corrective feedback, such as pronunciation, vocabulary, and grammar or sentence structure. Furthermore, the paper’s objective is to compare all these data with the emotional response of students after being orally corrected in their EFL class.

The Effects of ChatGPT-Generated Feedback on Saudi EFL Learners’ Writing Skills and Perception at the Tertiary Level: A Mixed-Methods Study

Corrective feedback plays a critical role in enhancing writing skills among English as a Foreign Language (EFL) learners, but large class sizes often hinder the provision of personalized feedback. Generative AI tools such as ChatGPT have emerged as promising solutions that offer immediate and individualized feedback for writing. This study examined the effects of ChatGPT-generated feedback in comparison to teacher-generated feedback on Saudi EFL students’ writing skills and perception at the university level. A mixed-method approach was employed to ensure comprehensive data collection and analyses. Using the pretest-posttest control group design, 102 participants were assigned to an experimental group, which received ChatGPT-generated feedback, and a control group, which received teacher-generated feedback. Pretests, posttests, and a perception survey were used to collect the data. ANCOVA, with pretest scores as a covariate, and content analysis of perception data were conducted. The results indicated no statistically significant differences in posttest scores between the experimental group and the control group, suggesting that ChatGPT-generated feedback was as effective as teacher-generated feedback. The perception survey revealed an overall positive attitude toward the use of ChatGPT for academic writing. The study provides empirical evidence supporting the effectiveness of generative AI tools in academic writing contexts.

Common Ground inconsistencies in dialogue systems: conflict patterns implied by polar question forms

In linguistics, research on dialogue systems has accentuated the need to focus on various pragmatic aspects for their management and modelling. Among the most important pragma-linguistic speech acts in dialogue systems studies are Clarification Requests, corrective feedback that in some circumstances require access to the set of shared knowledge known as Common Ground. Regarding Common Ground management, pragmatic studies suggest differences in the type of polar questions that people prefer be used in Clarification Requests, where polar questions can have two possible answers: true or false. This preference appears to depend on the relationship between bias and contextual evidence. In this work, we show that varying the form of polar questions in a given pragmatic setting can influence the capability of people to track Common Ground inconsistencies. As a result, we demonstrate that using a negative polar question in Italian has functional consequences when communicating conflicting material in the Common Ground. This can improve the quality of human interactions with dialogue systems, in terms of an improved identification of the conflict. The results obtained in this work provide insights into design of error reporting approaches in natural interactions.

L’utilisation de ChatGPT 3.5 pour la rétroaction corrective écrite interactive en enseignement-apprentissage du français langue seconde : une étude exploratoire

Generative artificial intelligence (GenAI) tools are becoming increasingly accessible, which makes it imperative to critically examine their potential impact in the educational environment, as well as the ways in which they can be integrated to enable learners to benefit from them. ChatGPT (OpenAI, 2022) is a conversational GenAI tool with which users can interact. The interactive nature of this tool seems to suggest affordances in language teaching and learning, specifically for textual revision. This exploratory study focuses on the use of ChatGPT for interactive written corrective feedback (WCF) in French as a second language. Participants (n=22) were French as a second language learners in a second-year university course. They first answered a questionnaire about their self-corrective practices for French writing tasks. Then, during a one-off intervention, they interacted with ChatGPT to solicit interactive WCF during synchronous exchanges. Finally, the participants answered a questionnaire about this experience and their perceptions of AI. A taxonomic analysis (Bilmes, 2009) within the framework of a qualitative content analysis (Selvi, 2019) was undertaken to develop typologies to classify the messages in the collected discussion threads. The quality of ChatGPT’s responses was also analyzed. The results show that many different types of prompts were created, but there were discrepancies in the degree of participant engagement. That said, the vast majority of participants claim to have benefited from this experience. ChatGPT’s responses were largely correct and appropriate, but the quality of the prompt was found to affect the quality of the response it solicits. The results of this study seem to demonstrate that ChatGPT could be a useful tool for interactive WCF in French language learning, however, more research is needed.

Investigating EFL children’s task motivation concerning the use of models as written corrective feedback

Investigating EFL children’s task motivation concerning the use of models as written corrective feedback