Corrective Feedback in Second Language Teaching and Learning : Research, Theory, Applications, Implications
Bibliographic record
Abstract
Introduction: The role of corrective feedback: Theoretical and pedagogical perspective - Hossein Nassaji and Eva Kartchava PART 1: ORAL CORRECTIVE FEEDBACK Chapter 1: Oral corrective feedback in L2 classrooms: What we know so far - Rod Ellis Chapter 2: The nature of peer corrective feedback during oral interaction: Cognitive and social perspectives - Masatoshi Sato Chapter 3: The timing of oral corrective feedback - Paul Gregory Quinn (University of Toronto) and Tatsuya Nakata PART 2: COMPUTER-MEDIATED CORRECTIVE FEEDBACK Chapter 4: Computer-assisted corrective feedback and language learning - Trude Heift and Volker Hegelheimer Chapter 5: Peer corrective feedback in computer-mediated collaborative writing - Neomy Storch Chapter 6: Interactional feedback in computer-mediated communication: A review of the state of the art - Nicole Ziegler and Alison Mackey PART 3: WRITTEN CORRECTIVE FEEDBACK Chapter 7: Language-focused peer corrective feedback in second language writing - Magda Tigchelaar and Charlene Polio Chapter 8: Negotiated oral negotiation in response to written errors - Hossein Nassaji Chapter 9: Why some L2 learners fail to benefit from written CF Corrective Feedback - John Bitchener PART 4: STUDENT AND TEACHER ISSUES IN CORRECTIVE FEEDBACK Chapter 10: Student and teacher beliefs and attitudes towards corrective feedback - Shaofeng Li Chapter 11: Non-verbal Feedback - Kimi Nakatsukasa and Shawn Loewen Conclusion, reflections, and final remarks - Hossein Nassaji and Eva Kartchava List of Contributors
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".