Corrective Feedback in Second Language Teaching and Learning : Research, Theory, Applications, Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it