Written Corrective Feedback and Its Challenges for Pre-Service ESL Teachers
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
Abstract: This study explored the emerging corrective feedback (CF) practices of a group of 18 pre-service English as a second language (ESL) teachers. Serving as tutors to a group of 61 high school ESL learners during a school semester, the pre-service teachers provided CF on texts written by the learners and exchanged via e-mail. The authors analyzed the types of CF they used and the types of errors they chose to focus on, along with the factors that explained their choices. Quantitative analyses of the frequency distribution of CF types relative to error types and qualitative analyses of data collected through journals and interviews confirmed that, similar to their in-service colleagues, pre-service teachers overused direct corrections at the expense of more indirect CF strategies. Drawing on the challenges faced by the pre-service teachers, the authors highlight the importance of implementing such opportunities for pre-service teachers to engage with and reflect on their emerging CF practices.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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