The Inconclusive Effectiveness of Indirect, Coded and Direct Written Corrective Feedback on EFL Student Writing
Bibliographic record
Abstract
This article examines the effectiveness of written corrective feedback (WCF) on the writing of EFL students at a Japanese university. WCF as a research topic has matured over the past several decades, drawing scrutiny in the process. The emergence of communicative language instruction, which sought to destigmatize grammatical errors while de-emphasizing error correction, prompted some critics to argue for an abandonment of WCF while others enumerated its shortcomings. The experiment in this paper investigates the effectiveness of three types of WCF – Indirect, Coded, and Direct – on sentence-initial conjunctions (SICs) in 110 first-year students in a semester-long English course. Results showed that while the error rates of all three groups steadily dropped over the length of the experiment, the rate of the comparison group, which received no WCF on the targeted error, dropped the most. The discussion section proposes several explanations for the results while concluding that although WCF can be beneficial, it is not necessary. The paper contributes to the ongoing debate about the effectiveness of WCF and bears practical implications for those instructors who question whether providing WCF represents the best use of their time.
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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 | 0.009 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".