Error Analysis in Second Language Writing: An Intervention Research
Bibliographic record
Abstract
Error analysis has been a widely used approach to assess the writing of second-language learners. With an extensive literature review, this research investigates the origins of error analysis, its development and applications in second-language writing competency. This research utilizes error analysis to examine errors made by writers using a second language and their impact on language learning and teaching. Thereafter, explores the advantages and disadvantages of error analysis for evaluating second-language writing. And, by assessing the accurate use of grammar and vocabulary in second-language writing following explicit instruction. The participants were intermediate-level English language learners who underwent a pre-test consisting of a writing task and self-assessment of their confidence in using grammar and vocabulary correctly. During the intervention, explicit instruction about grammar and vocabulary usage was provided to participants, including examples, practice opportunities, and feedback. A post-test included another writing exercise, a confidence assessment, and an inquiry about the effectiveness of the instruction given. The results showed that explicit training significantly boosted participants' confidence in using grammar and vocabulary correctly and improved accuracy in their use of written sentences. These findings suggest that targeted instruction on specific use of grammar and vocabulary can effectively enhance second-language writing skills. (Swain & Lapkin, 2000)
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".