EFFECTS OF FORM-FOCUSED PRACTICE AND FEEDBACK ON CHINESE EFL LEARNERS’ ACQUISITION OF REGULAR AND IRREGULAR PAST TENSE FORMS
Bibliographic record
Abstract
Conducted in English-as-a-foreign-language (EFL) classrooms at the university level in China, this quasi-experimental study compared the effects of three different corrective feedback treatments on 72 Chinese learners’ use of regular and irregular English past tense. Three classes were randomly assigned to a prompt group, a recast group, or a control group and then participated in form-focused production activities that elicited the target forms. In the two feedback groups, teachers consistently provided one type of feedback (i.e., either recasts or prompts) in response to learners’ errors during the activities, whereas in the control group, the teacher provided feedback only on content. Pretests, immediate posttests, and delayed posttests administered 2 weeks after the treatment assessed participants’ acquisition of regular and irregular past tense forms in both oral and written production. Comparisons of group means across testing sessions using a repeated-measures ANOVA consistently revealed large effects for time. Post hoc within-group analyses of the eight immediate- and delayed-posttest measures revealed significant gains by the prompt group on all eight measures, the recast group on four, and the control group on three. The effects of prompts were larger than those of recasts for increasing accuracy in the use of regular past tense forms, whereas prompts and recasts had similar effects on improving accuracy in the use of irregular past tense forms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".