The Effect of Teacher Feedback on the Simple Past Tense Acquisition in Senior High School Students' English Writing
Bibliographic record
Abstract
Teacher feedback is an essential part which can not be overlooked in English writing teaching. Whether teacherfeedback is effective and which type of feedback is more effective in improving learners’ writing proficiency haveattracted more and more researchers’ attention. Direct feedback is the feedback that teachers correct students’ errorsdirectly under the form or the right using habit of target language. Indirect feedback is the feedback that teachersonly mark out or point out students’ errors but not correct errors. This study explores the effects of two differentkinds of feedback (direct feedback and indirect feedback) on the acquisition of English simple past tense in highschool students’ writing. And it attempts to answer the following three questions: 1) Do teachers’ direct and indirectfeedback in English writing influence senior high school students’ acquisition of simple past tense? 2) If so, whichkind of feedback (direct or indirect feedback) has the greater influence on improving students’ acquisition of simplepast tense in English writing? 3) What perceptions do students have towards teachers’ direct and indirect feedback inEnglish writing?The result showed that the two kinds of feedback have an influence on the acquisition of the target structure,however, there is no significant difference between direct and indirect feedback group, which states that indirectfeedback class was not better than direct feedback class.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".