Feedbacking Strategies of English Language Teachers on the Written Outputs of Students in Distance Learning
Bibliographic record
Abstract
This study aimed to describe the feedback strategies of English Language Teachers on the written outputs of students in distance learning and how these strategies help the students improve their written outputs. It also aimed to propose a feedback framework that can be adopted by Language teachers in distance learning. This study was conducted using a general qualitative inquiry design in which a researcher-made validated interview guide served as the primary data collection tool. The study had 10 English Language Teachers as Participants who were chosen purposively using criteria set by the researcher. The findings of this study revealed that English teachers use positive words to motivate students to do better, provide constructive criticisms to improve students’ outputs, observe confidentiality, remind students to avoid plagiarism, focus on grammatical errors, use rubrics for objectivity’s sake, and give general comments on errors in class. The participants perceived that these feedback strategies lead to improvement, motivate students to keep on improving their outputs, raise awareness among students on their errors, and assure students that their outputs are being monitored. Furthermore, the things that the English teachers consider when they provide feedback on the written outputs of their students include being punctual, and assuring students that their outputs are being monitored. Additionally, feedback strategies have to be humane, and objective, and should check on the authenticity of students’ output. Lastly, the proposed feedback framework is centered on improving students’ written outputs. Its seven elements are complementary to each other to realize the main objective of the framework.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".