Students' Perception of Electronic Feedback: Lecturer, Peer, and Self-Electronic Feedback on Writing Academic with Different Students' Writing Achievement
Bibliographic record
Abstract
The aimed of the study is to see the Writing is one of the essential skills that need honing through continuous practice. This skill is significant in the workplace, especially since this is the minimum skill needed in the day-to-day business, office, academe, and corporate transactions which is why students in EFL classes should be trained so that English writing proficiency is honed regardless of the socio-cultural background. The study is participated by 30 students through a cluster class from the Universitas Islam Negeri Intan Lampung. The study used a Likert-Scale questionnaire and the data were analyzed using the SPSS version 26 while the reliability and validity were evaluated through Cronbach’s Alpha coefficient. The data about students’ perception of the lecturer’s electronic feedback states that the majority of the students (24% of High students, 30% of Fair students, and 27% Low of students) and the students who received feedback on the perception in peer electronic feedback showed that majority of participants (20% of High students, 27% of Fair students, and 26% of Low students) felt that they received electronic feedback on grammar, organization, and vocabulary. While, the opinions of the participants reveal that the majority of them (17% of High students, 27% of Fair students, and 20% of Low students) felt that they did not receive self-feedback on grammar organization, and vocabulary. 75% of the participants embraces the role of the lecturer in providing electronic feedback and emphasizes the responsibility of the learners in correcting the errors committed by the learners, while 69% of the participants believed that it was important to receive peer electronic feedback, arguing that it was also the peer responsibility to give feedback for the leaners’ errors. Lastly, 75% believed that self-feedback was not too important to improve their language development, arguing that they did not get benefits from self-feedback because they were not sure about the errors they revised. In summary, the study suggests that e-feedback is a valuable tool for improving academic writing in EFL students. It necessitates broader adoption and customization in educational practices while also indicating areas for further enhancement, such as self-feedback and culturally sensitive feedback.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".