Enhancing Writing Skills with Social Media-Based Corrective Feedback
Bibliographic record
Abstract
This study investigates the effectiveness of utilizing corrective feedback delivered through social media networks to enhance the writing skills of students at Andijan State Institute of Foreign Languages. Adopting a mixed-methods approach, the research explores the integration of platforms such as Facebook to facilitate peer feedback, track student progress, and provide personalized learning experiences tailored to individual needs. The study involved a controlled experiment where participants were divided into an experimental group receiving online feedback and a control group receiving traditional feedback. The findings reveal that corrective feedback provided through social media significantly improves writing accuracy, fluency, and complexity. Students in the experimental group demonstrated marked improvements in sentence structure, grammar, vocabulary, and content organization compared to those in the control group. Moreover, the study highlights the potential of social media as an engaging and collaborative tool that motivates students and supports continuous learning outside the traditional classroom setting. These results underscore the importance of incorporating technology into language instruction, suggesting that social media networks can serve as an effective medium for enhancing the writing skills of learners in both formal and informal educational environments. The implications of this study are significant for educators seeking innovative methods to support student development and improve writing proficiency in the digital age.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".