Improving Writing Skills through Essay Writing via 'Write & Improve' for Error Analysis and 'Padlet' for Collaborative Writing & Peer Review
Bibliographic record
Abstract
This study was carried out to improve the undergraduates’ writing skills through essay writing via Cambridge English 'Write & Improve' synchronously in the classroom for error analysis and 'Padlet' asynchronously outside the classroom for collaborative writing and peer review. However, previous research proved that Cambridge English 'Write & Improve' and Padlet could enhance students' engagement in mastering writing skills. It also recommended that the students be allowed to self-correct the most recurring errors in their writing tasks through error analysis using 'Write & Improve', and the students should go through peer review by writing collaboratively using Padlet. In this study, the researchers employed a quantitative and cross-sectional study by gathering and evaluating the results of two research groups, the control and experimental groups, both in the pre-test and the post-test. The experimental group pursued the training through digital learning, while the control group used traditional learning. However, after the post-test, the researchers administered the Paired t-test, as the sample size is 30 ( ), as part of the statistical analysis. The results showed that the calculated t (10.66) tabulated t (1.740) proved that the writing assessment training was effective. The Null Hypothesis , which said that the training had no significant effect because there was no significant improvement in the experimental group after the training, was rejected, and the Alternative Hypothesis (H1), which said that the training was effective because there was significant improvement in the experimental group after the training was accepted. Therefore, the findings demonstrated a subtle growth in the experimental group's writing skills in the post-test after three months of training.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".