Comparing Individual and Collaborative Translation in Google Docs: An Investigation of Thai EFL Undergraduates Translation Skills
Bibliographic record
Abstract
Existing research has indicated the benefits of synchronous collaboration through Google Docs in enhancing language skills and productivity among learners. However, there remains a lack of knowledge concerning synchronous collaborative translation within the context of English as in Foreign Language (EFL) classrooms, particularly concerning its impact on the quality of students’ translations. This study aims to investigate the English translation skills of 20 English majors, aged between 18 and 23 years old, enrolled in the Faculty of Arts and Humanities at a private university in Thailand. The investigation compares individual translation with synchronous collaborative translation in small-groups, conducted under time constraints, utilizing three distinct tests. Additionally, the study examines the prevalent language errors made by individuals and small-groups during the translation process. The findings of the study reveal significant disparities between individual translation and collaborative translation in small- groups across all three test variations employed. Notably, the results suggest that engaging in collaborative work within small-groups leads to higher translation accuracy when compared to individual efforts. Regarding the identified errors, this study highlights word choice, mechanics, articles, prepositions, and ellipses as the most frequently occurring mistakes in both individual and small-group translations. This research briefly discussed the implications of the identified errors of collaborative translation in EFL classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".