Coherence and Cohesion in an ESL Academic Writing Environment: Rethinking the Use of Translation and FOMT in Language Teaching
Bibliographic record
Abstract
For several years, the use of translation and specifically Machine Translation - including Free Online Machine Translation (FOMT) tools - in L2 curricula has been the subject of ongoing debate. Even though the use of such tools is commonly discouraged in L2 classrooms by educators, the persistence of English as a second language (ESL) students in utilizing the tools has inspired many scholars to investigate whether it is helpful to develop effective strategies that transform FOMT into a teaching/learning tool in the ESL/English for specific purposes (ESP) classroom. Specifically, scholars have examined how FOMT can impact or enhance the writing quality of ESL students' compositions in terms of coherence and cohesion. In line with the same research interests, this project examined ESL students' typical coherence/cohesion challenges in academic writing at an Ontario post-secondary institution offering courses in French. The study explored the writing behaviours, such as the use of technologies including FOMT, that influence these challenges. In addition, this project sought to ascertain whether ESL students can be trained to better achieve coherence/cohesion in academic writing and how this training affects their writing behaviours, with particular attention to the use of technologies such as FOMT. In doing so, the study employed a mixed-methods research design and collected survey data, writing samples and screen recordings from 6 high-intermediate-level ESL students. Survey data was also collected from 23 ESL instructors about ESL students' practices, including tool use. Semi-structured interviews were conducted with the students and 3 instructors who evaluated the writing samples. Based on the survey results, all the students demonstrated a positive attitude toward FOMT tools, and 5 students used the tools during the writing process in this project. In contrast, the instructors reported divided opinions about such tools for ESL writing purposes. The results showed that instructions can assist students with improving their text quality in terms of coherence and cohesion. As well, based on the results, FOMT can assist the students in constructing their texts during the writing process. The results demonstrated that this assistance can also have a subsequent positive impact on the coherence and cohesion levels in the produced texts.
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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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".