Machine Translation in Foreign Language Learning Classroom-Learners’ Indiscriminate Use or Instructors’ Discriminate Stance
Bibliographic record
Abstract
The use of machine translation (MT) tools in language learning classroom is now omnipresent, which raises a dilemma for instructors because of two issues, language proficiency and academic integrity, caused by that fact. However, with the unstoppable development and irresistible use of MT in language learning, rather than entangling with using it or banning it, it is more significant to figure out why learners turn to MT in spite of the prohibition from their instructors and how can instructors guide learners to use it appropriately. Consequently, this paper reviews articles with regard to the reason why learners turn to MT, the practical use of MT in learners’ writing, and some pedagogical solutions for making peace with MT in language learning classroom respectively. Implications can be garnered like that a course for learners of how to use MT tools properly should be included in the curriculum design, and simultaneously, the holistic understanding of these overwhelmingly fast-developed technology tools for instructors should be a part of teachers’ self-development, since instructors without knowledge said technology tools can not fully motivate language learners and implement the pedagogical solutions offered.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".