Actual Usage of Machine Translation by Japanese University Students and Verification of Test Results
Bibliographic record
Abstract
The objective of this study is to investigate the actual situation of Japanese university students' use of machine translation (MT). The case study focuses on Japanese university students and not only investigates when students use MT, but also examines how their attitudes change before and after they use MT for their assignments. In this study, Google Translate was used as the MT tool, and Microsoft Excel was used for analysis. By analyzing these results, it was found that when students were allowed to use MT, they themselves decided whether or not to use it depending on their task. Of the skills in writing, reading and listening, it is also found that students tend to use MT the most for writing tasks and the least for listening tasks. In addition, no statistical significance of using MT was found for any of these skills, indicating that the use of MT does not necessarily mean that all language-related questions can be solved. These results could provide valuable data for the future introduction of MT into education. The survey included a diverse range of university students selected through an open application process. However, the sample size was limited, so an extensive survey should be undertaken in the future.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".