Breaking Language Barriers: The Power of Machine Translation in Online Learning
Bibliographic record
Abstract
This research explored the potential of machine translation (MT) in enhancing the accessibility and inclusivity of online learning platforms, with Coursera serving as a case study. The study compared the performance of courses translated by humans (HT) to those translated by machines (MT) with a toggle feature allowing access back to the original content. The key metrics used were course completion rates and star ratings. The findings reveal that MT courses with the toggle feature have a 2.3 % higher course completion rate than standalone HT versions. Furthermore, MT courses have a higher likelihood of receiving a 5-star rating (88%) compared to HT courses (84%). These results consider potential confounding factors such as course pair fixed effects, product line, and learner tenure. Future research could involve experiments that randomly assign learners to different course versions or explore the potential of human translation with the toggle functionality to the original content. The findings have significant implications for practitioners in education and future research, highlighting the potential of MT in enhancing accessibility and inclusivity in online higher education.
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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 | 0.047 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".