Students of Different Subjects Have Different Levels of Extrinsic and Intrinsic Motivation to Learn English: Two Different Groups of EFL Students in Japan
Bibliographic record
Abstract
Here is documented an investigation to assess the motivational drivers of a group of Japanese, first-year, dental-university students taking part in compulsory EFL classes and to compare those motivational drivers with an investigation into the motivational drivers of a group of Japanese IT students. There was a clear difference between extrinsic and intrinsic motivational drivers between the two groups. It was discovered that dental students valued English much less for work related reasons (intrinsic) and more for personal reasons (extrinsic), and that overall they had a more favourable attitude to their EFL studies. It was demonstrated that for this group of dental students the importance of English for dentists at work and in research needs to be emphasized in lessons and that students have a favourable attitude to using English and would be happy to have more communication-based exercises in class. This work is the first documented evidence of students of different subjects having different motivations. It is important to the wider teaching community as there are few comparisons of motivation in the literature, and the investigation presented here clearly demonstrates that what motivates one group of students does not necessarily motivate another group, and quite probably, the fingerprint of motivational drivers is quite different for students of different subjects.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".