Investigating classroom dynamics in Japanese university EFL classrooms
Bibliographic record
Abstract
Since 1868 to the present day, the Ministry of Education, Sports, Science and Culture (MEXT) has implemented many reforms to enhance English education in Japanese universities. However, much still remains to be done to improve the situation and one of the biggest hurdles is the fact that there are many unmotivated students in Japanese university EFL classrooms. This thesis explores the reasons for this problem by focusing on inter- and intra-relations between teachers and students in this context. Data were collected through classroom observations, interviews and questionnaires. The study employs both qualitative and quantitative research methodologies and uses space and methodological triangulation in order to overcome parochialism. My conclusions are that: 1) Visible and invisible inter-member relations exist between members of university classes and their teachers; 2) The teacher's behaviour affects the students' behaviour and impacts on their learning; and 3) Cooperative learning has a positive influence on language acquisition; 4) Japanese university students may not perceive how little interaction they have with their teacher; 5) Students exhibit gender differences in terms of the types of problems encountered and the ways in which they deal with them, but some problems are dealt with negatively by female and male students alike; and 6) Teachers appear not to perceive the problems and when they do they often deal with them by using negative strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".