Levels of Enjoyment in Class Are Closely Related to Improved English Proficiency
Bibliographic record
Abstract
This study investigated the factors, such as students’ foreign language enjoyment, their foreign language classroom anxiety, the teacher’s language choice (English-only instruction versus English instruction with limited legitimate Japanese support), and demographics that affect improvement in English proficiency in EFL communicative classes. Ninety-three college students in Japan participated in this research, for which they completed a questionnaire after the seventh lesson of 14 total lessons during a semester. In addition to t-tests, a stepwise multiple linear regression analysis was performed using the dependent variable of the change in every student’s scores between the midterm and the final examination and six independent variables: age, gender, nationality, the teacher’s language choice, anxiety levels, and enjoyment levels. The results reveal that higher levels of enjoyment in class are significantly correlated with improvement in students’ English proficiency. The present study suggests that enjoyment levels, instead of anxiety levels, affect improvement in students’ English proficiency regardless of teachers’ language choices. Therefore, it is important for teachers to provide classes that are enjoyable for students by motivating and providing the students with activities in which they will succeed, thereby ensuring their self-confidence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".