Factors in Becoming an Emotionally Positive English User in University Freshman Classes
Bibliographic record
Abstract
English education in Japan has recently been changing to focus on communication skills. The purpose of this research was to identify how the emotional (foreign language enjoyment and foreign language classroom anxiety) and psychological (motivation and self-confidence) factors might differentially stimulate students’ attainment of higher English proficiency in student-centered communicative lessons. The classes included pair/group work with a point-addition system. A questionnaire was filled in by 108 EFL freshmen. A multiple linear regression analysis was calculated, and the results of the questionnaire exhibited that the students who had stronger motivation, self-confidence, and enjoyment could expect to receive higher TOEIC IP scores. The students' essay reports showed that the point-addition system introduced during the research might be a culprit for increasing the anxiety of even students with high English proficiency. Along with devising ways to lower students' anxiety (e.g. not using a stressful point-addition system), teachers are advised to use teaching methods that promote students’ positive emotions (FLE) that create more self-confidence and motivation through more communicative EFL activities.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".