Understanding pre-adolescent learners’ foreign language enjoyment: a mixed-methods study on Chinese primary school students of English
Bibliographic record
Abstract
The present study investigated the overall level and the underlying factors of pre-adolescent learners’ foreign language enjoyment. To this end, 277 Chinese primary school students in Years 3–6 completed a foreign language enjoyment scale and a questionnaire assessing nine student- and teacher-centered variables (Participants’ age was obtained from their parents or guardians). They also answered an open-ended question designed to elicit the causes of foreign language enjoyment. The quantitative results showed that the participants’ foreign language enjoyment was generally moderate-high. Boys and girls did not significantly differ in this emotion. Attitudes towards the foreign language, attitudes towards the foreign language teacher, and perceived relative standing among peers in foreign language proficiency significantly positively predicted foreign language enjoyment in descending order of magnitude, followed by age as a significant negative predictor. Teacher friendliness, joking, strictness, and predictability, and the teacher's foreign language use in class did not significantly predict foreign language enjoyment. The qualitative analysis revealed that foreign language enjoyment was linked to teacher- and peer-centered variables, classroom activities, perceived foreign language mastery, perceptions of learning tasks and materials, test result, classroom performance, personal affective states, and classroom atmosphere. The findings and their implications are discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".