EFL Learners’ Affect, Engagement, Misbehaviours, and Achievement: A Classroom Observation Perspective
Bibliographic record
Abstract
Most previous research examining the correlation between affect and achievement of learners of English as a foreign language (EFL) has relied on questionnaire and/or interview data. The current study, conducted in a Chinese EFL context, chose to explore this relationship on the basis of quantitative and qualitative classroom observation data, with a special focus on learners’ classroom engagement and misbehaviours. The participant sample involved the EFL learners and teachers in six classes at a key and a non-key university in Northwest China. Data in relation to participants’ affect, engagement, and misbehaviours were collected via classroom observations, including some video-recording. The participating students’ College English Test-Band 4 (CET-4) scores were used as a measurement of EFL achievement. Participants’ affect, engagement, and achievement formed a reciprocal relationship; the latter was negatively connected with misbehavious. Rather than gender, type of school (key vs. non-key university) had significant effects on the variables being examined. Data revealed that teachers, peers, and classroom environment were also influential factors in explaining the differences in the relationship between the identified variables.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".