Favorable and Unfavorable Characteristics of EFL Teachers Perceived by University Students of Thailand
Bibliographic record
Abstract
Teachers play pivotal roles in EFL classrooms. Characteristics of EFL teachers may affect students’ attitudes and motivations to language learning. The effective/good characteristics of the EFL teachers perceived by the students have been extensively investigated by the previous research works. However, the perceptions of the students from different backgrounds to EFL teachers may vary in different learning settings. In addition, the research works on both favorable and unfavorable characteristics of EFL teachers are comparatively scarce. This study aimed to investigate the favorable and unfavorable characteristics of the EFL teachers perceived by Thai university students. The data were collected from 6o students at Vongchavalitkul University. Open-ended questionnaires and semi-structured interviews were used as the main instruments for data collection. Useful information about EFL teachers’ personal trait-related characteristics and classroom teaching-related characteristics emerged from the data. The information is very useful and beneficial for the EFL teachers to reflect their personal characteristics and reconsider their classroom teaching, which may be very helpful for them to do some adjustment and preparation in their teaching to achieve better education results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".