What Makes an Effective English Language Teacher? The Life Histories of 13 Mexican University Students
Bibliographic record
Abstract
This study examined the educational life histories of 13 students at a Mexican university in order to gather their perspectives of effective language teaching. Most previous studies on students’ perspectives of language teaching have used quantitative and deductive methods, whereas this study employed qualitative and inductive methods. The main methodological approach was the ‘life history’ approach, and the specific methods were two extended interviews and an innovative ‘timeline’ activity. In total, 77 examples of effective (and ineffective) teachers emerged from the 13 students’ life histories. The study revealed three major findings. Firstly, teachers’ language knowledge and proficiency were not mentioned as important characteristics of effective language teaching, although several students did make reference to teachers’ command of language when it was perceived to be missing. Secondly, students generally favoured more ‘modern’ approaches (engaging, active, real-life skills, immersion in the target language), as opposed to more ‘conservative’ approaches (unappealing, passive, overly theoretical, lack of immersion in the target language). Thirdly, students emphasised the importance of a positive student-teacher relationship, and greatly appreciated the teacher being there to provide them with personalised attention. Notably, the students tended not to value autonomous learning, preferring teachers to be close to them to help them with their problems in class. Two main implications for practice were suggested. Firstly, a general consensus has been reached regarding several key characteristics of effective language teaching, strengthening the argument that these characteristics should be listened to, and acted upon, by teachers and educational decision-makers. Secondly, the study makes a strong case for future research to utilise more qualitative, inductive methods when investigating students’ perspectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".