Teaching Styles of Two Native English Speaking Teachers and One Korean English teacher
Bibliographic record
Abstract
In the hope of boosting English communicative competence, during the last decade, every megalopolis and province, in competition, have been hiring native English speaking teachers (NESTs) from English speaking countries. This research, in terms of teaching styles, is intended to cast a light on the gaps between the general preference of students in public elementary schools and the reality of the teaching provided by NESTs in terms of teaching styles, if such gaps exit. The participants were 6th graders from three schools in P City, Gyeonggi Province, South Korea: C, K, and G elementary schools. In C Elementary School, there were 32 participants; in K Elementary School, 39; in G Elementary School, 35. C Elementary School had a male NEST from Canada; K Elementary School, a female from New Zealand; G Elementary School, a male Korean English teacher. The students were asked to answer forty question items (two twenty-item questionnaires) related to their general preferences for English teachers (Type A) and their thoughts on NESTs or the Korean English teacher with regard to their teaching styles (Type B). In each type, five of the twenty items were related to a student-oriented teaching style; five, a subject-oriented teaching style; five, an action-oriented teaching style; five, an institution-oriented teaching style. This study reveals an important conclusion: in subject-oriented teaching style area, only the Korean English teacher meaningfully exceed the students’ general preferences. In other words, in the teaching style, the two NESTs do not satisfy the students’ general preferences. This result might means that the two NESTs have a tendency to treat language forms lightly in the classroom.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".