Affective factors in Foreign Language Teaching: Enlightenment From Dead Poets Society
Bibliographic record
Abstract
The focus of foreign language teaching has been changed from the teacher-centered model to the student-centered model. The traditional duck-feeding model of teaching cannot meet the requirements of the new era for it fails to activate students’ affect in learning. Since students’ affective factors exert a great impact on foreign language teaching, how to effectively motivate students becomes our primary focus. This study aims to comprehensively investigate how affective factors may influence foreign language learning by taking American film Dead Poets Society as an example. Besides, it intends to provide some pedagogical implications for educators by analyzing Keating’s teaching mode in Dead Poets Society . In the film, Keating is dedicated to the cultivation of the students’ independent thinking and innovative ability over the teaching process. Through affective interaction with students, Keating finds a suitable way to achieve their self-actualization. According to Maslow’s hierarchy of needs, Krashen’s affective filter hypothesis, and non-intelligence theory, Keating’s teaching mode can effectively help students build up self-confidence and seek their self -actualization. As is generally recognized that affective factors like motivation, self-confidence, anxiety and inhibition play important roles in language learning. Keating’s success can have some enlightenment for foreign language teaching as well. We hold that foreign language teachers should motivate students to seek self-actualization, relieve their anxiety and build up their self-confidence, love and respect them, which can contribute to enhancing of teaching effects ultimately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".