The Enlightenment of Affective Filter Hypothesis and Risk-Taking on English Learning
Bibliographic record
Abstract
Affective filter hypothesis reveals that learners with different emotional learning attitudes have different filter capability for language learning input. Learners with positive emotional attitude have a low filter of language learning input, while learners with negative emotional attitudes have a high filter of language learning input. Risk-taking means that the learner dares to take risks. He is not afraid of making mistakes and the unknown situation. The risk taker will seize every opportunity to use learned knowledge into practice, which is a positive emotional attitude. In the meantime, the adventurous students’ affective filter is relatively low. In actual classroom, teachers usually pay little attention to the emotional state of students, and teachers rarely realize the impact of motivation, self-confidence, anxiety, and risk taking on students’ English learning. More attention is still paid to the training of students’ basic skills. Therefore, this paper first explains the connotation of the affective filter hypothesis and risk-taking. After analyzing and explaining the actual teaching situation and the current situation of students, four suggestions are put forward from the perspectives of teachers and students. Among them, the first three suggestions are for teachers, and the last one is for students. The first suggestion is about teaching methods. Teachers should get rid of the obstacles of traditional teaching methods, use multimedia and other technology to assist teaching and give students more opportunities to speak in class. The second suggestion is about teaching atmosphere. Creating a relaxation and pleasant classroom environment is conducive to reducing students’ anxiety and good for students to take risks. The third suggestion is about self-confidence. In order to build students’ confidence in English learning, teachers should encourage students, discover the highlights of each student and praise students when they make progress. The fourth suggestion is about the significance of affective factors. Students should recognize the role of emotional factors in English learning and adopt effective methods to self-regulate.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".