The Relationship between Language Learners’ Anxiety and Learning Strategy in the CLT Classrooms
Bibliographic record
Abstract
This paper intends to explore how Taiwanese students perceive the relationship between their language learning strategy and anxiety in the foreign language classroom. Due to their previous learning experience, most of the participants hold an unfavorable attitude toward a grammar-translation teaching approach. Consequently, learner-centered instruction has been widely accepted and acknowledged as a welcome concept and feasible teaching approach in the English Foreign Language (EFL) context. To improve the proficiency of language learners in EFL classrooms, it is very important to take into account the need of the learners. The present study utilizes Foreign Language Classroom Anxiety Scale (FLCAS) and Communicative Language Teaching Attitude Scale (COLTAS) to examine the participants’ perceptions about learning English. The results indicate that most of the participants express a favorable attitude toward the Communicative Language Teaching (CLT) approach; however, they also reveal their high level of anxiety in the language classroom. Language anxiety is usually reported to have adverse effects on the learning of a second language. It is the language instructors’ mission to accelerate the language learning of their students. One way is to teach students how to learn more effectively and efficiently. Language learning strategies (LLS) are procedures that learners can use to facilitate learning. Both teachers and students should develop an awareness of the learning process and strategies that lead to success. The ultimate goal of this paper is to analyze the factors that affect the participants’ learning strategies and their language anxiety, and offer some pedagogical suggestions.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".