Factors Causing Learners' Anxiety in FL Speaking: The Case of GFP Learners at UTAS, Nizwa, Oman
Bibliographic record
Abstract
Research proved that anxiety plays an essential role in foreign language speaking, especially when it comes to speaking in front of others. Learners in the department of English always show a negative attitude in speaking presentations and tasks in full view of people. The aim of this paper is to investigate the factors that contribute to learners' anxiety in foreign language speaking from learners' and teachers' perspectives. The study aims to answer the following questions: what are the factors that cause learners' speaking anxiety and what types of anxiety do students have, and what are some strategies to lower it? The study was conducted through mixed method research to achieve the purposes of both qualitative and quantitative methods. The subjects of the study were 240 students and ten teachers in the foundation program. These students are studying at different levels from 1 to 4 to be prepared for the academic programs, which are business, IT and engineering. The research data was collected through a questionnaire for learners and an interview with teachers. The questionnaire is based mainly on Horwitz's scale, which is always considered the best scale to measure anxiety and anxiety indicators. The data revealed that most of the students face all types of anxiety in speaking, including communication apprehension, fear of negative evaluation and speaking test anxiety. From the teachers' perspective, anxiety was also related to the syllabus itself and the types of tasks given. To solve the issue of anxiety, the self-system model of motivation was implemented with a group of students giving them speaking tasks using the motivational model of Hadfield and Dornyei (2014), where we adopted some tasks to make a unit of teaching relying on Willis' Task-based framework where we added the language focus to each task.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".