Analyzing Foreign Language Test Anxiety among High School Students in an EFL Context (Note 1)
Bibliographic record
Abstract
With the increasing need to learn languages as a result of globalization there is a great demand on the part of the learners to communicate in a second/foreign language, which is also supported downwards by the governments and upwards by the parents. Among the many aspects of foreign language learning, affective factors are researched a lot as they are dependent on contexts, individual differences, cultural background, teaching methodology etc., which cause a variation in the results. The current research focuses on test anxiety as one of the major affective factors. Thus it aims to identify the level of test anxiety and its relationship with gender, grade level, and academic achievement. Moreover, the causes of test anxiety were investigated according to students’ own perceptions. A test anxiety scale and semi-structured interviews were conducted to gather the qualitative and quantitative data. The overall results showed that the participants had a moderate level of test anxiety. Females were found to be more anxious than males only in some aspects; low achievement scores provoked test anxiety with regard to a few items, and 9th graders were found to be more anxious than the 10th graders. According to participants’ own perceptions, test validity, time limit, teacher attitudes, test techniques, proctors, length of the test, testing environment and clarity of test instructions were the causes of test anxiety.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".