Types of Foreign Language Anxiety (FLA) Through Belief About Language Learning Inventory (BALLI): A Thematic Analysis
Bibliographic record
Abstract
This research uncovers the types of Foreign Language Anxiety (FLA) proposed by Horwitz, Elaine K., Horwitz, Michael B., & Joann Cope (1986) felt by students. This research uses instrument of Belief About Language Learning Inventory (BALLI) proposed by Horwitz, Elaine K. (1988) in the form of questionnaire using Guttman scale. It is narrative design of qualitative research. Furthermore, the analysis is completed by thematic analysis passing five steps to draw a conclusion correlated with the respondents’ respond to the questionnaire. The respondents of this research are the university students of Economic Department, Universitas Islam Sumatera Utara (UISU), Indonesia, Academic Year 2022-2023. The research result found that the highest percentage of people who are anxious about language learning is 64%. It focuses on test anxiety, specifically fear of making mistakes, followed by fear of negative evaluation, specifically fear of receiving a low grade (63%). Then it is on test anxiety, specifically feeling inferior (57%) followed by fear of negative evaluation, specifically fear of correction (49%). Finally, communication anxiety: lack of self-confidence ranks fifth (43%) followed by fear of speaking (35%). Because the percentage of test anxiety and fear of negative evaluation is higher than that of communication skill, the researchers conclude that what makes students anxious in learning foreign language (English) is the grammar or structure of the language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".