Chinese Senior High EFL Learners’ Foreign Language Reading Anxiety: Profile and Sources
Bibliographic record
Abstract
In comparison with other skill-specific foreign language anxiety, foreign language reading anxiety (FLRA) was a less-researched realm. The purpose of the study was to investigate the general profile and possible sources of FLRA in the under-explored Chinese senior high EFL students. The 60 participants were from 2 high schools in China. The study employed the “explanatory sequential mixed method design” (Creswell & Plano Clark, 2018). First, the quantitative data were collected via the adapted Foreign Language Reading Anxiety Scale (FLRAS) (Lu & Liu, M., 2015), and analyzed with SPSS. Then, based on participants’ scores on FLRAS (adapted), specific responses, and consent to further interviews, 4 subjects were selected as sources of qualitative data. The conclusions were drawn that (1) more than half the learners (78.33%) were generally exposed to little FLRA (M=2.57); (2) possible FLRA sources could be divided into 4 main categories, with overall 16 subsets: (a) individual factors (reading interest, self-expectation, reading strategy use, background knowledge); (b) textual factors (topic, task type, text length, tested vocabulary, grammar, text structure, rhetoric); (c) instructional factors (teaching method, evaluation); (d) situational factors (teacher-student dynamic, parental anticipation, peer pressure). Despite limitations such as limited sample size and scope, absence of further validation testing, and neglect of examination of background variables, the study purveyed valuable suggestions for language educators to enhance strategies addressing FLRA among Chinese EFL senior high learners. These suggestions included considering the impact of text structure and rhetorical devices on FLRA, prioritizing vocabulary instruction, and implementing anxiety-reducing interventions, e.g. the “flipped classroom model” (Gök et al. 2021). Furthermore, the study emphasized the need for further research on the more representative FLRA profile of Chinese young EFL learners, statistical examination of the relevance of various sources to FLRA, investigation of the relationship between background variables and FLRA among Chinese senior high EFL learners, and exploration of the universality and language-specific nature of different FLRA sources.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".