A Grey Relational Analysis between Some Selected Affective Factors and English Test Performance
Bibliographic record
Abstract
With Grey relational analysis (GRA), the paper examined the sensitivity of eight variables influencing test performance at branch level. The eight variables involved included language proficiency, test anxiety, self-esteem, achievement motivation, achievement goal, English proficiency test anxiety (EPTA), state anxiety and trait anxiety. Results showed that: (1) Besides language proficiency, motivation-to-avoid- failure was most sensitive to English test performance; (2) Trait and state anxiety were more sensitive to English academic performance than general test anxiety as well as the four branches of the EPTA; (3) Compared with the other three branches of the EPTA, EPTAS-listening was most intimate to English test performance; (4) All the affective concepts involved in this study were quite sensitive to English academic performance at branch level because their comprehensive grey correlation degree values were all over 0.50. This research has practical implications for English teachers and students seeking to enhance their performance in English proficiency test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".