Motivation and Test Anxiety in Test Performance Across Three Testing Contexts: The <scp>CAEL</scp>,<scp> CET</scp>, and <scp>GEPT</scp>
Bibliographic record
Abstract
This study examined test‐takers' motivation, test anxiety, and test performance across a range of social and educational contexts in three high‐stakes language tests: the Canadian Academic English Language (CAEL) Assessment in Canada, the College English Test (CET) in the People's Republic of China, and the General English Proficiency Test (GEPT) in Taiwan. The researchers issued a questionnaire exploring motivation, test anxiety, and perceptions of test importance and purpose to test‐takers in each of the three contexts. A total of 1,281 valid questionnaire responses were obtained: 255 from CAEL, 493 from CET, and 533 from GEPT. Questionnaire responses were linked to each test‐taker's respective test performance. The results illustrate complex interrelationships of test‐takers' motivation and test anxiety in their test performance. Differences in motivation and test anxiety emerged with regard to social variables (i.e., test importance to stakeholders and test purposes). Further, motivation and test anxiety, along with personal variables (i.e., gender and age), were associated with test performance. Given that motivation and test anxiety have typically been examined separately and in relation to a single testing context, this study addresses an important research gap.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".