A phenomenographic study on Chinese EFL teachers’ cognitions of positive and negative educational, social, and psychological consequences of high-stake tests
Bibliographic record
Abstract
This study explores the multifaceted cognitions of Chinese English as a Foreign Language(EFL) teachers concerning the positive and negative consequences of high-stakes tests. The prevalence of high-stakes testing in the Chinese educational system underscores the need to understand teachers' perspectives on the potential impacts of such assessments. The main aim is to phenomenographically investigate the various educational, social, and psychological consequences as perceived by EFL teachers. Additionally, the study aims to identify common themes and variations in teachers' cognitions, shedding light on the complexity of their perceptions. The study employs a qualitative phenomenographic approach and involves in-depth interviews with 30 Chinese EFL teachers. Thematic analysis was utilized to categorize and interpret the identified themes related to positive and negative consequences. The findings reveal a range of cognitions among EFL teachers, delineating positive outcomes, such as enhanced educational quality and recognition, alongside negative repercussions, including narrowed curriculum and increased stress. The axial and basic themes comprehensively understand teachers' perspectives on high-stakes testing. The study summarizes the identified themes and highlights their implications for educational practices and policies. Recognizing both positive and negative consequences emphasizes the nuanced impact of high-stakes tests on EFL teachers. The implications extend to curriculum design, teacher training, and the broader educational landscape, stressing the need for a balanced approach to assessment practices.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".