Exploring K–12 Teachers’ Assessment Literacy and Self-efficacy in China
Bibliographic record
Abstract
Over the past three decades, assessment literacy has become a global priority for teachers, but its overall status in China remains underexplored. Previous research suggested that teachers’ assessment literacy significantly influenced their self-efficacy in assessment. This study used a quantitative survey, including the “Questionnaire of Teacher Assessment Self-Efficacy” and the “Teacher Assessment Literacy Inventory”, to examine this dynamic among 312 teachers from Shanghai, China. Key findings included: (1) most Chinese teachers lacked a fundamental understanding of assessment literacy; (2) assessment literacy significantly impacted teachers’ self-efficacy, particularly in areas related to selecting and developing methods; and (3) secondary school teachers, mathematics teachers, and both novice and highly experienced teachers showed the greatest impact of assessment literacy on self-efficacy. These insights highlighted the need for enhanced professional development in assessment literacy in Asia and offered perspectives for Western educators working with teachers and students from Asia.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".