Chinese university students’ perceptions of assessment tasks and classroom assessment environment
Bibliographic record
Abstract
Classroom assessment tasks and environment are central to supporting student learning, yet are under-studied at the tertiary level, especially in China’s test-driven culture. This study explores the relationship between students’ perceptions of assessment tasks and classroom assessment environment, within the university context of teaching English as a foreign language (EFL) in China. A questionnaire was designed and administered, based on Dorman & Knightley’s (2006) Perceptions of Assessment Tasks Inventory (PATI) and Alkharusi’s (2011) scale, in order to measure students’ perceptions of the classroom assessment environment. PATI includes five subscales: congruence with planned learning, authenticity, student consultation, transparency, and diversity. Alkharusi’s scale comprises two subscales: learning-oriented classroom assessment environment and performance-oriented classroom assessment environment. Participants were 620 university students from three universities in China. The factor analysis findings identified the original five-factor PATI, and Alkharusi’s two-factor scales within this Chinese research context. Multiple regression analyses exploring the interrelationship showed that congruence with planned learning, authenticity , student consultation , and transparency significantly predicted the learning-oriented classroom assessment environment, explaining 48 % of the variance. Congruence with planned learning and student consultation negatively, and diversity positively, predicted the performance-oriented classroom assessment environment, explaining 12 % of the variance. The findings highlight the two core values in classroom assessment tasks: congruence with planned learning and student consultation in mediating the classroom environment. This study addresses the research gap in our limited understanding of the relationship between classroom assessment tasks and assessment environment, and aids teachers in structuring their day-to-day classroom assessment practices in support of their students’ learning.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".