Teachers’ Grading Decision Making: Multiple Influencing Factors and Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated Chinese secondary school English language teachers’ grading decision making, focusing on the factors they considered and types of assessment they used for grading. A questionnaire was issued to 350 secondary school English language teachers in China. Descriptive analyses of the questionnaire data showed that these teachers of English considered achievement and non-achievement factors in grading, placing greater weight on non-achievement factors, such as effort, homework, and study habits, and that they used multiple types of assessment, including performance and project-based assessment, teacher self-developed assessment, as well as paper and pencil tests for grading. MANOVA results suggested that both internal and external factors, such as the grade level teachers teach, the assessment training they have received, and their class size affect different aspects of their grading decision making. Multiple regression results further showed a significant relationship between the factors teachers considered and the types of assessment they used for grading. This study contributes to the understanding of the classroom English language teachers’ grading decision making in general and especially in the Chinese context and has significant implications for teacher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it