EFL Students’ Perception of Classroom Assessment Environment in Translation Courses
Bibliographic record
Abstract
The aim of this study was to explore the students’ perceptions on classroom assessment environment in translation courses. The sample of this study was made of 341 participants studying at an English language department in a Saudi university. Data were collected using self-reported questionnaire which was designed based on Alkharusi’s (2011) scale. Factor analysis was computed and the results revealed the presence of Alkharusi’s two original factors: perceived learning-oriented, and perceived performance-oriented classroom assessment environments. T-test was employed to explore the differences in perceptions between male and female students, but no significance was found between them. Implications and recommendations for classroom assessment as well as for future research have also been discussed. The practical implication of the research is that student outcomes might be improved by establishing classrooms that match those educational environments which have been shown to be associated with students’ learning. A limitation of most classroom learning environment instruments is that they measure an individual student’s perceptions of a whole class, as distinct from students’ perceptions of their own roles in the classroom. It is likely that future classroom and school environment research will be enhanced if personal as well as group assessments are adopted.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".