Examining Language Assessment Literacy for Saudi Pre-service EFL Teachers
Bibliographic record
Abstract
Teacher assessment skills include knowledge of assessment methods and techniques, the ability to interpret assessment results, and the ability to use assessment to tailor teaching strategies to student needs. The present study examines non-native Teaching English as a Foreign Language (TEFL) pre-service teachers' awareness of students’ assessment. The study also investigates whether studying a course in assessment significantly impacts pre-service teachers' assessment literacy. To answer the research questions, an online test was administered to 52 pre-service teachers who are seniors at Majmaah University. The collected data were analyzed descriptively and inferentially with the aid of IBM SPSS version 22. The results revealed that non-native pre-service TEFL teachers have a low level of assessment literacy. Despite the overall weak performance in the test, the participants showed strength in some of the standards for teachers’ competence. They revealed a high capability of choosing and developing appropriate assessment methods. On the other hand, pre-service teachers possessed little awareness about their ability to implement students’ test results to adjust the curriculum or make better instructional decisions. Based on the study results, institutions may consider adding more assessment courses to the TEFL study plan or adopting some of the existing curricula and extending it to include all the assessment skills needed for creating qualified teachers. Designing a careful professional development training program for EFL faculty members is another important recommendation of the present study. Finally, future research in the field is needed for planning and implementing training programs to improve teachers’ assessment skills.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".