Investigating Saudi University EFL Teachers’ Assessment Literacy: Theory and Practice
Bibliographic record
Abstract
Teacher assessment literacy (TAL) is believed to have positive impact on student learning outcomes. Therefore, attempts are made, especially, in advanced educational contexts to increase TAL. In the context of Saudi higher education, available empirical evidence indicates that EFL teacher assessment literacy is replete with loopholes. This mixed-method research investigated Saudi EFL teachers’ construction of assessment tasks, the influence the tasks had on students’ learning and the extent to which teachers’ assessment practices were in alignment with recommended assessment practices. The data were collected through analyzing teachers’ summative assessment tasks and a student survey with both close and open-ended questions. Apart from the participants’ responses to the open-ended questions of the survey, the data went through quantitative data analysis for frequencies and percentages. The findings revealed a serious incongruity between teachers’ assessment tasks and course learning outcomes. For instance, higher order learning outcomes were not assessed at all. Most of the tasks were selected-response questions (SRQs). As confirmed by the survey data, the assessment tasks mainly triggered memorization as a learning strategy. Therefore, suggestions are made that university teachers’ professional development with particular focus on their assessment literacy is placed at the center of higher education policies. Without valid assessment in place, the edifice of Saudi (higher) education system may lose its efficacy.
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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".