The Reality of Continuous Assessment Strategies on Saudi Students' Performance at University Level
Bibliographic record
Abstract
This study was carried out to explore the assessment practice in Saudi universities with the major focus on Continuous Assessment Strategies. The study specifically aimed to find out the different assessment strategies and their contribution to students’ learning and performance. It was conducted in Jubail University College and Al Mustaqbal University involving a sample of one hundred and fifty students and forty five teachers. Data was collected qualitatively and quantitatively using students’ academic records and questionnaires. From the findings, it was observed that the use of continuous assessment helps students to understand difficult areas related to the EFL content. The study revealed that although there are various kinds of continuous assessment strategies, EFL teachers at both the universities generally employed take-home assignments, written tests and recap exercises to assess how the EFL students learn English language. Continuous assessment also provides more confidence to students and prepares them for final examinations. Therefore, it was recommended that the competent EFL teachers, who are trained and knowledgeable about evaluation strategies and procedures, should be motivated to share their expertise in order to improve the performance of Saudi EFL university students.
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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 | 0.006 | 0.032 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".