The Effectiveness of Oral Assessment Techniques Used in EFL Classrooms in Saudi Arabia From Students and Teachers Point of View
Bibliographic record
Abstract
Assessing learners’ oral skills are considered as a crucial process in most EFL teaching and learning programs. However, it can be challenging for teachers to make a valid, reliable, and fair assessment. This study aimed to investigate Saudi college students’ and teachers’ point of views toward the effectiveness of oral assessment techniques used to assess learners speaking-skills in the EFL classroom. Two different questionnaires were administered to 12 EFL teachers and forty-two students’ who are majoring in English at the Languages and Translation College at King Saud University. Both quantitative and qualitative data were collected from respondents, treated statistically, analyzed and revealed in the following sections. The findings of the study revealed that EFL teachers are using a variety of communicative oral assessment techniques and are utilizing effective assessment procedures in assessing their students’ speaking skills. For students, the results revealed that students are generally satisfied with the assessment techniques and procedures that, teachers use in assessing their language performance. Recommendations and suggestions are offered for all concerned parties.
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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 | 0.007 | 0.019 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".