Saudi EFL Primary School Teachers’ and Parents’ Perceptions of Online Assessment During COVID-19 Pandemic
Bibliographic record
Abstract
Since March 2020, the world has been impacted by the COVID-19 pandemic, which resulted in sudden school closures and a rapid transition from traditional face-to-face education to a new model of online learning and assessment. The present study analyzes two cohorts—primary-school EFL teachers and students’ parents—regarding the perceptions they have had and the challenges they have faced when assessing young EFL learners online during COVID-19. A specific aim of the study is to identify primary-school EFL teachers’ perceptions of the online methods used in assessing young EFL learners in the Riyadh region of Saudi Arabia. The research follows a quantitative method involving a convenience-sampling method for the selection of the study’s participants. A total of 34 primary-school EFL teachers and 20 parents of young learners who are studying online in primary public schools were the main participants of the study. The researcher used a survey-based method involving a five-point Likert scale to collect data from the participants. The surveys were distributed online via the social-media application WhatsApp. The statistically analyzed responses yielded two types of descriptive statistics: frequencies, and percentages. The results show that both the teachers and the parents perceived online assessments as more convenient, fun and interactive than traditional paper-based assessments. Furthermore, both the teachers and the parents associated online assessments with serious challenges, such as cheating and technical problems. And teachers held positive views of various online methods and techniques for the assessment of young EFL learners.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".