Teaching Grammar to Iraqi EFL Students of Al- Hamdaniya University during COVID-19 Pandemic: Problems and Solutions
Bibliographic record
Abstract
During the fight against Covid-19, schools and universities in Iraq and many other countries have been closed and digital learning has begun to take place. In this paper, the researchers have tried to identify the difficulties which faced students through Electronic Learning (hereafter, E-learning) during Covid-19. Inadequate instruction, lack of internet and electricity, little experience and low attendance are just some of the problems that our student face in this type of learning. To assess the benefit of such learning in Iraq, it is hypothesized in this paper that online learning has a bad impact on students’ performance be it spoken or written. To test the validity of the hypothesis, an online questionnaire of (3) items was given to (30) 4th year students of English department to identify the problems and solutions to digital learning from their own perspective. Data was analyzed by using a mixed method (i:e both quantitative and qualtitative) because such method describes and interpretes statistical percentages. The results of the analysis show that the biggest problem for most of the students in particular in our country is that electricity and internet are not available all the time. Another conclusion is that some students personally are not interested in the subject of grammar. It has also been found that the best solution is to go back to classroom teaching or face to face communucation. The study provides some recommendations which can be of benefit to EFL teachers, students and probably to the teaching process in cases of emergency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".