Challenges to Effective English Teaching in Primary Schools in Buraydah, Saudi Arabia: Perspectives of English Teachers
Bibliographic record
Abstract
Despite extensive efforts to improve the quality of English language teaching, Saudi students in the local primary schools have a poor level of proficiency in English. Hence, this study aims to examine the barriers to effective English teaching from the perspectives of teachers in the primary schools in Burydah primary schools in Saudi Arabia. 50 teachers in primary schools in Saudi Arabia were recruited through convenience sampling. The study recruited teachers from Buryadah, a city in Saudi Arabia. Self-reported questionnaires with close- and open-ended questions were used to collect rich data. Several teacher-related, student-related, classroom-related, and school-related challenges were reported. Teachers believed that the key barriers to effective English teaching in descending order were the limited ability to use technology, limited technical support to use technology, irrelevant curriculum, lack of training in immersive learning, lack of student motivation, cultural differences among students, overcrowded curriculum, malfunctioning air conditioners, limited engagement at the class, impaired communication skills, limited use of interactive teaching methods, limited teacher training, dull curriculum or unengaging content, limited students’ ability to use technology, large class size, and limited flexibility in adapting the curriculum to the interests and needs of students. There is a need for cooperation among teachers, school headmasters, students, policymakers, and parents to address these barriers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".