Effective Teaching Strategies to Eliminate Spelling Problems Among Saudi English Language Undergraduates
Bibliographic record
Abstract
Saudi university students who learn English as a foreign language face multiple difficulties during the process of learning especially while mastering writing skill and its component (spelling). This paper aims to explore the most recommended teaching strategies to eliminate the Saudi university students’ spelling errors. The research participants were 15 students in English Language Department at Tabuk University and 15 English language lecturers from the same department. Group structured interviews were designed for the lecturers and students. The findings reveal that, there are different effective teaching strategies to master English spelling such as, practicing spelling, lecturers’ pedagogical practices and Learners’ Engagement. This paper concludes that, the spelling problems of EFL learners could be addressed by a variety of intervention strategies such as, instructors should be introduced to a range of teaching methods such as simulation situations where they can experience problems arising from poor spelling and roleplaying .Students should also be encouraged to engage in the learning process by setting tasks like, learning the spelling of a few selected words, which they can test each other on in pairs class and so evaluate their own and their peer’s work. This paper hoped that, the findings revealed in this study will help the policymakers in taking necessary actions in improving the learning experience of Arab learners of English.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".