A Case Study of Question Formations of the Saudi EFL Learners at Bisha University
Bibliographic record
Abstract
<p>This paper has studied the question formation techniques used by the Saudi students at Bisha University. It addresses the problems faced by the students in forming questions in English. The study has identified that a large number of the students suffer from the lack of proper grammar rules in forming various types of interrogative sentences and also from the intense mother tongue interferences. The research has attempted to discover the degree of the students’ difficultness/difficulties in forming questions and analyses the various types of their problems. The paper has also correlated the problems in the area, and the syllabus, the materials, tasks, and methodology prescribed. To achieve this objective, a questionnaire based survey has been used as a research tool to obtain data from both the girls’ and boys’ colleges of the University of Bisha. The survey comprised students’ questionnaire and the tests based on Wh- questions and yes-no question formations in English. The survey has identified that most of the students had problems in forming interrogative sentences due to the mother tongue interferences. The study has also highlighted a few major problems, for instances, the syllabus is indifferent to the needs of the students, and not enough emphasis is done specifically on the English question formation aspect of the grammar. The author concluded the study with the hope that the educationalists and other stakeholders realize that no course is fruitful unless: 1). It is interesting, 2). It effects a progressive change in the ability level of the learners, and 3). It helps the students to use their potentiality to the optimum level.</p>
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".