The Common Use of Connecting Ideas in Writing Paragraphs of Two Saudi Contexts
Bibliographic record
Abstract
This study investigated the undergraduate use of specific English coordinating and subordinating conjunctions based on Azar’s & Kolln’s and Funk’s textbooks, which is (and, but, or, so, because). Also, this research intended to compare students who studied in two EFL contexts to explore how the given instructions regarding the use of conjunctions were applied in students’ writing. The data was collected from 26 students studying in two different Saudi universities. First, the data was gathered from (group one), which consisted of 13 participants, who studied at a university in the north-central part of Saudi Arabia, and their major was English Language. However, the second group was inclusive of 13 participants who studied at the Applied Linguistics Department at a university in the middle of Saudi Arabia. The participants were asked to write two to three paragraphs using specific coordinating and subordinating conjunctions. Thus, the researcher collected 26 sampled data containing 13 written paragraphs from each context to compare the participants of the two contexts in the use of the learned conjunctions. The data were analyzed based on the introduced coordinating and subordinating conjunctions in Azar’s & Kolln’s and Funk’s textbooks. The analysis was accomplished by identifying the frequent occurrence of those conjunctions in students’ written data and how those conjunctions were used in joining two or more clauses. The results showed that there were no significant differences in students’ use of those conjunctions, and several participants had challenges in recognizing the function of those simple or common conjunctions. It has been found that those participants of the two contexts had committed the same types of errors when trying to combine their clauses.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".