Zone of Proximal Development: Investigating the Most Usage Conjunctions and the Common Issues Written by EFL Students at Paragraph Levels
Bibliographic record
Abstract
This qualitative paper covered an in-depth investigation of using different types of conjunctions taking into consideration their meaning and functions. To investigate the common issues of using conjunctions and exploring the most types of conjunctions that the participants applied, ZPD was framed to develop the study. The participants were undergraduates who were studying at one of the Saudi universities. They were selected from level one who enrolled in the Grammar 1 course. The sample of the study was chosen randomly. They were divided into two groups, which were Group one and Group two. Both received the same instructions from the same instructor in the class. The difference was that group one had an opportunity to use their textbook and were allowed to discuss and receive help from their partners. Whereas, group two did not receive any help; they were supposed to structure their written texts individually. For this reason, the zone of proximal development theory was selected as a framework. The findings of the study highlighted the participants’ issues in using conjunctions, including fragment sentences, creating too-long sentences with unclear messages, and failing to use punctuations with conjunctions. Further, the results listed the conjunctions that each group used. Group two only used three familiar conjunctions, which were And, But, and Because. However, group one was better at using various conjunctions because they tried to use more types, such as And, Or, So, But, and Because. Thus, applying cooperative learning and scaffolding raised the chance of using student-centered methods in grammar classrooms.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".