Bibliographic record
Abstract
Research findings indicate conflicting views as to interference from L1 rhetorical patterns in the essays written by students whose first language is not English. Essays are still considered important for required assignments and exams in institutions of higher learning, but the challenge for L1 Arabic students is to express their ideas clearly. Although there have been studies of the use of L1 in L2 writing, there are very few rigorous ones done on L1 Arabic texts in Lebanon and specifically from the students’ viewpoint. This study aims to evaluate, holistically and analytically, according to language, organization and content, the expository academic essays written by first year university L1 Arabic students and to examine any significant correlation between these scores and the quality of these essays through content analysis. In addition, students’ perceptions of any problems they have in writing the academic essay are surveyed through a questionnaire. Results indicate a significant positive correlation between students’ essay scores and the content analysis. However, findings from the student questionnaire revealed that they do not view any significant interference from L1 nor any significant problems in writing the academic essays which are contrary to the essay scores and content analysis results. Recommendations are made for L2 contexts and future research.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 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 | 0.001 |
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".