Exploring and Enhancing Comprehension of Elliptical Constructions in Students’ Academic Writing: A Case Study
Bibliographic record
Abstract
This study explores and enhances the comprehension and use of elliptical constructions in academic writing among EFL students at the Modern College of Business and Science (MCBS). Elliptical constructions, linguistic phenomena where certain words or phrases are intentionally omitted to avoid redundancy, present unique challenges in academic writing contexts. The study, involving 50 students and six teachers from three institutions, employs a Quantitative Content Analysis method and a questionnaire based on the Likert scale and open-ended questions. The research aims to enhance students’ comprehension and use of elliptical constructions, with two specific objectives: to assess the level of awareness among students regarding the use of elliptical constructions in their written work, and to formulate strategies to improve students’ comprehension and application of elliptical constructions in academic writing. The findings reveal varying degrees of awareness and usage among students, with those scoring higher demonstrating noticeable competency. However, teachers’ opinions on effective strategies diverged, indicating a need for more focused strategies. The study suggests that effective teaching strategies should consider the diverse methods employed by teachers and emphasize real-world applications, interactive learning, and personalized feedback. This approach is anticipated to enhance students’ understanding and use of elliptical constructions in their academic writing.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".