Factors Influencing Students’ Performance in Second Language Writing Skills
Bibliographic record
Abstract
The ability to articulate ideas, convey information, and engage stakeholders through written mediums has become a vital competency. This study delves into the Factors influencing Engineering students’ performance in second language writing skills at two private higher education institutions. The research aims to analyze the issues surrounding students’ writing skills comprehensively. To conduct this investigation, a quantitative methodology was employed. A sample of 247 students was randomly selected, and their opinions were gathered through a survey questionnaire. The findings of the research revealed that the majority of the students struggle to master the art of effective writing. The results show that the majority of students exhibited poor writing performance because of the errors that occurred such as: prepositions, articles, spelling, concord, verb tense, word choice, structure, organization, omission, repetition and cohesion. To enhance their writing skills, students need more chances to practice writing in structured ways. Activities that require crafting complete sentences, experimenting with sentence variation, and incorporating fresh vocabulary will help. In addition, constructive feedback is crucial for pinpointing mistakes and offering strategies for improvement, guiding students toward more polished writing. Reading and writing are deeply connected, so teachers shold increase students' exposure to diverse reading materials which will help them better understand grammar, sentence structure, and vocabulary.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".