Analysis of Factors Affecting Students’ Business Writing Skills
Bibliographic record
Abstract
Writing skill is one of the four cornerstone skills in the process of learning and using English. The purpose of the study was to investigate the major cause of students’ business writing difficulties and to find out best solution to enhance their business writing skills. This research has utilized both qualitative and quantitative methods to investigate the challenges that undergraduate students at Nazareth Art and Science College. Two hundred Nazareth Art and Science College students participated in this survey. Primary data was gathered by survey questionnaires administered to a random sample of 200 students and classroom observation. Descriptive statistics were used to analyze the survey data. The findings of this study reveal that various hurdles were encountered by the students in their business writing. Challenges such as limited vocabulary, lack of grammar knowledge, lack of practice and traditional teaching method all contribute to the difficulties of students’ business writing. Classroom observation data also reveals that students have trouble with vocabulary, grammar, and practice and teaching method. It is suggested that the current study's scope be broadened to include students from a variety of universities in order to get to the heart of the difficulties associated with business writing. It is anticipated that the current research would persuade university professors to give business writing the same level of consideration as other pedagogical focuses.
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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 | 0.014 |
| 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 | 0.001 |
| 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".