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Record W4402244830 · doi:10.5430/wjel.v14n6p629

A Study on Factors Influencing Students’ Business Writing Skills

2024· article· en· W4402244830 on OpenAlexvenueno aff
Ng Miew Luan, Megala Rajendran, M. Sumathy, K. Thomas Alwa Edison, Toong Hai Sam, Ajay Prakash, Lamessa Oli

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationKnowledge managementPsychology

Abstract

fetched live from OpenAlex

Writing is a language skill which is used for communication. The purpose of this study is to identify the most common factors that influence students’ business writing and to provide them effective strategies to improve their business writing skills. Queens College students were the focus of this study. For this study, qualitative and quantitative approaches were employed. A total of 106 students from Queen's College took part in the study. A questionnaire was employed for data collection. The researchers analyzed the survey data using descriptive statistics. The results of this research showed that learners had a lot of difficulties with their business writing. Business writing can be challenging for most students for various of reasons, including traditional teaching approaches, insufficient practice, a lack of motivation, and a lack of sufficient vocabulary. The study suggests that students' business writing skills could be enhanced by having frequent practice, using effective teaching methods, having manageable class sizes, providing frequent feedback on errors, and through encouraging students to shift their perspective on the importance of writing.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.016
GPT teacher head0.329
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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