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Record W4399864571 · doi:10.5430/wjel.v14n5p424

Assessment of Factors that Affect Students’ Business Writing Skills

2024· article· en· W4399864571 on OpenAlexvenueno aff
P. Mathumathi, Vijayakumar Selvaraj, Md. Abdul Momen Sarker, Lamessa Oli, Ajay Prakash, MV Ramesh

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)Computer scienceMathematics educationPsychologyCommunication

Abstract

fetched live from OpenAlex

Writing is a language skill that used for effective communication. The research set out to identify the most common reasons why students struggle with business writing and to provide them with strategies to become better writers overall. Nazareth Art and Science College undergraduates have been the focus of this study, which has employed qualitative and quantitative methodologies to probe the difficulties they face. A total of 106 students from Nazareth College of Art and Science took part in the study. Questionnaire was employed for data collection. The researchers analyzed the survey data using descriptive statistics. The results of this research show that learners had a lot of trouble with their business writing. Business writing can be challenging for students for a variety of reasons, including traditional teaching approach, insufficient practice, lack of motivation and lack of sufficient vocabularies. The study suggests that students' writing could be enhanced with enough practice, the right methods of teaching writing, smaller class sizes, frequent feedback on errors, and 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.161
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.374
Teacher spread0.356 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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