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Record W4392102242 · doi:10.5430/wjel.v14n3p43

Analysis of Factors Affecting Students’ Business Writing Skills

2024· article· en· W4392102242 on OpenAlexvenueno aff
R. Shruthi, Ramesh Kumar, K. Abinaya, A. Rajeswari, K. Savitha

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.351
Teacher spread0.339 · 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 designQualitative
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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