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Record W3133593356 · doi:10.5430/wje.v11n1p42

Does Accounting and Finance Courses Enable Soft Skill Learning? A Mediation Study

2021· article· en· W3133593356 on OpenAlexvenueno aff
Mona Mohamed Elshaabany

Bibliographic record

VenueWorld Journal of Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsSoft skillsCurriculumVocational educationMediationPsychologyMathematics educationAccountingMedical educationPedagogyBusinessSociology

Abstract

fetched live from OpenAlex

Accounting and finance courses are critical to any management programs as they are relevant to other vocational courses. Does these courses help in improving soft skills also? This is a question which is not much probed in literature and is the focus of the study. The objective of the research is to analyze the effect of two independent variables (interest of students and their educational/professional background) on the soft skill learning in accounting and finance courses under the mediating effect of readiness for the course and the technical learnings from the course. The research is based on a survey of respondents from three stakeholders (students, faculty and professionals) and uses regressions to analyze single variable and multi variable mediations. The study found a suppression effect of interest of students on soft skill learning in the presence of technical learning. Also, soft skill learning was found to be positively affected by educational/professional background of students, readiness of students to take the course and technical learning, taken together. The findings of the study would enable business schools to prepare a better curriculum for enhanced learning in accounting and finance courses, which ultimately affects the three stakeholders.

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)0.001
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 score0.335
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.341
Teacher spread0.329 · 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
Published2021
Admission routes1
Has abstractyes

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