Does Accounting and Finance Courses Enable Soft Skill Learning? A Mediation Study
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".