Bridging the Gap between Accounting Students and the Profession: A Case Study
Bibliographic record
Abstract
This study addresses the gap between accounting educators and the profession. Research indicates accounting graduates are underprepared for this challenging career. The issue stems from accounting educators who only lecture and accounting students who memorize the information provided in these lectures. Accounting students need opportunities to build their professional skills through learning activities that mimic real-world situations. This qualitative study analyzes the perspectives of advanced accounting students before and after computer-assisted instruction to determine if they believe their critical thinking, communication and problem solving skills have improved. I conducted an analysis using an auditing simulation, semi-structured interviews before and after computer use, and reflective journals provided by advanced accounting students attending Central North Carolina universities. The auditing simulation introduced in the study created the opportunity for students to practice their knowledge of accounting and enhance their professional skills. The findings suggested students made a connection between concepts learned in course work and situations found in the accounting profession. The simulation built confidence in students and motivated them to learn more about enhancing skills necessary for the accounting profession. The evidence recommends accounting educators to use simulations as a supplemental learning tool in preparing accounting students for the profession. This study allows colleges, universities, and state accounting associations and societies to explore and conduct future research in using learning tools to enhance professional skills to bridge the gap between accounting students and the profession.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".