Competency‐Based Education and Assessment for the Accounting Profession: A Critical Review*
Bibliographic record
Abstract
ABSTRACT In recent years many professional accounting associations have become interested in establishing competency‐based professional requirements and assessment methods for certifying accounting professionals. A competency‐based approach to qualification specifies expectations in terms of outcomes, or what an individual can accomplish, rather than in terms of an individual's knowledge or capabilities. This idea has an obvious appeal to many practitioners and administrators of professional qualification programs. However, there is limited knowledge about competency‐based approaches in the accounting profession and among accounting academics, which is constraining discussion about the value of these approaches and about the strengths and weaknesses of the different competency models that have sprung up in various jurisdictions. In this paper we review and synthesize the literature on competency‐based approaches. We identify a number of theoretical benefits of competency‐based approaches. However, we also find many alternative definitions and philosophies underlying competency‐based approaches, and a variety of visions of how competencies should be determined and assessed. We note that there is limited evidence supporting many competency‐based approaches and we identify 14 research questions that could be used to help policy makers to more effectively address policy matters related to competency‐based education and assessment.
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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 | 0.039 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".