Using Mini-Cases to Develop AICPA Core Competencies
Bibliographic record
Abstract
Abstract Purpose The subject area of the assignment is financial accounting and AICPA core competency skill development. This instructional tool enhances coverage of financial accounting topics in undergraduate Intermediate Accounting courses and graduate level Financial Accounting courses. Methodology/approach This paper provides a series of mini-cases which can be assigned to students to complete either in writing, through a brief presentation or both. Assignments can be completed on an individual basis or as a group. This provides flexibility for targeting different skill sets. Findings Mini-cases are short and less time-consuming than traditional cases, so instructors can use multiple assignments with different formats in a single semester. This provides students the opportunity to improve skills over a number of assignments within a semester. Practical implications A list of supplementary materials is made available and includes sample mini-cases, sample search results from the AICPA Codification, and sample memorandums. Originality/value The mini-cases provided in this paper are designed to facilitate the development of AICPA core competencies. This includes communication and leadership skills, strategic and critical thinking skills, problem solving, anticipating and serving evolving needs, synthesizing intelligence to insight, and integration and collaboration.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".