A Structured Review of Research‐Informed Instructional Strategies to Support <scp>CPA</scp> Enabling Competencies in Future Accountants*
Bibliographic record
Abstract
ABSTRACT CPA enabling competencies underpin the human skills and professional values that all future accountants should possess. Nevertheless, to date, the discourse is limited within the scholarship of teaching and learning on how to best inculcate these competencies in future accountants. This study attempts to spark such a discourse by conducting a structured literature review (SLR) of the research‐informed instructional strategies to foster CPA enabling competencies, skills, and values in future accountants, and outlines future research opportunities. The findings indicate that the CPA competency “acting ethically and demonstrating professional values” can be engrained in future accountants using business cases and targeted courses/lessons in accounting classrooms. “Leading” is best taught through targeted courses/lessons. “Collaboration” can be gained through team‐based learning (group work) and software. “Managing self and others” can be engrained through a strategic course setup. “Adding value” can be achieved by experiential learning. “Solving problems” can be facilitated through in‐class activities that specifically target critical thinking skills. Finally, “communication” is facilitated with writing tasks and software. The top five research‐informed teaching tools that advance CPA enabling competencies are collegial tools (i.e., group work, peer review, and writing prompts), software, business cases, experiential tools, and targeted courses/lessons. In the future, an in‐depth SLR should be conducted on each of the five research‐informed teaching tools for their integration within accounting classrooms.
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 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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".