Accessing Research‐Informed Instructional Strategies to Teach Financial and Managerial Accounting: A Review and Recommendations
Bibliographic record
Abstract
ABSTRACT Financial and managerial accounting (FMA) courses are critical courses that give first‐year students their first taste of technical accounting content. This paper surveys 20 years of literature in five specialist accounting education journals to draw out research‐informed instructional strategies and associated recommendations on their use and development. The findings follow Anson's instructional design model, identifying five categories of research‐informed instructional strategies that target FMA: experiential learning; software; content or exam design/delivery; in‐class activities; and games. First, the study recommends the use of experiential tools to teach FMA. Second, firms should develop software tools that go beyond multiple choice questions and leverage tools like Excel spreadsheets, data analysis, and visualization, as well as media, such as videos and podcasts. Finally, universities should build communities of practice to encourage a greater level of discourse around teaching FMA. One of the contributions of this paper is to systematically filter, organize, and present the academic peer‐reviewed literature that describes research‐informed instructional strategies in FMA.
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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 | 0.027 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".