A Conceptual Framework for Learning Management Accounting
Bibliographic record
Abstract
This paper demonstrates how Schoenfeld's (1985) conceptual framework for mathematics can provide an alternate framework for learning and thereby teaching management accounting. The four-part framework—heuristics, resources, beliefs, and controls—is a refinement to problem-based learning with three attributes in regard to management accounting. First, all aspects for teaching management accounting are integrated into a single framework or theory. Consistency among all parts of management accounting clarifies student and instructor roles in the learning process. Second, the framework's problem-solving focus with linkages to explanatory materials or resources allows students to be rigorously informed about the functionality of management accounting heuristics. Third, transition or extension of relatively simple, standard problems to more complex nonstandard problems or cases is facilitated by introducing appropriate beliefs and controls. In effect, this approach enables management accounting, and particularly case analysis, to be taught with more structure.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".