An Investigation into Assurance of Learning in an Introductory Financial Accounting Course
Bibliographic record
Abstract
This study investigates assurance of learning for an introductory financial accounting course at a large Canadian university. This university is considering AACSB accreditation. To measure student learning, exam questions, the sole method of assessment, were mapped to the learning objectives of the course. Student results, by question, were collected in order to assess the extent to which course objectives were being met. The course objectives were then linked to program and university learning outcomes and applicable AACSB standards. A conceptual framework situating introductory financial accounting within the program and university environment is constructed. This framework can be applied by universities pursuing or supporting AACSB accreditation using a course-embedded approach. This paper contributes to the accounting education literature by providing a case study of the early stages of implementing assurance of learning in an accounting course. It describes an approach for determining the achievement of course objectives and provides a framework for the development of course objectives that support program and university-wide learning outcomes and AACSB accreditation standards.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".