Practice Makes Better: Using Immersive Cases to Improve Student Performance on Day 1 of the Common Final Examination*
Bibliographic record
Abstract
ABSTRACT In the context of an accredited CPA program, we investigate whether an immersive case delivered over several weeks in a team‐based environment in the Performance Management elective module improves student performance on Day 1 examinations and whether all students benefit equally from the immersive case. Results show that using an immersive case prior to Capstone 1 significantly improves student performance on practice Day 1 examinations and Day 1 of the Common Final Examination (CFE). Although high‐ability students and non‐English‐as‐a‐second‐language (ESL) students with co‐op experience perform better regardless of whether an immersive case is used, ESL students benefit from using an immersive case when evaluating performance on Day 1 of the CFE. In addition, spending six to eight weeks preparing extensively for the CFE after graduate classes end in July seems to contribute to closing performance gaps between students. This study benefits the education process by identifying a tool that educators can use to improve performance on the CFE. It provides insights that may prove useful to CPA Canada and accredited post‐secondary institutions as they revamp their programs to align with Competency Map 2.0.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".