Does Tax Curriculum or VITA participation correlate with improved REG Section CPA Exam Scores?
Bibliographic record
Abstract
Abstract Students who graduate with an accounting degree often take the Certified Public Accountant (CPA) exam to increase their earning potential and gain prestige within the accounting profession. The Regulation (REG) section of this exam exemplifies a student's knowledge in the United States’ tax filing system and makes up a quarter of the total exam. Accounting students often participate in Volunteer Income Tax Assistance (VITA) programs which may provide an avenue of preparation for the REG section of the CPA exam. This research aims to identify if there is a connection between universities that offer a VITA program and their respective students' success on the REG section of the CPA exam. Tax course options are also analyzed to determine if the amount of tax courses taken also impacts the REG section score of the CPA exam. This was done using a combination of public data consisting of different universities’ first-time pass rates on the REG section and a survey that was distributed to professors regarding their accounting and VITA programs. No difference was found in REG pass rates between those universities that offered VITA and those that did not offer VITA. However, this research did find that when students attended a university that offered elective tax courses, they scored higher on the REG section of the CPA exam. This means that universities could begin offering additional tax courses if they wish to see an improvement in their students’ CPA exam scores. The results of this research can be used to communicate to universities what level of significance VITA programs have on their accounting students’ future success.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".