Anatomy of an MBA Program Capstone Project Assessment Measure for AACSB Accreditation
Bibliographic record
Abstract
There is very little written about assessment measures business schools use for assessing their programs that not only helps them meet the assessment criteria necessary for AACSB accreditation but also helps them improve the curriculum to build top-tier successful programs. This paper informs the literature on the assessment measure (and process) used by an MBA program to assess student learning through end-of-program capstone projects; success that is demonstrated by stabilized enrollments and a recent top-tier ranking. This paper is useful to any graduate or undergraduate business program that chooses to use capstone projects as an assessment measure to earn or renew AACSB accreditation. Following guidelines suggested by Banta (2004, 2007, 2011) and Polomba and Banta, (1999) a defined process is implemented to collect, assess, and disseminate assessment data to improve the MBA curriculum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".