Outcomes Assessment and Quality Enhancement through AACSB Business Accreditation: The Case of the University of Bahrain
Bibliographic record
Abstract
Assessment of students learning is a critical component of the accreditation process of the Association to Advance Collegiate Schools of Business (AACSB). There is also a general agreement that the most tangible benefits from AACSB accreditation result from the changes in the internal operations to bring the higher education institutions into compliance with its standards. While AACSB is the most sought after Business accreditation in the Arab region, there is very little written about AACSB accreditation and the challenges facing business colleges in meeting the standards related to outcomes assessment in this region. This paper aims to address this gap in the literature by discussing the case of the College of Business of the University of Bahrain. It considers some of the concerns raised in the existing literature and offers suggestions for other institutions involved in the accreditation process. It also examines the link found between outcomes assessment and quality in higher education as well as the challenges and opportunities of AACSB accreditation for the College of Business of the University of Bahrain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".