Scholarship of teaching and learning at AACSB accredited business school: who’s doing it, and how’s it captured?
Bibliographic record
Abstract
Purpose This paper explores who among the AACSB categorization of academics conducts the scholarship of teaching and learning (SoTL) research within business schools and how AACSB-accredited business schools capture SoTL research as part of their portfolio of intellectual contributions. Design/methodology/approach This study adopts a qualitative-method research design by collecting primary data through surveys, semi-structured interviews and secondary data in policy documents focused on AACSB-accredited business schools in Canada and the United States. Findings The findings establish that scholarly and practice academics who possess rigorously acquired research skills due to their terminal degrees are most likely to conduct SoTL research. The results also reveal an even split among respondents regarding whether their AACSB-accredited business school captures SoTL with their journal ranking frameworks. Practical implications Based on the findings, two recommendations are offered to foster more SoTL research at AACSB-accredited schools. First, higher education leaders (e.g. business school deans) can further inculcate a culture of SoTL research at the department and institutional levels by creating communities of practice (CoPs). Second, AACSB-accredited business schools could adopt more inclusive journal ranking frameworks to capture better and incentivize SoTL research. Originality/value This is the first known study to explore how AACSB Standards 3 and 8 are implemented and operationalized regarding SoTL research. Understanding how these standards are adopted and implemented could help institutional leaders, standard setters and administrators better facilitate SoTL research.
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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 | 0.039 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".