Exploring teaching effectiveness and research on teaching and learning at AACSB accredited business schools in Canada and the US
Bibliographic record
Abstract
Purpose This study explores the implementation of two Association to Advance Collegiate Schools of Business (AACSB) standards by business schools across Canada and the US. First, this study examines how teaching effectiveness is defined and measured in light of Standard 7 (Teaching Effectiveness and Impact). Second, this study explores the value of research on teaching and learning in relation to Standard 8 (Impact of Scholarship). Design/methodology/approach This study adopts a thematic analysis framework based on data obtained from an online survey, semi-structured interviews, and policy documents. Findings The results reveal that business schools rarely define teaching effectiveness; instead, they adopt various measures to evaluate teaching effectiveness. The results reveal that research on teaching and learning alone usually does not lead to tenure; however, it is valued if part of a portfolio that includes discipline-specific research. Lastly, this research highlights a stigma associated with research on teaching and learning relative to discipline-specific research. Practical implications This study introduces a comprehensive and integrated teaching evaluation framework that can be adopted to define teaching effectiveness and elevate the teaching function. In addition, the authors argue that business schools should nurture a niche set of academics that holds PhDs in their respective disciplines and are education experts to increase the production of research-informed instructional strategies curated for business schools. Originality/value This is the first study to explore how AACSB standards related to teaching effectiveness and research on teaching and learning are interpreted and implemented at AACSB accredited business schools.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".