Let the students be heard – student voices on teaching excellence awards
Bibliographic record
Abstract
Globally, academics are encouraged to facilitate teaching excellence. Many business schools use teaching excellence awards to recognize exceptional efforts toward students’ learning, foster pedagogical innovation, and improve faculty motivation. However, prior literature has noted that many business schools lack clear and transparent criteria for TE awards, which can hinder the process and potentially reduce the motivational effects. Research has yet to fully incorporate student voices across a global setting into the evaluation criterion. As a result, this study seeks to identify universal criteria for TE awards based on a large-scale survey of 2,775 business students across eleven countries and five continents intending to capture global student perspectives. First, we reveal whether the possession of a TE award for an educator has any importance from students’ perspectives. Second, we find that students across the globe have a general agreement regarding the criteria for awarding an excellent educator by identifying 30 criteria for TE awards students noted across our global sample. Third, we reveal 15 criteria that are specific to some countries but not globally. Lastly, we explore the differences in TE award criteria across different study levels. Overall, our study makes a significant contribution by identifying global criteria based on student voice to inform the development of teaching excellence award criteria in business education or by higher education providers and professional bodies.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.012 |
| 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".