Designing the Value of Pedagogical Innovations
Bibliographic record
Abstract
Despite an increasing pressure to publish, management scholars do find the time to innovate so as to deliver relevant content to students in the best conditions, leading to the development of new and exciting pedagogical innovations. As more and more publications present experiential exercises as a specific type of pedagogical innovation, the question of whether these new exercises will be adopted by others remains open – in particular when they require significant resources and/or outcomes are unsure. Indeed, the innovation literature argues that the relative adoption of innovations reflects their perceived value. In the context of experiential exercises, this suggests that it is important to design the value of the innovation in terms perceivable by management scholars. In this paper, we conduct a systematic literature review of articles introducing new management-related experiential exercises published between 2008 and 2019, in an effort to investigate the most commonly used methods through which creators of new experiential exercises assess the effectiveness of their innovations. Drawing on the concept of value proposition from the innovation literature, we posit that the type of evidence required to provide a compelling case for the adoption of new exercises may in fact depend on the degree to which they represent high or low stake endeavors to users. We then build upon this argument by illustrating how a mixed methods collection strategy may be particularly useful for articulating the value proposition of exercises perceived as reflecting “higher stakes”.
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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 | 0.057 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".