Peeling the (experiential) onion: A review of the interconnected layers of research on experiential learning in <i>Management Learning</i> between 2010 and 2024
Bibliographic record
Abstract
In an essay published for the 40th anniversary issue of Management Learning , Reynolds reflected on the impact of, and reactions to, experiential learning to teach management. Fifteen years later, in honor of the journal’s 55th anniversary, we delve into the research published since that point to explore how experiential learning is invoked in Management Learning . To this end, we reviewed and coded 45 articles published between 2010 and 2024. This process pushed us to reflect on three different (often interconnected) ways in which experiential learning is examined in the journal, with articles that explore the experiential learning process, center on one or more specific dimensions of experiential learning, and attend to contextual elements that facilitate or hinder experiential learning. We also situate the methods and activities discussed across the sample within the clusters of experiential learning identified by Grain, allowing us to identify areas in which research in Management Learning overlaps with and extends the model. To close, we relate our findings to contemporary debates about experiential learning and education, both within the journal and the field, and propose future research directions.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".