Peeling the (experiential) onion: A review of the interconnected layers of research on experiential learning in <i>Management Learning</i> between 2010 and 2024
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it