Innovation in leadership education: inquiry-based pedagogy
Bibliographic record
Abstract
Purpose The purpose of our research is to better understand inquiry-based pedagogy in the context of leadership education. Specifically, we sought to learn about how leadership learning is characterized in an immersive inquiry course, and how inquiry-based pedagogy is experienced by students engaged in interdisciplinary leadership learning. Design/methodology/approach We used a case study approach as an overarching methodology. The research methods employed to collect data were World Cafe and episodic narrative interview. Further, we used collocation analysis and systematic text condensation as analytical strategies to interpret data. Findings Our findings led us to four primary conclusions: (1) inquiry-based learning helps to foster an inquiry mindset amongst leadership education students; (2) the challenges and tensions associated with inquiry-based learning are worth the learning gains for leadership students; (3) the opportunity to learn in relationship is beneficial for leadership development outcomes and (4) students’ experiences of inquiry-based learning in leadership education often included instances of transformation. Research limitations/implications Limitations of the research were: (1) it is a case study situated within a unique, particular social and educational context; (2) demographic data were not collected from participants, so results cannot be disaggregated based on particular demographic markers and (3) the small sample size involved in the study makes it impossible to generalize across a broad population. Practical implications This research has enabled a deep understanding of structural and relational supports that can enable effective inquiry-based learning in leadership education. It also offers evidence to support institutional shifts to inquiry-based pedagogy in leadership education. Social implications Our research demonstrates that use of inquiry-based pedagogy in leadership education has long-lasting positive effects on students' capacity for applied leadership practice. Consequently, participants in this type of leadership learning are better positioned to effectively lead social change that is pressing in our current global context. Originality/value There is scant (if any) published research that has focused on using inquiry-based pedagogies in leadership education. This research makes a significant contribution to the scholarship of leadership education.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".