Beyond Learner Reaction: Measuring the Impact of Leadership Development at The Ivey Academy
Bibliographic record
Abstract
This paper provides an in-depth exploration of The Ivey Academy’s transition from traditional satisfaction-based evaluations to a more comprehensive impact evaluation approach in leadership development. Recognizing the limitations of relying solely on participant satisfaction, The Ivey Academy adopted a modified framework inspired by the Kirkpatrick Model, which evaluates satisfaction, learning, application, and longterm impact. This framework utilizes a range of data collection tools, including surveys, interviews, and action plans. The paper details the implementation process, from securing stakeholder engagement to designing effective surveys and overcoming the challenges of resistance and operational limitations. A key focus of the paper is on the impact survey results from the first term of open enrollment programs, which demonstrate significant improvements in workplace behavior and leadership strategies among participants. Additionally, it highlights the challenges in ensuring data comparability across diverse programs and audiences. Looking ahead, the paper discusses future directions for The Ivey Academy, emphasizing the refinement of the evaluation process, expanding impact measurement, and exploring standardization across various leadership development programs. This approach underscores The Ivey Academy’s commitment to driving realworld change through leadership education, offering valuable insights for other institutions aiming to adopt similar evaluation practices.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".