Enhancing Experiential Learning Opportunities Through the Integration of Continuous Improvement Practices
Bibliographic record
Abstract
Experiential learning (EL) in higher education has become a prominent academic curriculum component. Recent provincial guidelines emphasize that post-secondary institutions provide students EL opportunities, outlining criteria as to what counts as an EL activity. While these guidelines provide instruction on what an EL opportunity should contain, it does not detail how post-secondary institutions should develop and implement these activities responsive to their unique student population needs. This Organizational Improvement Plan (OIP) aims to determine how an Ontario university can provide meaningful, student-focused EL opportunities through a centralized, theoretically-informed EL implementation framework. It centers around a Problem of Practice (PoP) at Gordon University (GU), where the absence of formal internal practices on how to develop and implement EL has created an imbalance in current offerings. Throughout the OIP, a distributed-adaptive hybrid leadership approach combined with the change path model (Cawsey et al., 2016) creates a pathway to propel identified change practices forward. An organizational analysis identifies key change areas, determining the scope and type of change needed. Using Starratt’s (1991, 1996) ethics of care, justice and critique reveals the ethical considerations and challenges a chosen solution needs to address. The result is a proposed solution to the PoP that focuses on organizational learning using Kolb’s (2015) EL theory. Embedding this organizational learning in GU’s existing quality assurance academic review framework will formalize the process. The model for improvement (Langley et al., 1994; Langley et al., 2009; Moen & Norman, 2009) guides the implementation, monitoring and evaluation, and communication plans to ensure continuous improvement of the chosen solution.\nKeywords: Experiential learning, Continuous improvement, Quality assurance, Distributed-adaptive leadership
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".