Educational Leadership Supporting Faculty-Motivated Professional Development in Teaching and Experiential Learning
Bibliographic record
Abstract
Teaching is one of the primary responsibilities of most university faculty members. Yet, pedagogical training and professional development in teaching and experiential learning are not employment requirements for most Ontario university faculty. This incongruence impacts faculty’s sense of self-efficacy, ability to protect their academic freedom, and their ability to design pedagogically informed curriculum. Additionally, it can negatively impact student outcomes while influencing institutional reputations and funding. In response, recommendations to address this problem of practice (PoP) must acknowledge the faculty prerogatives of autonomy, self-governance, and academic freedom. For that reason, this Organizational Improvement Plan (OIP) evaluates and proposes educational leadership approaches to promote faculty-motivated professional development at an anonymized institution designated as The Ontario University (OntU). What strategies might further promote the uptake of faculty-driven pedagogical training? With an emphasis on a collaborative, constructivist approach, this OIP recommends distributed and transformational leadership strategies to accommodate the autonomous prerogatives of faculty members and which align with both administrative and collegial governance structures. In addition to using a constructivist framework, the conceptual frameworks of self-determination and learning culture theories are used to evaluate ethical approaches to the PoP and develop recommendations. Ultimately, the goal of this OIP is to inspire and enact meaningful, transformational change at OntU that increases the number of faculty who choose to engage in pedagogical professional development and the realization of its far-reaching benefits to a variety of stakeholders.
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.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".