Re-Invigorating Mid-Career Faculty at a Canadian Mid-Sized College: Strategies for Professional Development and Mentorship
Bibliographic record
Abstract
Mid-career faculty (MCF) currently comprise a predominant number of faculty at higher education institutions. Mid-career faculty applies to those individuals who have been teaching within post-secondary education for more than five years but are still more than five years from retirement. As faculty move into this middle phase of their career, there tends to be fewer opportunities for professional development, reduced opportunities for advancement or leadership, and a lack of mentorship. These decreased opportunities lead to a decrease in MCF job satisfaction. At Riverside College (a pseudonym) a significant percentage of the faculty leaving the college in the last few years have been MCF. In this organizational improvement plan (OIP), I explore what strategies will support the development of MCF at a mid-sized community college in Western Canada, that will lead to increased engagement and retention. Within the OIP I will use a combination of Appreciative Inquiry, the Plan-Do-Study-Act cycle, and ADKAR® to guide the change. I will draw on perspectives from an interpretive lens and use both distributive and authentic leadership approaches. The solution chosen to address the problem of practice is to develop specific MCF professional development and mentorship as a retention strategy. One example of professional development is the scholarship of teaching and learning (SoTL). Scholarship of teaching and learning is a process for faculty to focus their professional development in mid-career. Educational developers responsible for faculty professional development support MCF to examine their teaching practices and move from scholarly teaching to scholarship of teaching and learning.\nKeywords: mid-career faculty (MCF), retention, professional development, educational developer, leadership, scholarship of teaching and learning (SoTL).
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".