How Educational Developers can Re-engage Mid-Career Faculty Using SoTL
Bibliographic record
Abstract
Mid-career faculty (MCF) currently make up a significant number of faculty at higher educational institutions. This group comprises key stakeholders with institutional history, diverse teaching and learning experiences, and strong relationships with colleagues. While faculty need different kinds of support and opportunities at different career stages, it has been reported that mid-career professional development is under-researched and overlooked. We contend that professional development for MCF is essential if these faculty are going to continue to grow as educators, leaders, and scholars. With the support of Educational Developers (EDs), the Scholarship of Teaching and Learning (SoTL) is one way for faculty to focus their professional development in the middle years of their career. Drawing on the literature about challenges for MCF and using the micro-meso-macro-mega framework, we explore ways in which EDs can use SoTL to re-engage MCF on a revitalized path. Our synthesis offers reflections on our career experiences as EDs and boundary-spanning points to ponder for both EDs and MCF as they enter into SoTL engagement.
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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 | 0.064 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".