Teaching dossier guidance for professional faculty: an evidence-based approach for demonstrating teaching effectiveness
Bibliographic record
Abstract
This research delves into the challenging paradox facing university faculty: they are often hired with minimal formal teacher training yet must exhibit teaching effectiveness when seeking promotion or tenure. This issue becomes particularly salient for educators with non-traditional, professional backgrounds who must demonstrate pedagogical competence despite lacking conventional academic training. This study examines teaching dossier guidelines employed by prominent universities that hire permanent teaching-focused business faculty who may have diverse, non-traditional backgrounds. For example, a Chartered Professional Accountant who trained in a public accounting firm and worked as a Chief Financial Officer of a public energy company or a sales executive who led the business development department of a large company likely do not possess the same academic training of a doctorate degree like other academics; however, such professional faculty may possess relevant experience and skills to teach accounting or marketing, respectively, to post-secondary students effectively. Our analysis identifies recurring recommendations for faculty to incorporate into their teaching dossiers, encompassing elements such as summaries of teaching responsibilities, documentation of course development or modification, creation of instructional materials, ongoing pedagogical improvement endeavors, outstanding teaching materials, articulation of teaching philosophies, and evidence of collegial collaboration and support. Our findings reveal a disconnect in understanding and recognizing the significance of teaching and teaching dossiers. In light of these observations, this paper outlines the limitations inherent in the current system. It suggests promising avenues for future research within this domain. We aim to foster a more equitable and supportive environment for all faculty members engaged in the complex task of academic teaching.
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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 | 0.318 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".