The Scholarship of College Teaching: Research Opportunities in the New Millennium
Bibliographic record
Abstract
This paper examines some of the challenges facing contemporary Canadian community colleges and explores research opportunities related to (a) measuring the effectiveness of college teaching, (b) preparing beginning college teachers, and (c) re-conceptualizing professional development programs. First, the criteria for measuring teaching effectiveness at colleges are often derived from those used in elementary and secondary schools; questions remain as to whether, or to what degree, these criteria are appropriate measures of effective teaching in the college setting. Second, unprecedented retirements are creating an influx of beginning faculty with extensive subject expertise but often with little or no training as professional teachers; while traditional mentorship models often function as in-house induction programs, innovative pre-service training programs are being developed to more fully prepare aspirants to the profession of college teaching. Finally, some colleges are re-conceptualizing their professional development programs, employing innovative models such as Teaching Circles which are designed to build communities of scholars focused on institutional excellence in teaching. Numerous opportunities exist for research into teaching effectiveness, faculty induction, and professional development amid the changes and challenges that currently face the community colleges.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".