Learning and Development Through the Scholarship of Teaching and Learning
Bibliographic record
Abstract
ABSTRACT This article focuses on learning that can facilitate the transition from being a scholarly teacher to becoming a scholar of teaching and learning, outlining the critical steps faculty can take to deepen their engagement with the scholarship of teaching and learning (SoTL). It begins by distinguishing between scholarly teaching, which involves applying research‐based strategies in the classroom, and being a SoTL scholar, which requires faculty to systematically investigate their own teaching practices and the learning outcomes of their students. The article explores various pathways through which course instructors can learn and engage with SoTL, such as attending workshops, collaborating with colleagues in interdisciplinary communities, or engaging with existing SoTL literature. It discusses how faculty can identify areas of inquiry in their teaching practice, collect and analyze data, and use this evidence to refine and transform their teaching methods. The article also highlights institutional support structures that can facilitate this transition, including mentorship, funding for SoTL research, and peer review opportunities. Through these approaches, faculty can develop a scholarly identity in teaching and contribute meaningfully to the field.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| 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".