The Scholarship of Teaching and Learning
Bibliographic record
Abstract
This book is a guide to the scholarship of teaching and learning (SoTL) for scientists, engineers, and mathematicians teaching at the collegiate level in countries around the world. It shows instructors how to draw on their disciplinary knowledge and teaching experience to investigate questions about student learning. It takes them all the way through the inquiry process beginning with framing a research question and selecting a research design, moving on to gathering and analyzing evidence, and finally to making the results public. Numerous examples are provided at each stage, many from published studies of teaching and learning in science, engineering, or mathematics. At strategic points, short sets of questions prompt readers to pause and reflect, plan, or act. These questions are derived from the authors’ experience leading many SoTL workshops in the United States and Canada. The taxonomy of SoTL questions— What works? What is? What could be? —that emerged from the SoTL studies undertaken by the Carnegie scholars provides a useful framework at many stages of the inquiry process. The book addresses the issue of evaluating and valuing this work, including implications for junior faculty who wish to engage in SoTL. The authors explain why SoTL should be of interest to science, technology, engineering, and mathematics (STEM) faculty at all types of institutions, including faculty members active in traditional STEM research. They also give their perspective on the benefits of SoTL to faculty, to their institutions, to the academy, and to students.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".