Symposium: Better teaching through science: incorporating the scholarship of teaching & learning
Bibliographic record
Abstract
The Scholarship of Teaching and Learning, also referred to as SOTL, provides a framework for instructors to evaluate student learning and use evidence to determine pedagogical changes in the classroom. Engagement in SOTL challenges scholars to ask questions about their teaching practices and share with a larger community of practice. Examples of this include manuscript submissions to peer-reviewed journals, presenting abstracts at conferences, and other outlets that allow scholars to disseminate their findings. SOTL practices can be applied within an individual classroom or across a curriculum. Additionally, the promotion and tenure process at many institutions of higher education are highly recommending that faculty demonstrate impact on student learning. This symposium, presented at the 2022 Poultry Science Association Annual Meeting, highlighted best practices in SOTL, implementation of SOTL programming, and discussed using SOTL as a tool to evaluate teaching effectiveness. Poultry and animal science educators shared their experiences with implementing SOTL in their classroom and the benefits to students. From this symposium, we can conclude that there are multiple ways to document teaching excellence and conduct SOTL projects. This is of interest to educators implementing scholarly teaching in their classrooms.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".