Sparking SoTL: Triggers and Stories from One Institution
Bibliographic record
Abstract
With the growing interest in educational research across post-secondary campuses, it is useful to identify the specific supports that best enable Scholarship of Teaching and Learning (SoTL) initiatives. This paper documents a picture of SoTL interests and supports at one institution through survey and semi-structured collaborative interview data. Both the survey data (289 respondents) and three semi-structured group interviews (8 participants total) provide a picture of participants who have completed or are interested in completing a SoTL study; the events and experiences that triggered an interest in SoTL; and their perceptions of the importance of SoTL in their own teaching, student learning, in their department, and within the institution as a whole. Based on these two datasets, we propose four lenses that are defined in terms of SoTL triggers and which we name a Scholarship Window. We conclude with a number of recommendations as a way to build capacity for SoTL at the institutional level. Suite à l’intérêt grandissant de la recherche dans le domaine de l’éducation sur tous les campus d’enseignement supérieur, il est utile d’identifier les soutiens spécifiques qui favorisent le mieux les initiatives de l’avancement des connaissances en enseignement et en apprentissage (ACEA). Cet article présente un tableau des intérêts et des soutiens en ACEA dans un établissement donné, établi par le biais d’une enquête et d’une série d’entrevues en collaboration semi-structurées. Les données obtenues suite à l’enquête (289 répondants) et celles de trois entrevues semi-structurées (8 participants au total) ont permis d’établir un tableau de participants qui ont complété ou qui s’intéressent à compléter une étude en ACEA, les événements et les expériences qui ont déclenché cet intérêt en ACEA ainsi que les perceptions des répondants de l’importance de l’ACEA pour leur propre enseignement, pour l’apprentissage des étudiants et pour leur département, ainsi qu’au sein de l’établissement dans son ensemble. Sur la base de ces deux groupes de données, nous proposons quatre lentilles qui sont définies en tant que déclencheurs d’ACEA, que nous avons nommées « fenêtre sur l’avancement des connaissances ». En conclusion, nous présentons un certain nombre de recommandations pour renforcer les capacités en ACEA au niveau institutionnel.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.020 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".