Leveraging the Scholarship of Teaching and Learning for Quality Enhancement
Bibliographic record
Abstract
This paper argues a divide exists between quality assurance (QA) processes and quality enhancement, and that the Scholarship of Teaching and Learning (SoTL) can bridge this divide through an evidence-based approach to improving teaching practice.QA processes can trigger the examination of teaching and learning issues, providing faculty with an opportunity to systematically study their impact on student learning.This form of scholarship positions them to take a critical and empowered role in the continuous improvement of student learning experiences and to become full participants in the goal of QA structures.A document analysis of current provincial QA policies in Canada reveals a gap between how teaching and learning challenges are identified and how those challenges are studied and acted upon.A QA report is not the end result of an assurance process.It is the beginning of a change process that is intended to lead to improvements in the student learning experience.The authors consider how SoTL provides a research-minded approach to initiate continuous improvements within a QA framework, and provides considerations for how it might be integrated into evolving provincial frameworks.Dans cet article, les auteurs soutiennent qu'il existe un fossé entre les processus d'assurance de la qualité (AQ) et l'amélioration de la qualité et que l'avancement des connaissances en enseignement et en apprentissage (ACEA) peut combler ce fossé par le biais d'une approche basée sur l'évidence pour améliorer les pratiques d'enseignement.Les processus d' AQ peuvent déclencher l'examen des problèmes relatifs à l'enseignement et à l'apprentissage et ce faisant, donner aux professeurs l'occasion d'étudier systématiquement leur impact sur l'apprentissage des étudiants.Cette forme de recherche savante leur permet de jouer un rôle important dans l'amélioration continue des expériences d'apprentissage des étudiants et de devenir des participants à part entière pour atteindre l'objectif des structures de l' AQ.Une analyse des documents relatifs aux politiques actuelles d' AQ au Canada révèle qu'il existe un écart entre la manière dont les défis relatifs à l'enseignement et à l'apprentissage sont identifiés et la manière dont ces défis sont étudiés et dont on y a remédié.Le rapport d' AQ n'est pas la conclusion du processus d'assurance de la qualité.C'est le début d'un processus de changement qui doit aboutir à des améliorations de l'expérience d'apprentissage des étudiants.Les auteurs examinent la manière dont l' ACEA fournit une approche de recherche pour entreprendre les améliorations continues dans le cadre de l' AQ et apporte des réflexions pour déterminer comment l' ACEA peut être intégré dans des cadres provinciaux évolutifs.
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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 | 0.048 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.004 | 0.008 |
| 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".