Case Study Methodology: Flexibility, Rigour, and Ethical Considerations for the Scholarship of Teaching and Learning
Bibliographic record
Abstract
Individuals and teams engaging in the scholarship of teaching and learning (SoTL) in multidisciplinary higher education settings must make decisions regarding choice of research methodology and methods. These decisions are guided by the research context and the goals of the inquiry. With reference to our own recent experiences investigating pedagogical and curricular practices in a pharmacy program, we outline case study methodology as one of the many options available for SoTL inquiry. Case study methodology has the benefits of flexibility in terms of the types of research questions that can be addressed and the data collection methods that can be employed. Conducted with proper attention to the context of the case(s) selected, ethical treatment of participants, and data management, case studies also have the necessary rigour to be credible and generalizable. In the matter of generalization, however, we recommend that the readers of a case study draw their own conclusions about the applicability of the findings to other settings. Les particuliers et les groupes qui sont actifs dans le haut savoir en matière d’enseignement et d’apprentissage (ACEA) dans les milieux pluridisciplinaires de l’enseignement supérieur doivent prendre des décisions en ce qui concerne le choix des méthodologies et des méthodes de recherche. Ces décisions sont guidées par le contexte de la recherche et par les objectifs de l’interrogation. En nous basant sur nos propres expériences récentes quand nous avons examiné des pratiques pédagogiques et curriculaires dans un programme de pharmacie, nous décrivons la méthodologie des études de cas comme l’une des nombreuses options disponibles pour les interrogations en ACEA. La méthodologie des études de cas a l’avantage d’être souple en ce qui a trait aux types de questions de recherche qui peuvent être étudiées et aux méthodes de collecte de données qui peuvent être employées. Quand elles sont menées avec l’attention requise pour le contexte des cas choisis, le traitement éthique des participants et la gestion des données, les études de cas présentent également la rigueur nécessaire pour être crédibles et généralisables. Toutefois, en ce qui concerne la généralisation, nous recommandons que les lecteurs d’une étude de cas tirent leurs propres conclusions concernant le caractère applicable des résultats à d’autres situations.
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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 | 0.687 | 0.582 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.015 | 0.075 |
| Scholarly communication | 0.035 | 0.025 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".