Diverse methodological approaches and considerations for SOTL in higher education. The Canadian Journal for the Scholarship of Teaching and
Bibliographic record
Abstract
The scholarship of teaching and learning (SoTL) is a distinctive form of research that is shaped by multi-disciplinary contexts and focuses on practice-driven institutional/curricula/classroom inquiries with an explicit transformational agenda. This essay provides critical insight to the importance of communities of SoTL practitioners, diverse investigations and methodological approaches within and across disciplines. For example, through an integrated and iterative process, a community of SoTL researchers plays a critical role to enhance and sustain on-going SoTL research. Equal to all forms of research endeavour, interpretations of SoTL research require a healthy scepticism, analysis of methodological rigour, and an openness to alternative perspectives and analysis. L'avancement des connaissances en enseignement et apprentissage (ACEA) dans l'enseignement supérieur est une forme de recherche distinctive façonnée par des contextes multidisciplinaires. Axée sur la pratique, elle est centrée sur caractérisée par des enquêtes sur des institutions, des programmes d’enseignement et des classes, tout en axée sur la pratique et visant un objectif transformationnel explicite. Cet essai fournit un aperçu critique de l’importance des communautés d’intervenants de l’ACEA et des diverses enquêtes et approches méthodologiques au sein de chaque discipline et entre elles. Par exemple, grâce à un processus intégré et
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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 | 0.163 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.028 | 0.124 |
| Scholarly communication | 0.036 | 0.019 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.009 | 0.013 |
| 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".