Phenomenology as a methodology for Scholarship of Teaching and Learning research
Bibliographic record
Abstract
The Scholarship of Teaching and Learning (SoTL) is a rich forum where scholars from different fields and philosophical orientations find space to share their research on teaching and learning in higher education. Within this paper, we will share our individual and collective experiences of why we perceive phenomenology as a methodology well-suited for a broad range of SoTL purposes. Phenomenology is a research approach that focuses on describing the common meaning of the lived experience of several individuals about a particular phenomenon. We will discuss how phenomenology informed our own SoTL research projects, exploring the experiences of faculty and undergraduates in higher education. We will highlight the challenges and affordances that emerged from our use of this methodology. Phenomenology has motivated us to tell our stories of SoTL research and within those, to share the stories that faculty and students shared.
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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 | 0.101 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".