Teaching Qualitative Research: Fostering Student Curiosity through an Arts-Informed Pedagogy
Bibliographic record
Abstract
Creative pedagogical approaches in higher education can facilitate students’ journey in thinking like and becoming a qualitative researcher. Pedagogical approaches tend to focus on procedural steps of qualitative research neglecting students’ development of cognitive skills and reflective capacity. Arts-informed teaching methods for qualitative research show promise as an educational development in stimulating student interest and expanding their understanding of qualitative research through an experiential approach to learning. In this article, the use of an arts-informed pedagogy to structure a graduate level qualitative research course is discussed. This pedagogy, grounded in experiential teaching-learning theories, was developed to foster students’ curiosity as well as their capacity to think like a qualitative researcher through arts media including poetry, dance, film and story. If space is created in the classroom for curiosity to become a disposition and habit of mind, students may be inspired to be perpetually inquisitive and as such, think like a qualitative researcher.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".