Inquiry-based Learning in Higher Education: A Pedagogy of Trust
Bibliographic record
Abstract
This qualitative constructivist grounded theory study of trust within inquiry-based learning in higher education (IBL-HE) environments examined the experiences of instructors and students through four focus groups and nine individual interviews. As the study purpose is to understand the development and maintenance of trust in IBL-HE classrooms, participants are experienced instructors, learners, and authors of IBL-HE from Canada, USA, New Zealand, and Ireland. We used face-to-face sessions and zoom sessions to facilitate the focus group experience, and telephone for the individual interviews to explore the following two research questions: (1) what does trust mean in a higher education IBL (IBL-HE) classroom; and, (2) how do those involved create and maintain it? Our findings are revealed through our Pedagogy of Trust in IBL-HE using 3 themes: (1) Creating an environment of negotiated mutuality; (2) Emerging relationship/community building; and, (3) Internalizing and applying a mindset shift. Each of these stages involved a different trust relationship: (1) Professor-Student; (2) Student-Student; and, (3) Student-Self. These findings provide evidence for IBL as a pedagogy of trust in higher education, and reinforce the need for the Scholarship of Teaching and Learning (SoTL), and the lifelong learning skills desired by contemporary employers.
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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 | 0.032 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".