Expressions of Trust: How University STEM Teachers Describe the Role of Trust in their Teaching
Bibliographic record
Abstract
Positive teacher-student and student-student relationships are among the most significant factors contributing to learning, motivation, wellbeing, and graduation rates in higher education. Trust is commonly understood as a key element for the development and sustenance of positive educational relationships, yet relatively little empirical research investigates trust in higher education classrooms. In this study, we explore how science, technology, engineering and mathematics (STEM) teachers (n=29) from universities in four countries (Canada, New Zealand, Sweden, and USA) describe their intentions and actions related to trust in one of the large enrollment courses they teach. We consider the ways that teachers understand and value trust in their teaching, and what this might suggest about how they approach trust-building with and among their students. We report on four broad approaches to trust expressed by teachers in this study, framed as teacher statements to students: “trust me,” “trust yourself,” “trust each other,” and “I trust you.” This research has implications for teachers, SoTL scholars, and academic developers in higher education.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".