Humanizing STEM education: an exploratory study of faculty approaches to course redesign
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents the findings from the analysis of reflections from 26 STEM faculty at various institutions of higher education across the United States who participated in the online course, The Humanity of Inclusive Practices, part of the Teaching and Learning Academy, offered by the John N. Gardner Institute (Gardner Institute) for Excellence in Undergraduate Education. Participants answered three questions at the end of the online course: what are your equity challenges? What are your goals? How do you measure your success?; we analyzed responses using grounded theory. Findings from this study suggest that student-teacher positionality and inequity in prior knowledge may cause equity challenges for educators. Furthermore, the findings suggest that participants in the course set goals such as increasing student success (grades) in the course, empowering students, and incorporating inclusive material in curricula to humanize their course(s). Lastly, the findings reveal that educators measure their success through grades, as well as student engagement and feedback. Recommendations on how to tackle the challenges associated with humanizing STEM course redesign are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it