Same Classroom, Different Affordances? Demographic Differences in Perceptions of Motivational Climate in Five STEM Courses
Bibliographic record
Abstract
Students vary in their perceptions of teachers’ motivational supports, even within the same classroom, but it is unclear why this is the case. To enable the design of equitable environments and understand the theoretical nature of motivational climate, this study explored demographic differences in university students’ perceptions of instruction across five large, introductory STEM (science, technology, engineering, and mathematics) courses (N = 2,486), along with end-of-semester outcomes. Results indicated that women and students from traditionally underrepresented racial or ethnic groups (Black, Hispanic/Latino/a, or Indigenous students) tended to perceive slightly higher motivational support in their courses compared to men and traditionally overrepresented (White or Asian) students, respectively. However, patterns were not uniform across all courses or variables. Men and women did not significantly differ on end-of-semester interest in any course, but women tended to have lower self-efficacy in some courses and significantly higher grades in programming compared to men. Implications include a caution for researchers against interpreting sample-specific or aggregated evidence of demographic differences as generalizing to broader populations or specific settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".