Social Support and Motivation in STEM Degree Students: Gender Differences in Relations with Burnout and Academic Success
Bibliographic record
Abstract
Existing research underscores the importance of both social-environmental factors (e.g., social support) and psychological factors (e.g., motivation) as buffers against attrition, low performance, and psychological maladjustment in science, technology, engineering, and math (STEM) degree programs. Female STEM students in particular contend with additional academic challenges that can hamper their motivation and performance. This study investigated the relations between social support (personal vs. academic) and well-being, persistence, and academic outcomes as mediated by self-determined motivation and moderated by gender. Structural equation modelling with 221 STEM undergraduates showed significant indirect paths between personal support and STEM career intentions via autonomous motivation. Multigroup analyses further showed male STEM students to benefit more from both personal and academic supports, as well as from greater autonomous motivation, with the academic risks of controlled motivation observed primarily for female STEM students. Implications for motivationally supportive teaching practices in STEM degree programs are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".