Rethinking Resilience: Evaluating an Integrated Academic Resilience Curriculum in Undergraduate STEM Courses
Bibliographic record
Abstract
The Rethinking Resilience project includes an interactive Brightspace module designed to teach undergraduate students about stress, coping, and academic resilience. The goal of this module is to empower undergraduate students to identify their own stress responses, understand the effects of stress on the body and brain, and explore evidence-based strategies for enhancing their resilience in the face of moderate, everyday stressors such as those posed by an undergraduate degree. Given the known mental health challenges faced by undergraduate students in STEM (Pester, Noh, & Fu, 2023), it is hoped that this module may provide needed support to students and promote well-being, motivation, and healthy approaches to learning. In this way, it is hoped the module will augment existing STEM curricula and empower students to manage their academic workloads more effectively. We recently launched a pilot of the project with eleven participating classes, collecting pre- and post-module data regarding student stress and mental health, as well as feedback about the module. Continuing our meaningful collaboration, we intend to use this pilot data as a guide to evaluate the module as part of the curriculum in these courses and develop new ideas to further enhance its use, integrate it more intentionally into courses, and clarify expectations and usability for learners.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".