Bibliographic record
Abstract
COVID-19 necessitated the rapid transition to online learning, challenging the ability of Science, Technology, Engineering, and Math (STEM) professors to offer laboratory experiences to their students. As a result, many instructors sought online alternatives. In addition, recent literature supports the capacity of online curricula to empower students of historically underrepresented identities in STEM fields. Here, we present PARE-Seq, a virtual bioinformatics activity highlighting approaches to antimicrobial resistance (AMR) research. Following curricular development and assessment tool validation, pre- and post-assessments of 101 undergraduates from 4 institutions revealed that students experienced both significant learning gains and increases in STEM identity, but with small effect sizes. Learning gains were marginally modified by gender, race/ethnicity, and number of extracurricular work hours per week. Students with more extracurricular work hours had significantly lower increase in STEM identity score after course completion. Female-identifying students saw greater learning gains than male-identifying, and though not statistically significant, students identifying as an underrepresented minority reported larger increases in STEM identity score. These findings demonstrate that even short course-based interventions have potential to yield learning gains and improve STEM identity. Online curricula like PARE-Seq can equip STEM instructors to utilize research-driven resources that improve outcomes for all students, but support must be prioritized for students working outside of school.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".