Learning and STEM identity gains from an online module on sequencing-based surveillance of antimicrobial resistance in the environment: An analysis of the PARE-Seq curriculum
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.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".