Items used for the social-psychological constructs evaluated by the survey instruments, the complete beginning of fall survey, and the complete end of fall and spring quarter surveys are presented in the supporting information.
Bibliographic record
Abstract
During the past few decades, there has been a nationwide push to improve performance and persistence outcomes for STEM undergraduates. As part of this effort, recent research has emphasized the need for focus on not only improving the delivery of course content, but also addressing the social-psychological needs of students. One promising intervention type that has been proposed as a multifaceted way to address both cognitive and social-psychological aspects of the learning process is the learning community. Learning communities provide students with opportunities to build a strong support system in college and are generally associated with increased student engagement and integration with campus systems and cultures. In this study, we examine the impact of a learning community intervention for first-year biological sciences majors, the Enhanced Academic Success Experience (EASE) program. Incoming freshmen are assigned to EASE based on their SAT (or ACT equivalent) Math score, a metric demonstrated to be a key predictor of student success in the program. We find that enrollment in EASE is correlated with higher STEM course grades; an increase of 0.25 (on a 0–4 point scale) in cumulative first-year GPA; and gains in non-academic outcomes, such as measures of sense of belonging and academic integration. Further, these outcomes are more pronounced for particular subgroup populations. For example, whereas surveyed male students seemed to benefit academically from participating in a learning community, female students reported a greater sense of belonging in regard to the biological sciences major and reported higher values for behavioral indicators of academic integration. Lastly, we find that the EASE program is positively correlated with students’ intention to stay in the biological sciences major. And, among the three race-oriented groups, this impact is most pronounced for under-represented students. In light of these findings, we discuss the potential of discipline-specific learning community programs to improve academic outcomes for students most at risk of leaving STEM majors, such as students underprepared for college level coursework.
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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 | 0.024 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.025 |
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".