19. Evidence in Support of Removing Boundaries to Undergraduate Research Experiences
Bibliographic record
Abstract
Undergraduate research is one of several high impact educational practices used by educational institutions to increase student engagement and success (Kuh, 2008). Many studies on the impact of undergraduate research have surveyed students or faculty on their personal experience and its influence on students’ subsequent degrees and employment (Brownell & Swaner, 2010). These studies have documented the ability of high impact educational practices to have the greatest influence on those students who self-identify as belonging to a minority or disadvantaged group. Few studies, however, have documented the impact on students with lower grades. The privilege of being admitted to an undergraduate research experience (e.g., an independent studies course) is typically reserved for upper-year students who have proven themselves academically. This paper presents correlational data from our campus showing that academically weaker students have a greater increase in academic performance between prior grade point average (GPA) and final grade in an independent studies course than academically stronger students. In addition, student annual GPA data shows that the impact of undergraduate research serves to raise student GPA in the year of an independent studies course. Although the findings are based on data from one small campus, they do raise the question that if undergraduate research has the greatest impact on academically weaker students and accelerates academic maturity, is limiting registration into these courses on the basis of superior GPA and years of study placing inappropriate boundaries on student learning?
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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 | 0.012 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".