Reshaping graduate outcomes of science students – The contribution of undergraduate research experiences
Bibliographic record
Abstract
Today’s science graduates require substantially different skills compared to yesterday’s graduates given the changing nature of modern science. As higher education institutions struggle to reform curricula and pedagogy, undergraduate research experiences (UREs) are increasingly being incorporated to enhance undergraduate science curricula. This study is situated within a traditional Bachelor of Science degree that offers students some voluntary opportunities to participate in UREs. This study explores two graduating science cohorts (n=272), comparing those who did and did not participate in UREs. A survey investigated student perceptions (importance, confidence and improvements) of five graduate outcomes in the context of science: writing skills, communication skills, quantitative skills (QS), teamwork skills and content knowledge. Cross-tabs and a linear discriminant analysis were used to investigate perception change between the two groups. The notable differences in perception scores in this study were consistently higher in QS, perhaps indicative of UREs emphasising the need for such skills in science or from students gaining increased confidence as a result of utilising QS within an authentic context. Our results reveal little difference in other student outcome areas, which raises questions around the role of UREs as a broad strategy for enhancing the achievement of graduate outcomes in science. This study is limited to a single institution and is focused on specific graduate outcomes, so only limited conclusions can be drawn. However, further research to determine the graduate outcomes gained from UREs would benefit the sector, particularly science disciplines, in the changing focus of government policy on student learning outcomes.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".