Assessing the effects of a hybrid approach based on accelerated undergraduate research training and international experience in biomaterials
Bibliographic record
Abstract
In 2018, the University of Texas at El Paso (UTEP) secured the International Research Experience for Students (IRES) grant from the National Science Foundation (NSF). Collaborating with the University of Victoria (UVIC) in Canada, the initiative aimed to equip UG STEM students with international research exposure. The focus of the scientific research was centered on utilizing 3D bioprinting to co-print human stem cell derived products with biomedical scaffolds. The IRES: UTEP-UVIC program's other goals included creating opportunities for the US Southwest Border region residents, particularly marginalized groups, to excel and integrate as STEM professionals in a domestic and international research setting. Over the three-year grant period, the program trained up to 15 undergraduates, implemented graduate-undergraduate working groups, prepared students for post-baccalaureate education, strengthened research collaborations, and fostered a globally engaged STEM workforce. Each cohort participated in hands-on lab training, including cell culture and 3D bioprinting, followed by mentor-guided research execution in the US followed by Canada. This study reports on the project activities and its outcomes in the form of insights generated from the project as well as the impacts of the COVID-19 pandemic on the project in its various aspects. The program positively influenced academic and professional outcomes for all participants, with a total of seven research-based publications and six conference presentations resulting from collective research experiences. Overall, the program successfully contributed to the development and success of STEM students, particularly those from underrepresented groups. In summary, we learned that UG research and training experiences are crucial for holistic student development, preparing them for both advanced academic pursuits and diverse career paths. These experiences contribute to a deeper understanding of the subject matter, the development of essential skills, and the cultivation of a lifelong appreciation for research and learning.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".