Students’ Perceptions of a Special Program for Developing Exceptional Talent in STEM
Bibliographic record
Abstract
This investigation was to explore perceptions of students who participated in the Cultivating Diverse Talents in STEM project in an R1 university through (a) university-based summer internship program, (b) subsequent school-year research internships, and (c) successive summer workshops or internships. Thirteen high school juniors from diverse backgrounds and low-income families were selected using a series of identification and assessment methods. Both the performance-based and paper-and-pencil assessments were measures of creative problem solving and application of conceptual understandings. A questionnaire was administered after students’ participation in the summer internship. The core theme, active involvement in problem solving inspired and motivated students with exceptional talent, was identified, including three categories: (a) academic initiative and engagement, (b) transition preparation, and (c) practical skill development. Strengths of diverse, underrepresented students with exceptional talent in STEM (spatial analytical skills, high academic resilience, and persistence) and critical elements of a quality STEM program (focusing on individual research interests and real-world problems, providing enriched and varied experiences, and creating supportive mentoring relationships) are included in the research implications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".