Starting Young in STEM: The Relationship Between Competition Entry Age and Student Engagement Patterns
Bibliographic record
Abstract
Early participation in science, technology, engineering, and mathematics (STEM) competitions is often promoted as a pathway to academic excellence and engagement, yet empirical evidence remains limited.This study investigates the relationship between the age of first STEM competition participation and subsequent academic and extracurricular outcomes among high school students.A sample of 116 students from Canada and the United States completed an online survey assessing competition history, STEM grade point average (GPA), and time spent on STEM activities.Participants were grouped as early starters (5-10 years old, n = 58), late starters (11-14+ years old, n = 48), or non-participants (n = 10).Independent t-tests revealed that early starters participated in more competitions (M = 5.10 vs. 3.65, p = .001,d = 0.70) and more frequently (M = 3.12 vs. 2.46, p = .002,d = 0.62) than late starters, alongside greater weekly STEM activity hours (M = 7.62 vs. 5.48, p = .012,d = 0.51).However, no significant GPA difference emerged (p = .108).Regression analysis (R = .13)identified advanced coursework and activity hours as predictors of GPA, not participation age.Findings suggest that early STEM competition exposure enhances engagement but not necessarily academic performance, offering insights for educators fostering STEM talent among youth.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".